Статті в журналах: "ML TECHNIQUES"

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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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Akula, Roopesh. "Fraud identification of credit card using ML techniques." International Journal of Computing and Artificial Intelligence 1, no. 2 (July 1, 2020): 31–33. http://dx.doi.org/10.33545/27076571.2020.v1.i2a.15.

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S, Sabarinath, Thirumalaivasan R, Shiam S, Mohamed Aashik M. S, K. Sudhakar, and Dr P. Rama. "Analysis of Stock Price Prediction Using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1533–37. http://dx.doi.org/10.22214/ijraset.2023.50378.

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Abstract: Time series forecasting has been widely used to determine the future prices of stock, and the analysis and modelling of finance time series importantly guide investors’ decisions and trades. This work proposes an intelligent time series prediction system that uses sliding-window optimization for the purpose of predicting the stock prices using data science techniques. The system has a graphical user interface and functions as a stand-alone application. The proposed model is a promising predictive technique for highly non-linear time series, whose patterns are difficult to capture by traditional model.

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Reddy, N. Narasimha, and G. M. Vema Reddy. "DDoS Attack Detection in SDN using ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 2035–38. http://dx.doi.org/10.22214/ijraset.2023.56350.

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Abstract: The increasing prevalence of DDoS attacks poses a serious threat to modern network infrastructures. SDN has been proposed as a promising solution for enhancing network security. However, detecting and mitigating DDoS attack in software definednetwork remains a challenging task. In this research paper, suggest an innovative approach in order to identify DDoS assaults in software-defined networks using (ML) techniques. Ourmethod entails gathering and analyzing network data. Traffic data using SDN controllers. We use variety of ML techniques analyze the traffic information to discover unexpected traffic patterns that might point to the presence of a DDoS attack. Random Forest, DecisionTree, K-Means clustering are among the algorithms used. We evaluate our approach using a real-world dataset and compare it to existing DDoS detection techniques in SDN. Our results show that our approach achieves high accuracy, precision, and recall rates indetecting DDoS attacks. We also demonstrate that our technique can detect either known and unexpected DDoS assaults with low false-positive rates. Overall, our study indicates thepotency of applying machine learning methods to SDN DDoS attack detection. Our methodoffers a promising remedy for boosting network security in contemporary infrastructures.

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Drabeck, Lawrence, Buvana Ramanan, Thomas Woo, and Troy Cauble. "Automated Techniques for Creating Speech Corpora from Public Data Sources for ML Training." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 1–9. http://dx.doi.org/10.18178/ijmlc.2020.10.1.890.

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15

Kasture, Pradnya. "DDoS Attack Detection using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 6421–24. http://dx.doi.org/10.22214/ijraset.2023.53133.

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Abstract: DDoS attacks are an attempt to prevent the service from being unavailable by overloading the server with malicious traffic. In the past few years, distributed denial of service attacks is becoming the most difficult and burdensome problem. The number and magnitude of attacks have increased from few megabytes of data to 100s of terabytes of data these days. As there are different attack patterns or new types of attacks, it is difficult to detect such attacks effectively. New techniques for generating and mitigating distributed denial of service attacks have been developed in the present paper, which demonstrate that they are far superior to those currently used. In addition, in order to carry out a thorough investigation of the challenges presented by distributed denial of service attacks, we classify DDoS attack methods and techniques used for their detection. We're comparing the attack module to a few other tools out there.

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Sanapala, Anuradha, B. Jaya Lakshmi, K. Sandhya Rani Kundra, and K. B. Madhuri. "Air Pollution Detection and Control System Using ML Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4 (May 4, 2023): 219–25. http://dx.doi.org/10.17762/ijritcc.v11i4.6442.

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In present times, air pollution is increasing day by day, depriving the health of many people due to the various toxic components in air. So, it is necessary to monitor and detect the levels of pollution in various areas and try to control it by taking precautionary actions. Air pollution detection and control system is all about detecting the level of pollution in a particular area based on the amount of polluting components and proposing the measures to control the pollution. Analysis is made on the regions of Visakhapatnam city in Andhra Pradesh, India and grouped based on their pollution and displayed along with each component level, reasons for the pollution depending on each component and measures that can be followed. Apart from this, we also display list of top 10 regions with the highest values for each component which can be used to identify the harmful regions based on the toxic components.

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Mahesh T R and Vinoth Kumar. "Early Detection of Cancer using Machine Learning (ML) Techniques." International Journal of Information Technology, Research and Applications 2, no. 1 (March 31, 2023): 14–21. http://dx.doi.org/10.59461/ijitra.v2i1.24.

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Early detection of cancer sickness leads to rapid treatment, reducing the risk of morbidity and mortality. The diagnosis of oral cancer continues to be a challenge for dental careers, particularly in the location, evaluation, and review of early-stage oral disease. Due to the lack of optimal analysis using conventional methods, oral cancer is identified and grouped using AI at an early stage. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. AI techniques are used to show the movement and treatment of dangerous locations and may accurately predict future disease effects. A combination of expert AI and highlight determination calculations produces improved results in the early detection and forecast of oral cancer. The main goal and commitment of this audit study is to summarize the use of AI technologies for accurate early prediction of oral malignant development.

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Calabe P S, Prabha R, and Veena Potdar. "Cardiac ailment recognition using ML techniques in E-healthcare." World Journal of Advanced Research and Reviews 17, no. 1 (January 30, 2023): 302–7. http://dx.doi.org/10.30574/wjarr.2023.17.1.0010.

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Heart ailments can take numerous forms, and they are frequently referred to as cardio vascular illnesses. These can range from heart rhythm problems to birth anomalies to blood vessel disorders. It has been the main cause of death worldwide for several decades. To recognize the illness early and properly manage, it is critical to discover a precise and trustworthy approach for automating the process. Processing massive amounts of data in the field of medical sciences necessitates the application of data science. Here we employ a range of machine learning approaches to examine enormous data sets and aid in the accurate prediction of cardiac diseases. This paper explores the supervised learning models of Naive Bayes, Support Vector Machine, K-Nearest Neighbors, Decision Tree, in order to provide a comparison investigation for the most effective method. When compared to other algorithms, K-Nearest Neighbor provides the best accuracy at 86.89%.

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Bavarva, Sneh, Kalpesh Senva, and Priyank Bhojak. "SQLI Attack: An Approach using ML and Hybrid Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 10 (October 31, 2023): 1244–49. http://dx.doi.org/10.22214/ijraset.2023.56190.

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Abstract: Web-based systems are significantly at risk from SQL Injection Attacks (SQLIA), particularly in industries that handle sensitive data, like finance and healthcare. During these attacks, hostile actors insert false SQL queries into the database server of a web application in an effort to steal sensitive data. The use of classifiers and techniques like end-to-end deep learning and expanding the Aho-Corasick algorithm to detect SQLIA attacks have been covered in the literature. These researches have shed light on identifying and minimizing SQLIA, but the problem still exists. To detect and prevent SQLIA, a thorough methodology is suggested that combines static and dynamic studies with machine learning and hybrid tactics. The study compares several machine learning algorithms with hybrid techniques, showcasing the hybrid strategy's higher performance in both training and test sets. This method, which provides a practical means to secure web applications, is put out as a potential remedy for the persistent problem of SQLIA. In result, SQL Injection Attacks continue to be a danger, requiring effective ways for detection and prevention. It is highlighted as a promising solution the potential of machine learning and hybrid solutions, notably the hybrid approach. The study emphasizes how crucial it is to follow recommended procedures for protecting online applications from SQLIA attacks.

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詹木, 子豪. "Translation Techniques of Highway Engineering Price Foundation." Modern Linguistics 06, no. 03 (2018): 391–96. http://dx.doi.org/10.12677/ml.2018.63042.

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Choubey, Shubham. "Diabetes Prediction Using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4209–12. http://dx.doi.org/10.22214/ijraset.2023.54415.

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Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.

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Devi, P. Rama, Srujitha Meesala, Ramya Reddy, Kushal Senapathi, and Udaya Kolala. "Anticipating Consumer Demand using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1053–58. http://dx.doi.org/10.22214/ijraset.2023.50283.

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Abstract: Demand forecasting is essential for every growing online business. Without efficient demand forecasting systems in place, it might be next to impossible to always have the right amount of stock on hand. Because a food delivery service deals with a high volume of perishable raw materials, it is critical for the company to accurately forecast daily and weekly demand. If a warehouse has too much inventory, there is a greater likelihood of wastage, and if it has too little, there may be shortages, which would encourage customers to turn to your competitors. Therefore, predicting demand is one of the important tasks to be done. The project represents a food delivery company that operates in multiple cities. This particular company has various fulfillment centres in these cities for dispatching meal orders to their customers. Its objective is to anticipate consumer demand and the goal is to build a predictive regression model to assist the client in projecting demand for the following weeks so that these centres can organize their raw material stock properly, with the usage of various Machine Learning and Deep Learning Models and Techniques. For that purpose, there are various tools, techniques and methods are proposed. Linear regression model, Random Forest, XG Boosting, Decision tree is some of the models performed for getting the highest accuracy.

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Prasad Gundu, Ram, P. Pardhasaradhi, S. Koteswara Rao, and V. Gopi Tilak. "TOA-based source localization using ML estimation." International Journal of Engineering & Technology 7, no. 2.7 (March 18, 2018): 742. http://dx.doi.org/10.14419/ijet.v7i2.7.10936.

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This paper proposes the Time of arrival (TOA) measurement model for finding the position of a stationary emitting source for Line-of-Sight (LOS) scenario. Here Maximum Likelihood Estimation (MLE) is used as the positioning algorithm. For approximation of the roots of the solution, which directly corresponds to the source location, the optimization techniques used are Gauss-Newton, Gradient descent and Newton-Raphson methods. Two different cases are considered for investigation in this paper. The first case compares the three different optimization techniques in terms of convergence rate. In the second case the error values obtained from two different scenarios are compared, one involving a single trial only, while the second scenario uses Monte Carlo method of simulations. Firstly, the error values, for both the coordinates (two-dimensional), obtained by getting the difference between the measured source positions and the initially guessed source position are obtained for a single trial. Later using Monte Carlo simulation method, the Root-Mean-Square (RMS) error values, for both the coordinates (two-dimensional), for the optimization techniques are obtained. To improve the performance of the algorithm, Monte Carlo simulation has been used for multiple trials.

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Scheider, DM, R. Alhalel, M. Bourke, A. Elfant, P. Kortan, and GB Haber. "Endoscopic retrieval techniques for cable fractures complicating mechanical lithotripsy (ML)." Gastrointestinal Endoscopy 41, no. 4 (April 1995): 413. http://dx.doi.org/10.1016/s0016-5107(05)80508-5.

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Chandra Mouli, K., B. Indupriya, D. Ushasree, Ch V. Raghavendran, Babita Rawat, and Bhukya Madhu. "Network Intrusion Detection using ML Techniques for Sustainable Information System." E3S Web of Conferences 430 (2023): 01064. http://dx.doi.org/10.1051/e3sconf/202343001064.

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Network intrusion detection is a vital element of cybersecurity, focusing on identification of malicious activities within computer networks. With the increasing complexity of cyber-attacks and the vast volume of network data being spawned, traditional intrusion detection methods are becoming less effective. In response, machine learning has emerged as a promising solution to enhance the accuracy and efficiency of intrusion detection. This abstract provides an overview of proper utilization of machine learning techniques in intrusion detection and its associated benefits. The base paper explores various machine learning algorithms employed for intrusion detection and evaluates their performance. Findings indicate that machine learning algorithms exhibit a significant improvement in intrusion detection accuracy compared to traditional methods, achieving an accuracy rate of approximately 90 percent. It is worth noting that the previous work experienced computational challenges due to the time-consuming nature of the utilized algorithm when processing datasets. In this paper, we propose the exertion of more efficient algorithms to compute datasets, resulting in reduced processing time and increased precision compared to other algorithms to provide sustainability. This approach proves particularly when computational resources are limited or when the relationship between features and target variables is relatively straightforward.

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Ahire, Prof Pritam. "A Survey on Malware detection using ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (January 31, 2022): 314–24. http://dx.doi.org/10.22214/ijraset.2022.39813.

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Abstract: This Malware detection is a field of computer security that deals with the study and prevention of malicious software. It is not the only way to defend a company against a cyber- attack. In order to be effective, companies should analyse their risk and identify the vulnerabilities. In this paper, we will examine different techniques used to detect computer malware and malicious websites as well as future directives in this area of study and also, we will discuss the growth in computer malware and how traditional methods of detection are being replaced by innovative techniques like behavioural-based model and Signature-based model. Future directives involve developing better security products in order to fight against cyber fraud which is on a rise in recent years especially in Asia Pacific region. With this increase in cyber frauds and other malicious activities, traditional methods are not enough to block computers from it as this method has many drawbacks. In order to tackle these issues, researchers have been developing new techniques such as heuristic analysis, static & dynamic analysis which can detect more than 90% of malware samples without any false positives or negatives. Keywords: Behaviour-based approach, Dynamic analysis, Heuristic, Malware, Ransomware, Signature-based model, Static analysis, Vulnerability.

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Warghane, Atharva, Rohit Khawse, Lavanya Shinde, Kriti Deep Singh, Mohini Mokadam, and Shailesh Kurzadkar. "FREE GUY- A Hand Gesture Game Based on ML." International Journal of Computer Science and Mobile Computing 11, no. 2 (February 28, 2022): 23–26. http://dx.doi.org/10.47760/ijcsmc.2022.v11i02.003.

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Human Computer Interaction techniques became a bottleneck within the effective utilization of the obtainable info flow. The development of user interfaces affects changes in the Human Computer Interaction (HCI). The naturalness and intuitiveness of the hand gesture has been a major motivating factor for researchers in the HCI field to invest their efforts in researching and developing the most promising means of Human-Computer Interaction. This paper will let you know about the creation of hand gesture recognition program and implementation of its techniques in the computer game by controlling the movements of character introduced. The machine learning part is accomplished by the libraries like Tensorflow and OpenCV for setting up the detection, recognition and image processing techniques.

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Arslan, Ayse. "Mitigation Techniques to Overcome Data Harm in Model Building for ML." International Journal of Artificial Intelligence & Applications 13, no. 1 (January 31, 2022): 73–82. http://dx.doi.org/10.5121/ijaia.2022.13105.

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Given the impact of Machine Learning (ML) on individuals and the society, understanding how harm might be occur throughout the ML life cycle becomes critical more than ever. By offering a framework to determine distinct potential sources of downstream harm in ML pipeline, the paper demonstrates the importance of choices throughout distinct phases of data collection, development, and deployment that extend far beyond just model training. Relevant mitigation techniques are also suggested for being used instead of merely relying on generic notions of what counts as fairness.

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Li, Yuliang, Xiaolan Wang, Zhengjie Miao, and Wang-Chiew Tan. "Data augmentation for ML-driven data preparation and integration." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3182–85. http://dx.doi.org/10.14778/3476311.3476403.

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In recent years, we have witnessed the development of novel data augmentation (DA) techniques for creating additional training data needed by machine learning based solutions. In this tutorial, we will provide a comprehensive overview of techniques developed by the data management community for data preparation and data integration. In addition to surveying task-specific DA operators that leverage rules, transformations, and external knowledge for creating additional training data, we also explore the advanced DA techniques such as interpolation, conditional generation, and DA policy learning. Finally, we describe the connection between DA and other machine learning paradigms such as active learning, pre-training, and weakly-supervised learning. We hope that this discussion can shed light on future research directions for a holistic data augmentation framework for high-quality dataset creation.

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Jadhav, Neha, Prof Mrs M. E. Sanap, Samiksha Katore, Srushti Mahadik, and Akanksha Shendage. "Malware Detection and Prevention Using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (November 30, 2023): 356–57. http://dx.doi.org/10.22214/ijraset.2023.56514.

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Abstract: The most prevalent issue on the internet today is malware. Due to its dynamic nature and ability to inherit characteristics from other types, polymorphic malware constantly modifies its properties to avoid being identified by traditional signature methodologies. The activity is carried out either at a certain moment or after a specific period of time. This study investigates machine learning model-based behavior-based detection techniques for detecting malware families and predict their presence through static or dynamic analysis.

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Bhavani, Mudrakola, and Podila Mounika. "Educational Data Mining using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4940–47. http://dx.doi.org/10.22214/ijraset.2023.54210.

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Abstract: The ability to forecast students' performance is one of the most useful and important academic issues in the world today because of the development of technology. In the subject of education, data mining is incredibly useful, particularly for analysing student performance. The imbalanced datasets in this subject have made it extremely difficult to estimate students' performance, and there is no comparison of the various resampling techniques. This study compares multiple resampling procedures to manage the unbalanced information problem when projecting student performance of two distinctive datasets, including Borderline SMOTE, SMOTE-ENN, SMOTE, Random Over Sampler, SVM-SMOTE, and SMOTE-Tomek. Additionally, the dissimilarity between binary classification and multiclass, as well as the features' structures, are looked at. must be able to evaluate the effectiveness.

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ALGorain, Fahad T., and John A. Clark. "Covering Arrays ML HPO for Static Malware Detection." Eng 4, no. 1 (February 9, 2023): 543–54. http://dx.doi.org/10.3390/eng4010032.

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Malware classification is a well-known problem in computer security. Hyper-parameter optimisation (HPO) using covering arrays (CAs) is a novel approach that can enhance machine learning classifier accuracy. The tuning of machine learning (ML) classifiers to increase classification accuracy is needed nowadays, especially with newly evolving malware. Four machine learning techniques were tuned using cAgen, a tool for generating covering arrays. The results show that cAgen is an efficient approach to achieve the optimal parameter choices for ML techniques. Moreover, the covering array shows a significant promise, especially cAgen with regard to the ML hyper-parameter optimisation community, malware detectors community and overall security testing. This research will aid in adding better classifiers for static PE malware detection.

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Patil, Vijeeta, Pratima Ghattarki, and Vilas Naik. "A Comparative Study of Different ML Techniques for Medical Image Analysis." Journal of Image Processing and Artificial Intelligence 7, no. 1 (February 22, 2021): 21–31. http://dx.doi.org/10.46610/joipai.2021.v07i01.003.

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Patil, Anuj, Anklesh Patil, and Jivan Devhare. "Cyberbullying Detection in social media Using Supervised ML & NLP Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 8 (August 31, 2022): 469–71. http://dx.doi.org/10.22214/ijraset.2022.46219.

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Abstract: From the day internet came into existence, the era of social networking sprouted. In the beginning, no one may have thought the internet would be a host of numerous amazing services the social networking. Today we can say that online applications and social networking websites have become a non-separable part of one’s life. Many people from diverse age groups spend hours daily on such websites. Despite thoughtlet is emotionally connected through media, these facilities bring along big threats with them such as cyber-attacks, which includes include lying. As social networking sites are increasing, cyberbullying is increasing day by day. To identify word similarities in the tweets made by bullies and make use of machine learning and can develop an ML model that automatically detects social media bullying actions. However, many social media bullying detection techniques have been implemented, but many of them were textual based. Under this background and motivation, it can help to prevent the happen of cyberbullying if we can develop relevant techniques to discover cyberbullying in social media. A machine learning model is proposed to detect and prevent bullying on Twitter. Naïve Bayes is used for training and testing social media bullying content.

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Jayakumar, D., S. Srinivasan, G. Meghana, B. Sai Harika, and K. Yasaswini Priya. "An Eminent Spam Noticing Methodology for IOT Gadgets Using ML Techniques." Revista Gestão Inovação e Tecnologias 11, no. 2 (June 18, 2021): 2156–66. http://dx.doi.org/10.47059/revistageintec.v11i2.1857.

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Net of factors (IoT) is also a bunch of numerous sensory gadgets and actuators connected over a wireless or wi-fi channel for statistics transmission. IoT is growing on a everyday for the past few years. The little print are substantially extended inside the upcoming years. Moreover to improved extent, IoT gadgets produce big amounts of information in lots of unique methods with unique statistics first-rate described by their pace through the years and dependence. The gadget gaining knowledge of (ml) algorithm plays an vital role in protection and authorization supported the invention of artificial biotechnology to enhance the utilization and security of IoT structures. Attackers frequently looked for gaining knowledge of algorithms to need advantage of being susceptible to IoT systems designed for smart. Protection for IoT gadgets by way of detecting spam the usage of ml. Unsolicited mail detection on IoT employing a system getting to know Framework is proposed.

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Arunkumar, A., and D. Surendran. "Autism Spectrum Disorder Diagnosis Using Ensemble ML and Max Voting Techniques." Computer Systems Science and Engineering 41, no. 1 (2022): 389–404. http://dx.doi.org/10.32604/csse.2022.020256.

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Ali, Rao Faizan, Amgad Muneer, Ahmed Almaghthawi, Amal Alghamdi, Suliman Mohamed Fati, and Ebrahim Abdulwasea Abdullah Ghaleb. "BMSP-ML: big mart sales prediction using different machine learning techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (June 1, 2023): 874. http://dx.doi.org/10.11591/ijai.v12.i2.pp874-883.

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<span lang="EN-US">Variations in sales over time is the main issue faced by many retailers. To overcome this problem, we attempt to predict the sales by comparing the previous sales data of different stores. Firstly, the primary task is to recognize the pattern of the factors that help to predict sales. This study helps us understand the data and predict sales using many machines learning models. This process gets the data and beautifies the data by imputing the missing values and feature engineering. While solving this problem, predicting the monthly sales value is significant in the study. In addition, an essential element is to clear the missing data and perform proper feature engineering to better understand them before applying them. The experimental results show that the random forest predictor has outperformed ridge regression, linear regression, and decision tree models among the four machine learning techniques implemented in this study. The performance of the proposed models has been evaluated using root mean square error (RMSE).</span>

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Rastogi, Rohit, Priyanshu Arora, Luv Dhamija, and Rajat Srivastava. "Statistical Analysis of Online Voting System Through Blockchain and ML Techniques." International Journal of Cyber Behavior, Psychology and Learning 12, no. 1 (January 1, 2022): 1–19. http://dx.doi.org/10.4018/ijcbpl.313947.

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A digital voting system is a process that allows people to vote while sitting at their homes and is based on their face recognition identification. The votes will be counted and saved in a blockchain-based structure which is secure and immutable, thus giving availability with security in a system. The traditional voting system does not allow people to vote sitting at their home. Considering the situation of covid, everything is going digital. Questions on EVM from losing parties regarding some malfunctioning.

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Badi, Haitham, Sabah Hasan Hussein, and Sameem Abdul Kareem. "RETRACTED ARTICLE: Feature extraction and ML techniques for static gesture recognition." Neural Computing and Applications 25, no. 3-4 (January 18, 2014): 733–41. http://dx.doi.org/10.1007/s00521-013-1540-6.

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A. Alsemmeari, Rayan, Mohamed Yehia Dahab, Badraddin Alturki, and Abdulaziz A. Alsulami. "Priority Detector and Classifier Techniques Based on ML for the IoMT." Computers, Materials & Continua 76, no. 2 (2023): 1853–70. http://dx.doi.org/10.32604/cmc.2023.038589.

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Dr., Sudalaimani, and Revathi L.V. "Fault Diagnosis of Wind Turbines using Scada Data by ML Techniques." International Journal of Innovative Research in Advanced Engineering 10, no. 07 (July 31, 2023): 549–57. http://dx.doi.org/10.26562/ijirae.2023.v1007.19.

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Fault diagnosis technology is crucial for the safety and stability of the operation of wind turbines. Efficient wind turbine fault diagnosis technology quickly identifies the types of failures in order to reduce the operating and maintenance costs of wind farms and improve the efficiency of power generation. The majority of wind farms currently use supervisory control and data acquisition (SCADA) systems to acquire operation and maintenance data. SCADA systems are rich in data pertaining to the working parameters of wind turbines. Moreover, fault diagnostic functionality is not common in SCADA systems. SVMs are a common intelligence technique used in the fault diagnostics of wind turbines. The choice of SVM parameters is essential for precise model classification. The penalty factor and kernel function parameter of SVM were optimi zed in this study along with the construction of the SSA-ABC wind turbine defect detection model using the Artificial Bee Colony (ABC), a brand-new and very effective optimization technique. Data are obtained from a wind farm SCADA system and, after pre-processing and feature selection, entail a faulting set. Evidence suggests that the ABC-SVM diagnostic model, which has a quick convergence rate and a potent optimization capability, effectively enhances the accuracy of wind turbine fault diagnosis when compared to the GS-SVM, GA-SVM, and PSO-SVM models. The ABC-SVM diagnostic model may also be utilized in real-world engineering applications to identify defects.

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Nagy, Naya, Malak Aljabri, Afrah Shaahid, Amnah Albin Ahmed, Fatima Alnasser, Linda Almakramy, Manar Alhadab, and Shahad Alfaddagh. "Phishing URLs Detection Using Sequential and Parallel ML Techniques: Comparative Analysis." Sensors 23, no. 7 (March 26, 2023): 3467. http://dx.doi.org/10.3390/s23073467.

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In today’s digitalized era, the world wide web services are a vital aspect of each individual’s daily life and are accessible to the users via uniform resource locators (URLs). Cybercriminals constantly adapt to new security technologies and use URLs to exploit vulnerabilities for illicit benefits such as stealing users’ personal and sensitive data, which can lead to financial loss, discredit, ransomware, or the spread of malicious infections and catastrophic cyber-attacks such as phishing attacks. Phishing attacks are being recognized as the leading source of data breaches and the most prevalent deceitful scam of cyber-attacks. Artificial intelligence (AI)-based techniques such as machine learning (ML) and deep learning (DL) have proven to be infallible in detecting phishing attacks. Nevertheless, sequential ML can be time intensive and not highly efficient in real-time detection. It can also be incapable of handling vast amounts of data. However, utilizing parallel computing techniques in ML can help build precise, robust, and effective models for detecting phishing attacks with less computation time. Therefore, in this proposed study, we utilized various multiprocessing and multithreading techniques in Python to train ML and DL models. The dataset used comprised 54 K records for training and 12 K for testing. Five experiments were carried out, the first one based on sequential execution followed by the next four based on parallel execution techniques (threading using Python parallel backend, threading using Python parallel backend and number of jobs, threading manually, and multiprocessing using Python parallel backend). Four models, namely, random forest (RF), naïve bayes (NB), convolutional neural network (CNN), and long short-term memory (LSTM) were deployed to carry out the experiments. Overall, the experiments yielded excellent results and speedup. Lastly, to consolidate, a comprehensive comparative analysis was performed.

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Vernekar, Pratham, Aniruddha Singh, and Dr Kailash Patil. "Pothole and Wet Surface Detection Using Pretrained Models and ML Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 626–33. http://dx.doi.org/10.22214/ijraset.2023.49489.

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Abstract: Roads contribute significantly to the economy and serve as a transportation platform. Road potholes are a key source of worry in transportation infrastructure. The purpose of this research is to develop an Artificial Intelligence (AI) model for identifying potholes on asphalt pavement surfaces. Image processing techniques from pretrained models such as efficientnet, resnet50, mobilenet and ML models such as random forest, decision tree, SVC, SVM. Several studies have advocated employing computer vision techniques, including as image processing and object identification algorithms, to automate pothole detection. It is important to digitize the pothole identification process with acceptable accuracy and speed, as well as to deploy the procedure conveniently and affordably. Initially, a smartphone placed on the automobile windscreen captures many photographs of potholes. Later, by downloading pothole photographs from the internet, we expanded the amount and variety of our collection (2400 images with over 900 potholes). Second, to locate potholes in road photos, several object detection methods are used. To compare pothole detection performance, real-time Deep Learning algorithms in various setups are employed. Similarly Wet pavement decreases surface friction dramatically, increasing the likelihood of an accident. As a result, timely understanding of road surface condition is essential for safe driving. This research proposes a unique machine learning model pipeline for detecting pavement moisture based on live photos of highway scenes acquired via accessible to the public traffic cameras. We refined existing state-of-the-art feature extraction baseline models to capture background instance targets, such as pavement, sky, and vegetation, which are frequent in highway scenes, to simplify the learning job.

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Pandey, Prachi, and Abhijitha Bandaru. "Enhancing predictive accuracy of asset returns by experimenting with ML techniques." SHS Web of Conferences 169 (2023): 01062. http://dx.doi.org/10.1051/shsconf/202316901062.

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The unparalleled success of machine learning is indisputable. It has transformed the world with unimaginable solutions to insistent problems. The remarkable accuracy that machine learning manifests for making estimations is an object of fascination for plenty of researchers all over the world. The financial industry has also benefited from the growth of this electrifying field to predict asset returns, creditworthiness of a customer, and portfolio management, among others. In this research, we spotlight how this accuracy is contingent upon the analysis of various aspects of the data. We also experiment with simple techniques to make predictions and our findings suggest how these methods overshadow neural nets. The results indicate that the penalized linear models deliver the best performance. Random forest models had not been effective though. Machine learning models fitted with respect to median quantile loss were similarly observed to typically offer improvements across all machine learning models across all loss metrics. While little is known about the future of asset return that involves various risk and uncertainty, the recent enhancements in a machine learning field can contribute to deep domain training. Machine learning is increasingly gaining popularity nowadays in sectors including engineering, charity work, etc. Recently, even behavioral economics has started to leverage machine-learning expertise.

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Vishwakarma, Ashish, and Deepak Pancholi. "Performance Analysis of V-Blast Spatial Multiplexing with Ml and MMSE Equalisation Techniques using Psk Modulation." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2302–5. http://dx.doi.org/10.31142/ijtsrd14419.

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Singh, Balraj, Aadil Shaikh, Priyanka Jadhav, Rajneesh Chaturvedi, and Prof Ankit Sanghvi. "Cinephiles Integration System using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 3349–53. http://dx.doi.org/10.22214/ijraset.2023.50947.

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Abstract: Our project aims to create a movie recommendation and community platform where users can discover and share their favorite movies with others. The platform will utilize a recommendation system to suggest personalized movie recommendations based on the user's preferences and viewing history. Users will also be able to rate and review movies, create watchlists, and follow other users with similar movie tastes. The community aspect of the platform will allow users to engage with others through forums, discussions, and private messaging. This will create a space for movie enthusiasts to connect and share their thoughts on the latest releases, hidden gems, and all-time favorites. To develop the recommendation system, we will use collaborative filtering and content-based filtering techniques. The platform will also utilize machine learning algorithms to analyze user behavior and provide more accurate recommendations over time. Overall, our movie recommendation and community platform will provide a comprehensive and interactive movie-watching experience for users while promoting a sense of community among movie enthusiasts.

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Sundaram, Karthik Trichur. "Digital Transformation with AI/ML & Cybersecurity." International Journal of Computer Science and Mobile Computing 11, no. 11 (November 30, 2022): 1–3. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.001.

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Artificial Intelligence (AI) and Machine Learning (ML) have impacted the manufacturing industry, especially in the industry 4.0 paradigm. It encourages the usage of smart devices, sensors, and machines for production. Moreover, AI techniques and ML algorithms give predictive insights into various manufacturing tasks, such as predictive maintenance, continuous inspection, process optimization, quality improvement, and more. However, there are many open concerns and challenges regarding cybersecurity in smart manufacturing.

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Santhiya, S., N. Abinaya, P. Jayadharshini, S. Priyanka, S. Keerthika, and C. Sharmila. "Orthopedic patient analysis using machine learning techniques." Journal of Physics: Conference Series 2664, no. 1 (December 1, 2023): 012004. http://dx.doi.org/10.1088/1742-6596/2664/1/012004.

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Abstract Orthopedic patients have been increasing in hospital because of road traffic accidents, advanced age, a lack of exercise, inadequate nutrition, and other factors. The suggested article uses Machine Learning (ML) techniques to examine the patient reports. The ability to mimic the human actions is called ML. It is a subclass of AI that solves a number of healthcare-related issues. Here ML algorithms are used for health-related data. It solves a number of healthcare-related issues. ML is the process of a machine imitating intelligent human activities. It belongs to the Artificial Intelligence (AI) subclass. ML algorithms are used for medical data such as Logistic Regression, Support vector machine, K-Nearest Neighbor, Random Forest, Decision Tree, Artificial Neural Network to predict orthopedic illnesses such as Normal, Hernia and Spondylolisthesis orthopedic. ML techniques have increased the speed and accuracy for diagnosis. The most serious and urgent cases require rapid care. It improves patient care by lowering human error and stress on medical staff. Our primary objective is to improve machine performance and decrease incorrect categorization.

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Kandula, Lohith Ranganatha Reddy, T. Jaya Lakshmi, Kalavathi Alla, and Rohit Chivukula. "An Intelligent Prediction of Phishing URLs Using ML Algorithms." International Journal of Safety and Security Engineering 12, no. 3 (June 30, 2022): 381–86. http://dx.doi.org/10.18280/ijsse.120312.

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History shows that, several cloned and fraudulent websites are developed in the World Wide Web to imitate legitimate websites, with the main motive of stealing sensitive important informational and economic resources from web surfers and financial organizations. This is a type of phishing attack, and it has cost the online networking community and all other stakeholders thousands of million Dollars. Hence, efficient counter measures are required to detect phishing URLs accurately. Machine learning algorithms are very popular for all types of data analysis and these algorithms are depicting good results in battling with phishing when we compare with other classic anti-phishing approaches, like cyber security awareness workshops, visualization approaches giving some legal countermeasures to these cyber-attacks. In this research work authors investigated different Machine Learning techniques applicability to identify phishing attacks and distinguishes their pros and cons. Specifically, various types of Machine Learning techniques are applied to reveal diverse approaches which can be used to handle anti-phishing approaches. In this work authors have experimentally compared large number of ML techniques on different phishing datasets by using various metrics. The main focus in this comparison is to showcase advantages and disadvantages of ML predictive models and their actual performance in identifying phishing attacks.

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Meltzer, Jed A. "Localizing the component processes of lexical access using modern neuroimaging techniques." Mental Lexicon 7, no. 1 (June 8, 2012): 91–118. http://dx.doi.org/10.1075/ml.7.1.05mel.

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Neuroimaging plays an increasingly important role in the investigation of all aspects of human cognition, including language. Historically, experimental psychology and neuroimaging relied on very different techniques, as neuroimaging studies required comparisons between different tasks rather than manipulation of conditions within a single task, as is standard in behavioural experiments. However, methodology has advanced in the past decade such that many classic behavioural paradigms can now be employed in studies that measure brain activity. We review the technical foundations of conducting studies on single-trial brain responses, using event-related fMRI and electrophysiological recordings. We focus in particular on studies of picture naming, illustrating how the same techniques that were originally used to define temporal processing stages in reaction time studies can now be applied to brain imaging studies to reveal the neural localization of those stages.

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