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Добірка наукової літератури з теми "ML TECHNIQUES"

Автор: Grafiati

Опубліковано: 19 січня 2024

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Статті в журналах з теми "ML TECHNIQUES"

Jeevana, P., T. Nandini, D. Srilekha, G. Dinesh, and Mrs Archana. "Diabetic Prediction using ML Techniques." YMER Digital 21, no. 04 (April 30, 2022): 585–93. http://dx.doi.org/10.37896/ymer21.04/59.

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In today's world, diabetes is a huge problem. Diabetes can cause blood sugar levels to rise, which can contribute to strokes and heart attacks. One of the most rapidly spreading diseases is this one. After speaking with a doctor and receiving a diagnosis, patients are normally required to receive their reports. Because this procedure is time-consuming and costly, we were able to fix the problem utilizing machine learning techniques. In medical organizations, many machine learning applications are both exciting and important. Machine learning is being more widely used in the medical field. Our study aims to create a system that can better predict a patient's diabetic risk level. The medical data set is put to many different uses. in order to develop an artificial intelligence model for disease prediction The National Institute of Diabetes and Digestive and Kidney Diseases provided the data. Among the items on the list are blood pressure, age, insulin level, BMI, and glucose. Models are created using classification methods such as Ada Boost, Gradient Boost, XG Boost, and Cat Boost. The outcomes reveal that the processes are extremely precise. According to the findings, the prediction made with the use the prediction utilizing the Gradient Boosting model had the highest accuracy, according to the findings. Our investigation covers a wide range of machine learning topics as well as the numerous types of prediction models available. We go over the different sorts of models that can be used to create predictions, as well as the characteristics of machine learning.

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Venkata Vara Prasad, D., P. Senthil Kumar, Lokeswari Y. Venkataramana, G. Prasannamedha, S. Harshana, S. Jahnavi Srividya, K. Harrinei, and Sravya Indraganti. "Automating water quality analysis using ML and auto ML techniques." Environmental Research 202 (November 2021): 111720. http://dx.doi.org/10.1016/j.envres.2021.111720.

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Kosinska, Joanna, and Maciej Tobiasz. "Detection of Cluster Anomalies With ML Techniques." IEEE Access 10 (2022): 110742–53. http://dx.doi.org/10.1109/access.2022.3216080.

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Derangula, Sirisha. "Identification of phishing websites using ML techniques." International Journal of Communication and Information Technology 1, no. 2 (July 1, 2020): 28–32. http://dx.doi.org/10.33545/2707661x.2020.v1.i2a.16.

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Nanajkar, Jyotsna, Mayuresh Warang, Pratik Suthar, Shivam Shinde, and Atharv Pawar. "DDoS Attack Detection Using ML/DL Techniques." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (January 8, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem27967.

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The increasing integration of IoT devices has heightened the vulnerability of networks to sophisticated and evolving cyber threats, particularly DDoS attacks, which can severely disrupt service availability. Leveraging machine learning algorithms, this research aims to enhance the proactive identification of anomalous patterns indicative of DDoS attacks within IoT environments. By employing a combination of feature extraction, classification, and ensemble learning methods, the proposed model demonstrates promising results in distinguishing between normal network behaviour and malicious activities associated with DDoS attacks. The study contributes to the advancement of security measures in IoT networks, offering a proactive and adaptive solution to mitigate the impact of DDoS attacks, ultimately bolstering the resilience of interconnected systems in the evolving landscape of cyber threats. This study presents a novel approach for the detection of Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks using machine learning techniques.

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Kumar, Sachin, and Subhasree Bhattacharjee. "REVIEW OF AI/ML TECHNIQUES ON COVID-19." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (September 1, 2022): 116–18. http://dx.doi.org/10.33564/ijeast.2022.v07i05.020.

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COVID-19 has caused severe worldwide threat by taking away over 6 million lives. Artificial intelligence and machine learning methods are used significantly for fighting against this pandemic. For solving different problems of COVID-19, different AI/ML techniques are required. This article provides comprehensive review on different AI/ML applications on COVD-19. For classification, prediction, and diagnosis AI/ML methods have huge contribution. AI/ML methods for risk assessment of COVID-19 have also been narrated.

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Abuzaid, Nawal. "Image SPAM Detection Using ML and DL Techniques." International Journal of Advances in Soft Computing and its Applications 14, no. 1 (March 28, 2022): 227–43. http://dx.doi.org/10.15849/ijasca.220328.15.

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Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor (KNN), Support-Vector Machine (SVM), NaïveBays (NB), and Convolutional Neural Network (CNN)). Several experiments evaluate the efficiency and performance of the (ML) algorithms for spam detection. Using the Image Spam Hunter Dataset extracted from real spam e-mails, the proposed model achieved over 99% accuracy on spam image detection. Keywords: SPAM, Machine Learning, Image Classification, Feature Extraction, Deep Learning.

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T., Logeswari. "Performance Analysis of Ml Techniques for Spam Filtering." International Research Journal on Advanced Science Hub 2, Special Issue ICIES 9S (November 3, 2020): 64–69. http://dx.doi.org/10.47392/irjash.2020.161.

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Kore, Rahul C., Prachi Ray, Priyanka Lade, and Amit Nerurkar. "Legal Document Summarization Using Nlp and Ml Techniques." International Journal of Engineering and Computer Science 9, no. 05 (May 20, 2020): 25039–46. http://dx.doi.org/10.18535/ijecs/v9i05.4488.

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Reading legal documents are tedious and sometimes it requires domain knowledge related to that document. It is hard to read the full legal document without missing the key important sentences. With increasing number of legal documents it would be convenient to get the essential information from the document without having to go through the whole document. The purpose of this study is to understand a large legal document within a short duration of time. Summarization gives flexibility and convenience to the reader. Using vector representation of words, text ranking algorithms, similarity techniques, this study gives a way to produce the highest ranked sentences. Summarization produces the result in such a way that it covers the most vital information of the document in a concise manner. The paper proposes how the different natural language processing concepts can be used to produce the desired result and give readers the relief from going through the whole complex document. This study definitively presents the steps that are required to achieve the aim and elaborates all the algorithms used at each and every step in the process.

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Sethuraman, Sriram, V. S. Nithya, and D. Venkata Narayanababu Laveti. "Noniterative Content-Adaptive Distributed Encoding Through ML Techniques." SMPTE Motion Imaging Journal 127, no. 9 (October 2018): 50–55. http://dx.doi.org/10.5594/jmi.2018.2862647.

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Більше джерел

Дисертації з теми "ML TECHNIQUES"

Garg, Anushka. "Comparing Machine Learning Algorithms and Feature Selection Techniques to Predict Undesired Behavior in Business Processesand Study of Auto ML Frameworks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285559.

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In recent years, the scope of Machine Learning algorithms and its techniques are taking up a notch in every industry (for example, recommendation systems, user behavior analytics, financial applications and many more). In practice, they play an important role in utilizing the power of the vast data we currently generate on a daily basis in our digital world.In this study, we present a comprehensive comparison of different supervised Machine Learning algorithms and feature selection techniques to build a best predictive model as an output. Thus, this predictive model helps companies predict unwanted behavior in their business processes. In addition, we have researched for the automation of all the steps involved (from understanding data to implementing models) in the complete Machine Learning Pipeline, also known as AutoML, and provide a comprehensive survey of the various frameworks introduced in this domain. These frameworks were introduced to solve the problem of CASH (combined algorithm selection and Hyper- parameter optimization), which is basically automation of various pipelines involved in the process of building a Machine Learning predictive model.
Under de senaste åren har omfattningen av maskininlärnings algoritmer och tekniker tagit ett steg i alla branscher (till exempel rekommendationssystem, beteendeanalyser av användare, finansiella applikationer och många fler). I praktiken spelar de en viktig roll för att utnyttja kraften av den enorma mängd data vi för närvarande genererar dagligen i vår digitala värld.I den här studien presenterar vi en omfattande jämförelse av olika övervakade maskininlärnings algoritmer och funktionsvalstekniker för att bygga en bästa förutsägbar modell som en utgång. Således hjälper denna förutsägbara modell företag att förutsäga oönskat beteende i sina affärsprocesser. Dessutom har vi undersökt automatiseringen av alla inblandade steg (från att förstå data till implementeringsmodeller) i den fullständiga maskininlärning rörledningen, även känd som AutoML, och tillhandahåller en omfattande undersökning av de olika ramarna som introducerats i denna domän. Dessa ramar introducerades för att lösa problemet med CASH (kombinerat algoritmval och optimering av Hyper-parameter), vilket i grunden är automatisering av olika rörledningar som är inblandade i processen att bygga en förutsägbar modell för maskininlärning.

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Boualem, Abdel-Malek. "Ml-tasc : systeme de conversion de formalismes de langages techniques et scientifiques dans un environnement a syntaxe controlee et a contexte limite. traduction automatique multilingue." Nice, 1993. http://www.theses.fr/1993NICE4683.

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Entre les premieres approches de w. Weaver et a. D. Booth dans les annees quarante et les travaux pour la conception des systemes actuels, les techniques utilisees dans le domaine de la traduction automatique et la traduction assistee par ordinateur ont considerablement evolue. Si certains systemes de traduction semblent actuellement etre satisfaisants pour des langues a fort degre de ressemblance comme les langues europeennes, la representation, la saisie, l'edition et la traduction de langues appartenant a des familles differentes demeurent fondamentalement complexes. En outre, la plupart des modeles existants pour la formalisation de sous-ensembles du langage naturel, au niveau lexico-syntaxique et semantique, ne sont pas parametrables; les outils de formalisation et de traitement, souvent dedies aux langues indo-europeennes, sont difficilement adaptables aux langues semitiques ou sino-tibetaines. Par ailleurs, le contexte economique international actuel conduit a un besoin de plus en plus croissant en outils de traitement multilingue, d'entrees/sorties et de traduction de documentations techniques et scientifiques. Nous presentons dans cette these la description d'un environnement logiciel que nous avons intitule: ml-tasc (traduction automatique multilingue a syntaxe controlee et a contexte limite). Nous proposons un modele parametrable pour la construction et la mise a jour des formalismes de langages techniques et scientifiques et une methode generale (multilingue) pour la conversion/traduction des formalismes construits. Les lexiques, elabores sur un modele d'ensembles structures, sont associes a des attributs morphologiques, syntaxiques et semantiques et les regles de grammaire sont modelisees par des automates a etats finis non deterministes. Ml-tasc appartient a la deuxieme generation des systemes de traduction automatique. Il repose sur le modele de traduction a structure intermediaire qui nous parait etre le mieux adapte pour les traductions multilingues. En outre, l'independance des informations linguistiques (lexiques et grammaires) des programmes de traitement (compilateurs et programmes de conversion) du systeme ml-tasc permet son adaptation a des langues differentes. Enfin, des modules d'entrees/sorties pour la saisie et l'edition des textes multilingues notamment pour les langues semitiques et a base d'ideogrammes ont ete developpes

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PIROZZI, MICHELA. "Development of a simulation tool for measurements and analysis of simulated and real data to identify ADLs and behavioral trends through statistics techniques and ML algorithms." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/272311.

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Con una popolazione di anziani in crescita, il numero di soggetti a rischio di patologia è in rapido aumento. Molti gruppi di ricerca stanno studiando soluzioni pervasive per monitorare continuamente e discretamente i soggetti fragili nelle loro case, riducendo i costi sanitari e supportando la diagnosi medica. Comportamenti anomali durante l'esecuzione di attività di vita quotidiana (ADL) o variazioni sulle tendenze comportamentali sono di grande importanza.
With a growing population of elderly people, the number of subjects at risk of pathology is rapidly increasing. Many research groups are studying pervasive solutions to continuously and unobtrusively monitor fragile subjects in their homes, reducing health-care costs and supporting the medical diagnosis. Anomalous behaviors while performing activities of daily living (ADLs) or variations on behavioral trends are of great importance. To measure ADLs a significant number of parameters need to be considering affecting the measurement such as sensors and environment characteristics or sensors disposition. To face the impossibility to study in the real context the best configuration of sensors able to minimize costs and maximize accuracy, simulation tools are being developed as powerful means. This thesis presents several contributions on this topic. In the following research work, a study of a measurement chain aimed to measure ADLs and represented by PIRs sensors and ML algorithm is conducted and a simulation tool in form of Web Application has been developed to generate datasets and to simulate how the measurement chain reacts varying the configuration of the sensors. Starting from eWare project results, the simulation tool has been thought to provide support for technicians, developers and installers being able to speed up analysis and monitoring times, to allow rapid identification of changes in behavioral trends, to guarantee system performance monitoring and to study the best configuration of the sensors network for a given environment. The UNIVPM Home Care Web App offers the chance to create ad hoc datasets related to ADLs and to conduct analysis thanks to statistical algorithms applied on data. To measure ADLs, machine learning algorithms have been implemented in the tool. Five different tasks have been identified. To test the validity of the developed instrument six case studies divided into two categories have been considered. To the first category belong those studies related to: 1) discover the best configuration of the sensors keeping environmental characteristics and user behavior as constants; 2) define the most performant ML algorithms. The second category aims to proof the stability of the algorithm implemented and its collapse condition by varying user habits. Noise perturbation on data has been applied to all case studies. Results show the validity of the generated datasets. By maximizing the sensors network is it possible to minimize the ML error to 0.8%. Due to cost is a key factor in this scenario, the fourth case studied considered has shown that minimizing the configuration of the sensors it is possible to reduce drastically the cost with a more than reasonable value for the ML error around 11.8%. Results in ADLs measurement can be considered more than satisfactory.

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Abd, Alrahman Chadi. "Spectroscopie Laser avec des cavités résonantes de haute finesse couplées à un peigne de fréquences : ML-CEAS et vernier effet techniques. Applications à la mesure in situ de molécules réactives dans les domaines UV et visible." Thesis, Grenoble, 2012. http://www.theses.fr/2012GRENY095/document.

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La communauté de la chimie atmosphérique souffre d'un manque de mesures rapides, fiables résolues spatialement et temporellement pour un large éventail de molécules réactives (radicaux tels que NO2, OH, BrO, IO, etc). En raison de leur forte réactivité, ces molécules contrôlent largement la durée de vie et la concentration de nombreuses espèces clés dans l'atmosphère, et peuvent avoir un impact important sur le climat. Les concentrations de ces radicaux sont extrêmement faibles (ppbv ou moins) et très variable dans le temps et dans l'espace, ce qui impose un véritable défi lors de la détection. Dans la première partie de cette thèse, un spectromètre UV robuste, compacte et transportable est développé, exploitant la technique ML-CEAS pour mesurer à des niveaux très faibles (pptv et même en dessous) des molécules réactives d'importance atmosphérique, en particulier, les radicaux d'oxyde d'halogènes, afin de répondre aux besoins émergents. La technique ML-CEAS est basée sur le couplage d'un laser femtoseconde à blocage de modes à une cavité optique de haute finesse, qui agit comme un piège à photons pour augmenter l'interaction entre la lumière et l'échantillon de gaz intracavité. Cela permet d'améliorer fortement la sensibilité d'absorption. La limite de détection obtenue pour le radical IO est de 20 ppqv pour un temps d'acquisition de 5 minutes, ce qui est un résultat impressionnant. Dans la deuxième partie de cette thèse, une nouvelle technique spectroscopique est développée appelée effet Vernier, qui est également basé sur l'interaction entre un laser femtoseconde à blocage de mode et une cavité optique de haute finesse. Cette technique fournit une sensibilité de détection similaire à la technique ML-CEAS, mais l'avantage est que le nombre des éléments spectraux est donné par la finesse de la cavité optique et donc peut atteindre plusieurs dizaines de milliers. De plus, cette configuration simplifie le montage expérimental par la suppression du spectrographe qui est remplacé par une simple photodiode. Le temps d'acquisition d'un spectre peut être aussi réduit à moins d' 1 ms
The atmospheric chemistry community suffers a lack of fast, reliable and space resolved measurements for a wide set of reactive molecules (e.g. radicals such as OH, NO3, BrO, IO, etc). Due to their high reactivity, these molecules largely control the lifetime and concentration of numerous key atmospheric species, and may have an important impact on the climate. The concentrations of such radicals are extremely low (ppbv or less) and highly variable in time and space, which imposes a real challenge during the detection. In the first part of this thesis, a compact, robust and transportable UV spectrometer is developed, exploiting the Mode-Locked Cavity Enhanced Absorption Spectroscopy (ML-CEAS) technique to measure pptv and sub-pptv levels of atmospherically important reactive molecules, in particular, halogen oxide radicals, to respond to the emerging needs. The ML-CEAS technique is based on coupling a Mode-Locked femtosecond laser to a high finesse optical cavity, which acts as a photon trap to increase the interaction between the light and the intracavity gas sample, which highly enhances the absorption sensitivity. The detection limit obtained for the IO radical is 20 ppqv (part per quadrillion), which is an impressive result. In the second part of this thesis, a new spectroscopic technique is developed, called Vernier effect, which is also based on the interaction between a mode-locked femtosecond laser with a high finesse optical cavity. This technique provides detection sensitivity similar to that of ML-CEAS technique, but the advantage is that the number of the spectral elements is given by the cavity finesse, so it can reach ten thousands, as well as this technique has a simple setup, where the spectrograph is replaced by a photodiode. Additionally, the time required to measure one output absorption spectrum can be less than 1 ms

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Bossé, Mathieu. "La réalisation audionumérique DIY de groupes rock au Québec : pour une méthode de travail plus efficace." Master's thesis, Université Laval, 2019. http://hdl.handle.net/20.500.11794/36713.

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Dans ce mémoire, nous nous penchons sur les processus créatifs et techniques d’enregistrement sonore de type DIY (Do It Yourself) pratiqués dans mes expériences avec différents groupes rock, punk, hardcore et alternatif de Québec. Les styles musicaux varient grandement d’un projet à l’autre, mais trois aspects sont communs à chaque groupe : 1) Ils sont issus d’une scène «underground» de la Ville de Québec. 2) Ils ont une instrumentation rock (pour nous, une instrumentation rock est composée des instruments suivants : batterie, guitare électrique, guitare basse et voix). 3) Ils enregistrent de manière DIY jusqu’à un certain niveau. Avec les avancées technologiques, l’enregistrement DIY est une pratique de plus en plus courante chez les groupes de musique. À notre expérience, les artistes œuvrant dans des scènes se rapprochant de la culture punk et underground sont souvent restreints par des ressources et des budgets limités ou ont simplement un désir de liberté créative lorsqu’il est temps d’enregistrer leurs chansons. Dans la recherche de l’indépendance, ils vont souvent opter pour l’approche DIY lorsqu’il est temps d’enregistrer une chanson ou un album, ce qui donne souvent une esthétique punk aux pistes audio. Dans ce partage d’expériences, l’objectif est d’analyser les méthodes de travail qui contribuent à obtenir des résultats de qualité en enregistrement DIY et d’en tirer une méthode efficace qui pourrait servir au DIYer en enregistrement. Nous croyons qu’en prenant connaissance de nos résultats et de nos analyses de chaque projet, un(e) artiste désirant enregistrer un projet musical de façon DIY sera en mesure de faire des choix plus sensés et éclairés par rapport à sa propre situation et son propre projet d’enregistrement. Enfin, j’offre mes observations et conclusions au réalisateur DIY pour qu’il analyse bien sa réalité unique à lui-même et à son ou ses projets d’enregistrement afin qu’il bénéficie d’une courbe d’apprentissage rapide et conviviale.
In this master’s thesis, we address the issue of creative processes and recording techniques in different DIY (Do It Yourself) settings as implemented through my experience with different rock, punk, hardcore and alternative bands in Quebec City. Musical styles greatly vary from one project to another, but all of the music groups share three common aspects : 1) They come from the underground scene around Quebec City. 2) They have a rock-type instrumentation (for us, a rock-type instrumentation is made up of minimum two of the following instruments: drums, electric guitar, electric bass and vocal.) 3) They record in a DIY setting, up to a certain point. With the rise of new technologies, DIY recording is more and more common in music bands. In our experience, artists from scenes close to the punk and underground cultures are often limited by their budget and resources or simply have a desire for creative liberty when recording their music. Seeking independence, they will often go for the DIY approach when the time has come for them to record a song or an album, which often lends a punk aesthetic to their audio tracks. This masters thesis seeks to analyse working methods that contribute to obtaining good quality results in DIY recording and to find an efficient methodology that could help the self-taught DIYer in recording music. We believe that by reading about these projects, with the analysis and feedback, an artist who wants to work in a DIY setting will be able to make better decisions in regard to their own recording project and situation. I also offer advice for the DIY recording artist to analyse his own unique recording reality and the one of his different recording projects in order for him to benefit from a faster and favorable learning curve.

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MASTRO, PIETRO. "Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses." Doctoral thesis, Università degli studi della Basilicata, 2023. https://hdl.handle.net/11563/162986.

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With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work.

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Abd, alrahman Chadi. "Spectroscopie Laser avec des cavités résonantes de haute finesse couplées à un peigne de fréquences : ML-CEAS et vernier effet techniques. Applications à la mesure in situ de molécules réactives dans les domaines UV et visible." Phd thesis, Université de Grenoble, 2012. http://tel.archives-ouvertes.fr/tel-00849145.

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Gherman, Titus Constantin. "Ml-ceas : une nouvelle technique spectroscopique d'absorption de haute sensibilité à l'aide de laser à impulsions ultracourtes." Université Joseph Fourier (Grenoble), 2004. http://www.theses.fr/2004GRE10137.

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Le résultat principal de ce travail de thèse est le développement d'une nouvelle technique de sensibilité élevée appelée: Mode-Locked Cavity-Enhanced Absorption Spectroscopy (ML-CEAS), qui exploite de manière constructive les propriétés spectrales des lasers à blocage de modes et des cavités optiques. Nous démontrons que ML-CEAS peut être facilement étendue à la région bleu doublant en fréquence une source laser Ti-Sa. La sensibilité élevée de ML-CEAS est démontre par l'observation d'une transition harmonique très élevée de C2H2 à 420 nm (23813 cm-1), avec la limite de détection de 5 x 10-9 cm-1 Hz-1/2. Cette nouvelle technique a été aussi applique au diagnostic des plasmas d'argon et d'azote autour de 400 nm. Un autre résultat est l'installation, et application en ML-CEAS, d'un laser semi-conducteur: diode-pumped, vertical external-cavity, surface-emitting laser (DP-VECSEL) à blocage de modes par un Semiconductor Saturable Absorber Mirror (SESAM). Notre système functionne autour de 1,04 æm. Le train d'impulsions a un taux de répétition de 1,12 GHz et la largeur d'impulsion d'environ 10 ps avec quelques mW de puissance émise en régime à blocage de modes. Une première démonstration de ML-CEAS employant un DP-VECSEL à blocage de modes est également rapportée
The principal result of this thesis work is the development of a new high sensitivity technique named: Mode-Locked Cavity-Enhanced Absorption Spectroscopy (ML-CEAS), which exploits constructively the spectral properties of mode-locked lasers and optical cavities. We demonstrate that ML-CEAS can be easily extended to the blue region by frequency-doubling a Ti-Sa laser source. The high sensitivity of ML-CEAS is put in evidence by recording a very high overtone transition of C2H2 at 420 nm (23813 cm-1), with detection limit of 5 x 10-9 cm-1 Hz-1/2. We also include the first practical application of this new technique to the diagnostics of argon and nitrogen plasmas around 400 nm. Another result is the setup, and application in ML-CEAS, of a semiconductor laser: diode-pumped, vertical external-cavity, surface-emitting laser (DP-VECSEL) and mode-locked by a Semiconductor Saturable Absorber Mirror (SESAM). Our system operates around 1. 04 æm. The train of pulses had a repetition rate of 1. 12 GHz and the pulse width was about 10 ps with a few mW of emitted power in mode-locking regime operation. A first demonstration of ML-CEAS using a mode-locked DP-VECSEL is also reported

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Gutierrez, Julian. "L'apport du pianiste cubain Emiliano Salvador au jazz latin : analyse technique et esthétique de son jeu." Doctoral thesis, Université Laval, 2018. http://hdl.handle.net/20.500.11794/33125.

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Ce projet de thèse traite principalement de l’apport du pianiste cubain Emiliano Salvador au jazz latin dans le contexte particulier du régime cubain. Sa musique, son langage et ses influences sont peu connus à cause des politiques de censure du système socialiste, qui rejetait toute forme d’expression américaine pour des raisons idéologiques. Un travail de terrain comprenant de nombreuses entrevues, visites de bibliothèques, de stations de télévision et de postes de radio cubaine a permis de lever le voile sur la démarche artistique et, conséquemment, sur le langage musical original d’Emiliano Salvador, et ce, au profit de mon propre processus créatif. En effet, par l’intermédiaire d’analyses de plusieurs de ses pièces, j’ai pu progresser dans mon style personnel en adoptant certaines caractéristiques de son langage, ce qui est démontré dans les analyses comparatives du dernier chapitre.

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Gupta, Anjali, Maxwell Krohn, and Michael Walfish. "Can Basic ML Techniques Illuminate Rateless Erasure Codes?" 2004. http://hdl.handle.net/1721.1/30467.

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The recently developed rateless erasure codes are a near-optimal channel coding technique that guaranteeslow overhead and fast decoding. The underlying theory, and current implementations, of thesecodes assume that a network transmitter encodes according to a pre-specified probability distribution.In this report, we use basic Machine Learning techniques to try to understand what happens when thisassumption is false. We train several classes of models using certain features that describe the empiricaldistribution realized at a network receiver, and we investigate whether these models can ÂlearnÂ topredict whether a given encoding will require extra overhead. Our results are mixed.

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Книги з теми "ML TECHNIQUES"

Appel, Andrew W. Modern compiler implementation in ML: Basic techniques. Cambridge: Cambridge University Press, 1997.

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Štuikys, Vytautas. Meta-Programming and Model-Driven Meta-Program Development: Principles, Processes and Techniques. London: Springer London, 2013.

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Appel, Andrew W. Modern compiler implementation in C: Basic techniques. Cambridge: Cambridge University Press, 1997.

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Metaprogramming And Modeldriven Metaprogram Development Principles Processes And Techniques. Springer, 2012.

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Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

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Bhattacharya, Aditya. Applied Machine Learning Explainability Techniques: Make ML Models Explainable and Trustworthy for Practical Applications Using LIME, SHAP, and More. Packt Publishing, Limited, 2022.

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Li Bassi, Gianluigi, and J. D. Marti. Chest physiotherapy and tracheobronchial suction in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0121.

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The airway lining fluid is a biphasic layer covering the respiratory tract epithelium. It has antimicrobial and immunomodulatory properties, and it is formed by a gel-phase (mucus), and a low-viscosity inner layer (sol-phase) that provides lubrication for ciliary beating. Mucus is continuously cleared from the airways through the ciliated epithelium and via the two-phase gas–liquid flow mechanism (i.e. coughing). Mucus production in healthy subjects is approximately 10–100 mL/day. Whereas, mucociliary clearance rates range between 4 and 20 mm/min. Critically-ill, mechanically-ventilated patients often retain mucus. Several chest physiotherapy techniques are applied to promote mucus clearance in these patients. The role of chest physiotherapy in mechanically-ventilated patients is debated, due to the lack of evidence from well-designed clinical trials. Retained mucus is aspirated through tracheobronchial suctioning. Closed suctioning is beneficial in patients with severe lung failure and at risk of alveolar collapse upon ventilator disconnection.

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Orenbuch-Harroch, Efrat, and Charles L. Sprung. Pulmonary artery catheterization in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0133.

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Haemodynamic monitoring is a significant component in the management of critically-ill patients. Flow-directed pulmonary artery catheters (PAC) are a simple and rapid technique for measuring several continuous or intermittent circulatory variables. The PAC is helpful in diagnosis, guidance of therapy, and monitoring therapeutic interventions in various clinical conditions, including myocardial infarction and its complications, non-cardiogenic pulmonary oedema and severely ill patients.The catheter is inserted through a large vein. The PAC is advanced, after ballooninflation with 1.5 mL of air, through the right ventricle across the pulmonary valve and into the pulmonary artery (PA). Finally, the catheter is advanced to the ‘wedge’ position. The pulmonary artery wedge pressure (PAWP) is identified by a decrease in pressure combined with a characteristic change in the waveform. The balloon should then be deflated and the PA tracing should reappear. Direct measurements include central venous pressure, pulmonary artery pressure, and PAWP, which during diastole represents the left ventricular end-diastolic pressure and reflects left ventricular preload. Cardiac output can be measured by thermodilution technique. Other haemodynamic variables can be derived from these measurements. Absolute contraindications are rare. Relative contraindications include coagulopathy and conditions that increase the risk of arrhythmias.

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Sever, Mehmet Şükrü, and Raymond Vanholder. Acute kidney injury in polytrauma and rhabdomyolysis. Edited by Norbert Lameire. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199592548.003.0252_update_001.

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The term ‘polytrauma’ refers to blunt (or crush) trauma that involves multiple body regions or cavities, and compromises physiology to potentially cause dysfunction of uninjured organs. Polytrauma frequently affects muscles resulting in rhabdomyolysis. In daily life, it mostly occurs after motor vehicle accidents, influencing a limited number of patients; after mass disasters, however, thousands of polytrauma victims may present at once with only surgical features or with additional medical complications (crush syndrome). Among the medical complications, acute kidney injury (AKI) deserves special mention, since it is frequent and has a substantial impact on the ultimate outcome.Several factors play a role in the pathogenesis of polytrauma (or crush)-induced AKI: (1) hypoperfusion of the kidneys, (2) myoglobin-induced direct nephrotoxicity, and intratubular obstruction, and also (3) several other mechanisms (i.e. iron and free radical-induced damage, disseminated intravascular coagulation, and ischaemia reperfusion injury). Crush-related AKI is prerenal at the beginning; however, acute tubular necrosis may develop eventually. In patients with crush syndrome, apart from findings of trauma, clinical features may include (but are not limited to) hypotension, oliguria, brownish discoloration of urine, and other symptoms and findings, such as sepsis, acute respiratory distress syndrome, disseminated intravascular coagulation, bleeding, cardiac failure, arrhythmias, electrolyte disturbances, and also psychological trauma.In the biochemical evaluation, life-threatening hyperkalaemia, retention of uraemic toxins, high anion gap metabolic acidosis, elevated serum levels of myoglobin, and muscle enzymes are noted; creatine phosphokinase is very useful for diagnosing rhabdomyolysis.Early fluid administration is vital to prevent crush-related AKI; the rate of initial fluid volume should be 1000 mL/hour. Overall, 3–6 L are administered within a 6-hour period considering environmental, demographic and clinical features, and urinary response to fluids. In disaster circumstances, the preferred fluid formulation is isotonic saline because of its ready availability. Alkaline (bicarbonate-added) hypotonic saline may be more useful, especially in isolated cases not related to disaster, as it may prevent intratubular myoglobin, and uric acid plugs, metabolic acidosis, and also life-threatening hyperkalaemia.In the case of established acute tubular necrosis, dialysis support is life-saving. Although all types of dialysis techniques may be used, intermittent haemodialysis is the preferred modality because of medical and logistic advantages. Close follow-up and appropriate treatment improve mortality rates, which may be as low as 15–20% even in disaster circumstances. Polytrauma victims after mass disasters deserve special mention, because crush syndrome is the second most frequent cause of death after trauma. Chaos, overwhelming number of patients, and logistical drawbacks often result in delayed, and sometimes incorrect treatment. Medical and logistical disaster preparedness is useful to improve the ultimate outcome of disaster victims.

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Soar, Jasmeet, and Jerry P. Nolan. Artificial ventilation in cardiopulmonary resuscitation. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0060.

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When cardiac arrest occurs, cardiopulmonary resuscitation (CPR) should be started with chest compressions first. The use of ventilations is determined by the training of rescuers, their ability and willingness to provide rescue breaths, patient characteristics, and the underlying cause of the cardiac arrest. Trained rescuers should give two ventilations after every 30 compressions, or once the airway is secured with a tracheal tube, ventilate the patient at 10 breaths/min without any pause in chest compressions. Rescuers who are unable or unwilling to provide effective ventilation, while awaiting expert help should use compression-only CPR. Ventilations are needed for the treatment of cardiac arrest in children, when arrest is from a primary respiratory cause, or during a prolonged cardiac arrest. Choice of ventilation technique depends on rescuer skills and the airway used. Effective oxygenation and ventilation can be maintained during CPR with a tidal volume of approximately 500 mL given over an inspiratory time of 1 second. Rescuers should give supplemental oxygen in as high a concentration as possible during CPR in order to rapidly correct tissue hypoxia. Once restoration of a spontaneous circulation has been achieved the inspired oxygen should be adjusted to maintain oxygen saturation between 94 and 98%.

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Частини книг з теми "ML TECHNIQUES"

Adriaans, Pieter. "ML techniques and text analysis." In Machine Learning: ECML-93, 460–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56602-3_164.

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Vashisht, Geetika, Ashish Kumar Jha, and Manisha Jailia. "Predicting Diabetes Using ML Classification Techniques." In Advances in Intelligent Systems and Computing, 845–54. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5148-2_73.

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McAllester, David. "A Logical Algorithm for ML Type Inference." In Rewriting Techniques and Applications, 436–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44881-0_31.

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Matthews, Brian. "MERILL: An equational reasoning system in standard ML." In Rewriting Techniques and Applications, 441–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56868-9_34.

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Shah, Manan, Ameya Kshirsagar, and Jainam Panchal. "Midstream sector with ML models and techniques." In Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry, 47–61. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003279532-4.

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Mohanty, Ashima Sindhu, Priyadarsan Parida, and Krishna Chandra Patra. "Usage of ML Techniques for ASD Detection." In Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications, 91–112. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003226147-5.

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Poudel, Khem, Movin Uddin, Revanth Kommu, Sadiat Muhammed, Nahid Hasan, and Sammi Hamdan. "HealthCare Text Analytics Using Recent ML Techniques." In Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23), 134–42. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33743-7_11.

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Deepa Shree, C. V., Jaaswin D. Kotian, Nidhi Gupta, Nikhil M. Adyapak, and U. Ananthanagu. "Predicting Code Runtime Complexity Using ML Techniques." In Lecture Notes in Electrical Engineering, 377–99. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7622-5_26.

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Khan, Hameed, Kamal Kumar Kushwah, Pradeep Kumar Jhinge, Gireesh Gaurav Soni, Ravi Kant Choubey, and Rupesh Kushwah. "Faults Diagnosis Using AI and ML." In Artificial Intelligence Techniques in Power Systems Operations and Analysis, 1–16. Boca Raton: Auerbach Publications, 2023. http://dx.doi.org/10.1201/9781003301820-1.

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Тези доповідей конференцій з теми "ML TECHNIQUES"

Trubat, Pau, Adrián Herrera, and Climent Molins. "Mooring Optimization Using ML Techniques." In ASME 2022 4th International Offshore Wind Technical Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/iowtc2022-98217.

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Abstract The mooring optimization process for a FOWT is a complex study that involves a large number of simulations. All possible designs of the optimization process should be assessed and the operational and survival constrains verified for acceptance or disregard the solution. These constraints are mainly the design bases of the mooring lines, but also the FOWT operational and survival conditions, which are mooring system dependant as it is the governing component for surge, sway and yaw stiffness. Machine Learning (ML) algorithms are used to predict the simulations results by training the model from a set of defined simulations, allowing for a significant reduction of the computational cost of the large number of required simulations. The methodology’s main advantage is the high velocity once the model is trained. However, some uncertainties can arise from the exactness of the predicted values. The aim of the paper is to design an optimized mooring system for the WindCrete platform for a Gran Canary Island with a 200 m sea depth for ULS. To overcome the difficulties of the optimization process two ML models are developed. The first one, the static ML model, is based on the static response of the mooring system that allows to reduce the solution space of the optimization problem. The second one, the dynamic ML model, is based on the dynamic response of the FOWT and allows to assess the constraints applied to the objective function as penalties. The objective function is defined as the total mass of the mooring system. The variables of the optimization problem are the main line and delta line lengths, the radius to anchor and the chain diameters. The first ML model is set-up to predict the static mean line tension at rated wind speed, the vertical force on anchor at rated wind speed and the initial Yaw period of the FOWT. These parameters allow to discard a large number of possible solutions that do not fit with the following design bases: the maximum line tension and no vertical force on anchor. Moreover, a maximum yaw natural period threshold is set-up based on design experience. This is needed due to the lack of damping in yaw direction of Spar platforms. The static ML model is used to create an initial sample of feasible solutions to train the dynamic ML model. Also, during the optimization process is used as a classification model to exclude non-feasible solutions to ensure the performance of the second ML model. The second ML model is based on the dynamic response of the WindCrete platform and the mooring system using OpenFast simulation tool. The model is set-up to predict the maximum line tension, vertical force on anchor, maximum surge position and maximum pitch which are used as constraints parameters to be applied at the optimization function as penalties. The results show a good approximation of both ML models with a high potential to be applied in determining design load cases, including fatigue assessment in the optimization design process.

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Surendiran, B., Tejus Paturu, Harsha Vardhan Chirumamilla, and Maruprolu Naga Raju Reddy. "Resume Classification Using ML Techniques." In 2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2023. http://dx.doi.org/10.1109/iconscept57958.2023.10169907.

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Glassner, Andrew. "ML/DL roundup." In SIGGRAPH '20: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3388769.3407509.

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Subudhi, Manorama, Kanhu Charan Bhuyan, and Ananya Dastidar. "IoT Assisted Farming using ML techniques." In 2022 IEEE 2nd International Symposium on Sustainable Energy, Signal Processing and Cyber Security (iSSSC). IEEE, 2022. http://dx.doi.org/10.1109/isssc56467.2022.10051428.

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Sk, Tajuddin, Leela Madhuri G, Lalitha Ram K, and Ranga Rao J. "Health Status Prediction using ML Techniques." In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2022. http://dx.doi.org/10.1109/iccmc53470.2022.9753766.

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Pasha, Syed Nawaz, Dadi Ramesh, Sallauddin Mohmmad, Navya P., P. Anil Kishan, and C. H. Sandeep. "Liver disease prediction using ML techniques." In INTERNATIONAL CONFERENCE ON RESEARCH IN SCIENCES, ENGINEERING & TECHNOLOGY. AIP Publishing, 2022. http://dx.doi.org/10.1063/5.0081787.

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Tummala, Sohith, Prithvi Sriman Maddukuri, Niranjana Nitish Varma Bhupathiraju, Mallikarjuna Rao Cherukupalli, and Srujan Singam. "Malicious software detection using ML algorithms." In LOW RADIOACTIVITY TECHNIQUES 2022 (LRT 2022): Proceedings of the 8th International Workshop on Low Radioactivity Techniques. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0161055.

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Rastogi, Rohit, Vaibhav Gupta, Vaibhav Sharma, and Tushar Gupta. "Threat Detection and Classification Using ML Techniques." In 2023 International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2023. http://dx.doi.org/10.1109/cictn57981.2023.10140771.

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De, Soumi, Prabu P, and Joy Paulose. "Effective ML Techniques to Predict Customer Churn." In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2021. http://dx.doi.org/10.1109/icirca51532.2021.9544785.

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Rani, Shilpa, Sandeep Kumar, Venkata Subbamma T, Arpit Jain, A. Swathi, and Ramakrishna Kumar M. V. N. M. "Commodities Price Prediction using Various ML Techniques." In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2022. http://dx.doi.org/10.1109/ictacs56270.2022.9987967.

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Звіти організацій з теми "ML TECHNIQUES"

Faybishenko, Boris, Lavanya Ramakrishnan, Tom Powell, Bhavna Arora, John Wu, and Deb Agarwall. On AI Prediction of Hydrological Processes Based on Integration of Retrospective and Forecasting ML Techniques. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769756.

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Ehiabhi, Jolly, and Haifeng Wang. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0003.

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Review question / Objective: A systematic review of Mental health diagnosis/prognoses of mental disorders using Machine Learning techniques with information from biometric signals. A review of the trend and status of these ML techniques in mental health diagnosis and an investigation of how these signals are used to help increase the efficiency of mental health disease diagnosis. Using Machine learning techniques to classify mental health diseases as against using only expert knowledge for diagnosis. Feature Extraction from signal gotten from biometric signals that help classify sleep disorders. Rationale: To review the application of ML techniques on multimodal and multichannel PSG datasets got from biosensors typically used in the Hospital. To help professionals grasp the steps of using machine learning to classify mental health diseases.

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Cook, Samantha, Matthew Bigl, Sandra LeGrand, Nicholas Webb, Gayle Tyree, and Ronald Treminio. Landform identification in the Chihuahuan Desert for dust source characterization applications : developing a landform reference data set. Engineer Research and Development Center (U.S.), October 2022. http://dx.doi.org/10.21079/11681/45644.

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ERDC-Geo is a surface erodibility parameterization developed to improve dust predictions in weather forecasting models. Geomorphic landform maps used in ERDC-Geo link surface dust emission potential to landform type. Using a previously generated southwest United States landform map as training data, a classification model based on machine learning (ML) was established to generate ERDC-Geo input data. To evaluate the ability of the ML model to accurately classify landforms, an independent reference landform data set was created for areas in the Chihuahuan Desert. The reference landform data set was generated using two separate map-ping methodologies: one based on in situ observations, and another based on the interpretation of satellite imagery. Existing geospatial data layers and recommendations from local rangeland experts guided site selections for both in situ and remote landform identification. A total of 18 landform types were mapped across 128 sites in New Mexico, Texas, and Mexico using the in situ (31 sites) and remote (97 sites) techniques. The final data set is critical for evaluating the ML-classification model and, ultimately, for improving dust forecasting models.

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Clausen, Jay, Vuong Truong, Sophia Bragdon, Susan Frankenstein, Anna Wagner, Rosa Affleck, and Christopher Williams. Buried-object-detection improvements incorporating environmental phenomenology into signature physics. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45625.

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The ability to detect buried objects is critical for the Army. Therefore, this report summarizes the fourth year of an ongoing study to assess environ-mental phenomenological conditions affecting probability of detection and false alarm rates for buried-object detection using thermal infrared sensors. This study used several different approaches to identify the predominant environmental variables affecting object detection: (1) multilevel statistical modeling, (2) direct image analysis, (3) physics-based thermal modeling, and (4) application of machine learning (ML) techniques. In addition, this study developed an approach using a Canny edge methodology to identify regions of interest potentially harboring a target object. Finally, an ML method was developed to improve automatic target detection and recognition performance by accounting for environmental phenomenological conditions, improving performance by 50% over standard automatic target detection and recognition software.

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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/u0qw7udtxd5iz469.

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This seminar explores machine learning techniques for prediction and causal inference, where a researcher or decision maker needs to make a prediction or understand the impact of an intervention in a heterogenous population. For example, researchers may want to infer the effect of an economic, educational, or public health intervention, or a firm may seek to understand how a change in pricing will impact aggregate demand. In these cases, the interest may be in an average effect, but also how the effect varies over different segments of the population (i.e., heterogeneity in the effect). This seminar will provide you with the tools to undertake such inquiry using machine learning (ML), while ensuring that you understand and can communicate how the methods work for prediction and causal inference. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent points.

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Weeks, Melvyn. Machine Learning for Prediction and Causal Inference. Instats Inc., 2022. http://dx.doi.org/10.61700/r1qb0f2baf6jj469.

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Robles, Marcelo, Claudio Dachevsky, and Héctor Llovera. Prospective study: evaluation of the efficacy and longevity of cross-linked hyaluronic acid in nasolabial folds filling. Editorial Lugones, December 2023. http://dx.doi.org/10.47196/0574.

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Introduction: the choice of minimally invasive methods positions hyaluronic acid fillers as a leading facial rejuvenation technique. Injectable hyaluronic acid fillers have been widely used in the clinical treatment of facial wrinkles. However, additional information and clinical evidence on the longevity of hyaluronic acid filler after injection is limited. Objectives: to demonstrate the efficacy, safety and longevity of cross-linked HA (AHR) 30 mg/ml (Estrianon Hyaluronic Implant 30® Allanmar International Company S.R.L., Argentina) for filling nasolabial folds (NLF). Design: multicenter, prospective and controlled clinical trial of 12 months duration. Materials and methods: 160 patients were included (132 women and 28 men) divided into 100, 30 and 30 of each cohort) and an injectable gel of cross-linked HA 30 mg/ml was applied in the correction of NLF to evaluate the safety, efficacy and longevity of the product. Results: a correction of the depression in the relevant sulci was observed, which was maintained until the end of the study. The mean level of improvement was clinically significant in 94% of cases. The severity of NLF was reduced by approximately 2 points to the range of mild or barely visible. Conclusions: Estrianon Hyaluronic Implant 30® proved to be a safe, effective filler with considerable longevity. At 12 months, significant correction was still observed and the treatment was well tolerated by patients.

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Irudayaraj, Joseph, Ze'ev Schmilovitch, Amos Mizrach, Giora Kritzman, and Chitrita DebRoy. Rapid detection of food borne pathogens and non-pathogens in fresh produce using FT-IRS and raman spectroscopy. United States Department of Agriculture, October 2004. http://dx.doi.org/10.32747/2004.7587221.bard.

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Rapid detection of pathogens and hazardous elements in fresh fruits and vegetables after harvest requires the use of advanced sensor technology at each step in the farm-to-consumer or farm-to-processing sequence. Fourier-transform infrared (FTIR) spectroscopy and the complementary Raman spectroscopy, an advanced optical technique based on light scattering will be investigated for rapid and on-site assessment of produce safety. Paving the way toward the development of this innovative methodology, specific original objectives were to (1) identify and distinguish different serotypes of Escherichia coli, Listeria monocytogenes, Salmonella typhimurium, and Bacillus cereus by FTIR and Raman spectroscopy, (2) develop spectroscopic fingerprint patterns and detection methodology for fungi such as Aspergillus, Rhizopus, Fusarium, and Penicillium (3) to validate a universal spectroscopic procedure to detect foodborne pathogens and non-pathogens in food systems. The original objectives proposed were very ambitious hence modifications were necessary to fit with the funding. Elaborate experiments were conducted for sensitivity, additionally, testing a wide range of pathogens (more than selected list proposed) was also necessary to demonstrate the robustness of the instruments, most crucially, algorithms for differentiating a specific organism of interest in mixed cultures was conceptualized and validated, and finally neural network and chemometric models were tested on a variety of applications. Food systems tested were apple juice and buffer systems. Pathogens tested include Enterococcus faecium, Salmonella enteritidis, Salmonella typhimurium, Bacillus cereus, Yersinia enterocolitis, Shigella boydii, Staphylococus aureus, Serratiamarcescens, Pseudomonas vulgaris, Vibrio cholerae, Hafniaalvei, Enterobacter cloacae, Enterobacter aerogenes, E. coli (O103, O55, O121, O30 and O26), Aspergillus niger (NRRL 326) and Fusarium verticilliodes (NRRL 13586), Saccharomyces cerevisiae (ATCC 24859), Lactobacillus casei (ATCC 11443), Erwinia carotovora pv. carotovora and Clavibacter michiganense. Sensitivity of the FTIR detection was 103CFU/ml and a clear differentiation was obtained between the different organisms both at the species as well as at the strain level for the tested pathogens. A very crucial step in the direction of analyzing mixed cultures was taken. The vector based algorithm was able to identify a target pathogen of interest in a mixture of up to three organisms. Efforts will be made to extend this to 10-12 key pathogens. The experience gained was very helpful in laying the foundations for extracting the true fingerprint of a specific pathogen irrespective of the background substrate. This is very crucial especially when experimenting with solid samples as well as complex food matrices. Spectroscopic techniques, especially FTIR and Raman methods are being pursued by agencies such as DARPA and Department of Defense to combat homeland security. Through the BARD US-3296-02 feasibility grant, the foundations for detection, sample handling, and the needed algorithms and models were developed. Successive efforts will be made in transferring the methodology to fruit surfaces and to other complex food matrices which can be accomplished with creative sampling methods and experimentation. Even a marginal success in this direction will result in a very significant breakthrough because FTIR and Raman methods, in spite of their limitations are still one of most rapid and nondestructive methods available. Continued interest and efforts in improving the components as well as the refinement of the procedures is bound to result in a significant breakthrough in sensor technology for food safety and biosecurity.

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Gillor, Osnat, Stefan Wuertz, Karen Shapiro, Nirit Bernstein, Woutrina Miller, Patricia Conrad, and Moshe Herzberg. Science-Based Monitoring for Produce Safety: Comparing Indicators and Pathogens in Water, Soil, and Crops. United States Department of Agriculture, May 2013. http://dx.doi.org/10.32747/2013.7613884.bard.

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Using treated wastewater (TWW) for crop irrigation represents an important opportunity for ensuring adequate food production in light of growing freshwater scarcity worldwide. However, the environmentally sustainable approach of using TWW for irrigation can lead to contamination of produce with fecal pathogens that may remain in treated water. The overall goal of this research was to evaluate the correlation between the presence of fecal indicator bacteria (FIB) and that of a suite of human pathogens in TWW, the irrigated soil, and crops. Field experiments were conducted to compare secondary and tertiary TWW with dechlorinated tap water for irrigation of tomatoes, a typical commercial crop, in Israel, a semi-arid country. Human pathogens including bacteria (Salmonella), protozoa (Cryptosporidiumand Giardia), and viruses (Adenovirus [AV Types A, B, C & 40/41] and Enterovirus [EV71 subtypes]) were monitored in two field trials using a combination of microscopic, cultivation-based, and molecular (qPCR) techniques. Results from the field trials indicate that microbial contamination on the surface of tomatoes did not appear to be associated with the source of irrigated waters; FIB contamination was not statistically different on tomatoes irrigated with TWW as compared to tomatoes irrigated with potable water. In fact, Indicator bacteria testing did not predict the presence of pathogens in any of the matrices tested. High concentrations of FIB were detected in water and on tomato surfaces from all irrigation treatment schemes, while pathogen contamination on tomato surfaces (Cryptosporidiumand Salmonella) was only detected on crops irrigated with TWW. These results suggest that regular monitoring for pathogens should take place to accurately detect presence of harmful microorganisms that could threaten consumer safety. A notable result from our study is that the large numbers of FIB in the water did not appear to lead to FIB accumulation in the soil. With the exception of two samples, E. coli that was present at 10³ to 10⁴ cells/100 mL in the water, was not detected in the soil. Other bacterial targets associated with the enteric environment (e. g., Proteusspp.) as well as protozoal pathogens were detected in the TWW, but not in the soil. These findings suggest that significant microbial transfer to the soil from TWW did not occur in this study. The pattern of FIB contamination on the surfaces of tomatoes was the same for all treatment types, and showed a temporal effect with more contamination detected as the duration of the field trial increased. An important observation revealed that water quality dramatically deteriorated between the time of its release from the wastewater treatment plant and the time it was utilized for irrigation, highlighting the importance of performing water quality testing throughout the growing season at the cultivation site.

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Barefoot, Susan F., Bonita A. Glatz, Nathan Gollop, and Thomas A. Hughes. Bacteriocin Markers for Propionibacteria Gene Transfer Systems. United States Department of Agriculture, June 2000. http://dx.doi.org/10.32747/2000.7573993.bard.

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The antibotulinal baceriocins, propionicin PLG-1 and jenseniin G., were the first to be identified, purified and characterized for the dairy propionibaceria and are produced by Propionibacterium thoenii P127 and P. thoenii/jensenii P126, respectively. Objectives of this project were to (a) produce polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1; (b) identify, clone and characterize the propionicin PLG-1 (plg-1) and jenseniin G (jnG) genes; and (3) develop gene transfer systems for dairy propionibacteria using them as models. Polyclonal antibodies for detection, comparison and monitoring of propionicin PLG-1 were produced in rabbits. Anti-PLG-1 antiserum had high titers (256,000 to 512,000), neutralized PLG-1 activity, and detected purified PLG-1 at 0.10 mg/ml (indirect ELISA) and 0.033 mg/ml (competitive indirect ELISA). Thirty-nine of 158 strains (most P. thoenii or P. jensenii) yielded cross-reacting material; four strains of P. thoenii, including two previously unidentified bacteriocin producers, showed biological activity. Eight propionicin-negative P127 mutants produced neither ELISA response nor biological activity. Western blot analyses of supernates detected a PLG-1 band at 9.1 kDa and two additional protein bands with apparent molecular weights of 16.2 and 27.5 kDa. PLG-1 polyclonal antibodies were used for detection of jenseniin G. PLG-1 antibodies neutralized jenseniin G activity and detected a jenseniin G-sized, 3.5 kDa peptide. Preliminary immunoprecipitation of crude preparations with PLG-1 antibodies yielded three proteins including an active 3-4 kDa band. Propionicin PLG-1 antibodies were used to screen a P. jensenii/thoenii P126 genomic expression library. Complete sequencing of a cloned insert identified by PLG-1 antibodies revealed a putative response regulator, transport protein, transmembrane protein and an open reading frame (ORF) potentially encoding jenseniin G. PCR cloning of the putative plg-1 gene yielded a 1,100 bp fragment with a 355 bp ORF encoding 118 amino acids; the deduced N-terminus was similar to the known PLG-1 N-terminus. The 118 amino acid sequence deduced from the putative plg-1 gene was larger than PLG-1 possibly due to post-translational processing. The product of the putative plg-1 gene had a calculated molecular weight of 12.8 kDa, a pI of 11.7, 14 negatively charged residues (Asp+Glu) and 24 positively charged residues (Arg+Lys). The putative plg-1 gene was expressed as an inducible fusion protein with a six-histidine residue tag. Metal affinity chromatography of the fused protein yielded a homogeneous product. The fused purified protein sequence matched the deduced putative plg-1 gene sequence. The data preliminarily suggest that both the plg-1 and jnG genes have been identified and cloned. Demonstrating that antibodies can be produced for propionicin PLG-1 and that those antibodies can be used to detect, monitor and compare activity throughout growth and purification was an important step towards monitoring PLG-1 concentrations in food systems. The unexpected but fortunate cross-reactivity of PLG-1 antibodies with jenseniin G led to selective recovery of jenseniin G by immunoprecipitation. Further refinement of this separation technique could lead to powerful affinity methods for rapid, specific separation of the two bacteriocins and thus facilitate their availability for industrial or pharmaceutical uses. Preliminary identification of genes encoding the two dairy propionibacteria bacteriocins must be confirmed; further analysis will provide means for understanding how they work, for increasing their production and for manipulating the peptides to increase their target species. Further development of these systems would contribute to basic knowledge about dairy propionibacteria and has potential for improving other industrially significant characteristics.

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