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Journal articles on the topic 'Popularity detection'

Author: Grafiati
Published: 25 May 2024

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1

Zhang, Xiaoming, Xiaoming Chen, Yan Chen, Senzhang Wang, Zhoujun Li, and Jiali Xia. "Event detection and popularity prediction in microblogging." Neurocomputing 149 (February 2015): 1469–80. http://dx.doi.org/10.1016/j.neucom.2014.08.045.

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NN, Mrs Deepti. "D-SCAN : DEPRESSION DETECTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (April 23, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem31462.

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Depression is a serious illness that affects millions of people globally. From child to senior citizen are facing depression. Major area is occupied by adults, college going students and teenagers also. In recent years, the task of automation depression detection from speech has gained popularity. We provide a comparative analyses of various features for depression detection by evaluating how a system built on text-based, voice-based, and speech-based system. Detecting texts that express negativity in the data is one of the best ways to detect depression. In this paper, this problem of depression detection on social media and various machine learning algorithms that can be used to detect depression have been discussed. Key Words: Depression, Face detection, Audio detection, Video detection, Healthcare innovation, Result.
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Miao, Zhongchen, Kai Chen, Yi Fang, Jianhua He, Yi Zhou, Wenjun Zhang, and Hongyuan Zha. "Cost-Effective Online Trending Topic Detection and Popularity Prediction in Microblogging." ACM Transactions on Information Systems 35, no. 3 (June 9, 2017): 1–36. http://dx.doi.org/10.1145/3001833.

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Wolcott, M. J. "Advances in nucleic acid-based detection methods." Clinical Microbiology Reviews 5, no. 4 (October 1992): 370–86. http://dx.doi.org/10.1128/cmr.5.4.370.

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Laboratory techniques based on nucleic acid methods have increased in popularity over the last decade with clinical microbiologists and other laboratory scientists who are concerned with the diagnosis of infectious agents. This increase in popularity is a result primarily of advances made in nucleic acid amplification and detection techniques. Polymerase chain reaction, the original nucleic acid amplification technique, changed the way many people viewed and used nucleic acid techniques in clinical settings. After the potential of polymerase chain reaction became apparent, other methods of nucleic acid amplification and detection were developed. These alternative nucleic acid amplification methods may become serious contenders for application to routine laboratory analyses. This review presents some background information on nucleic acid analyses that might be used in clinical and anatomical laboratories and describes some recent advances in the amplification and detection of nucleic acids.
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Hao, Yaojun, Peng Zhang, and Fuzhi Zhang. "Multiview Ensemble Method for Detecting Shilling Attacks in Collaborative Recommender Systems." Security and Communication Networks 2018 (October 11, 2018): 1–33. http://dx.doi.org/10.1155/2018/8174603.

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Faced with the evolving attacks in collaborative recommender systems, the conventional shilling detection methods rely mainly on one kind of user-generated information (i.e., single view) such as rating values, rating time, and item popularity. However, these methods often suffer from poor precision when detecting different attacks due to ignoring other potentially relevant information. To address this limitation, in this paper we propose a multiview ensemble method to detect shilling attacks in collaborative recommender systems. Firstly, we extract 17 user features by considering the temporal effects of item popularity and rating values in different popular item sets. Secondly, we devise a multiview ensemble detection framework by integrating base classifiers from different classification views. Particularly, we use a feature set partition algorithm to divide the features into several subsets to construct multiple optimal classification views. We introduce a repartition strategy to increase the diversity of views and reduce the influence of feature order. Finally, the experimental results on the Netflix and Amazon review datasets indicate that the proposed method has better performance than benchmark methods when detecting various synthetic attacks and real-world attacks.
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Skaperas, Sotiris, Lefteris Mamatas, and Arsenia Chorti. "Real-Time Video Content Popularity Detection Based on Mean Change Point Analysis." IEEE Access 7 (2019): 142246–60. http://dx.doi.org/10.1109/access.2019.2940816.

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Singha, Subroto, and Burchan Aydin. "Automated Drone Detection Using YOLOv4." Drones 5, no. 3 (September 11, 2021): 95. http://dx.doi.org/10.3390/drones5030095.

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Drones are increasing in popularity and are reaching the public faster than ever before. Consequently, the chances of a drone being misused are multiplying. Automated drone detection is necessary to prevent unauthorized and unwanted drone interventions. In this research, we designed an automated drone detection system using YOLOv4. The model was trained using drone and bird datasets. We then evaluated the trained YOLOv4 model on the testing dataset, using mean average precision (mAP), frames per second (FPS), precision, recall, and F1-score as evaluation parameters. We next collected our own two types of drone videos, performed drone detections, and calculated the FPS to identify the speed of detection at three altitudes. Our methodology showed better performance than what has been found in previous similar studies, achieving a mAP of 74.36%, precision of 0.95, recall of 0.68, and F1-score of 0.79. For video detection, we achieved an FPS of 20.5 on the DJI Phantom III and an FPS of 19.0 on the DJI Mavic Pro.
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Madana Mohana, R., Paramjeet Singh, Vishal Kumar, and Sohail Shariff. "Brutality detection and rendering of brutal frames." MATEC Web of Conferences 392 (2024): 01072. http://dx.doi.org/10.1051/matecconf/202439201072.

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The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.
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Satwik, Pallerla. "Hate Speech Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 1646–49. http://dx.doi.org/10.22214/ijraset.2024.59053.

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Abstract: The rise in popularity of microblogging sites such as Facebook, Instagram, and Twitter has resulted in more people from different backgrounds indirectly communicating with one another. Our study aims to design an autonomous Deep Neural Network (DNN) algorithm for social media hate speech detection to tackle this problem. Using cutting-edge Natural Language Processing (NLP) techniques, the objective is to build a strong system that can recognize and categorize hate speech material in text data with accuracy. Our DNN algorithm allows for the real-time detection and moderation of offensive information, providing a proactive strategy against online hate speech. With the deployment of this technology, everyone will be able to access a safer and more welcoming online environment.
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Patil, Vaibhavi, Sakshi Patil, Krishna Ganjegi, and Pallavi Chandratre. "Face and Eye Detection for Interpreting Malpractices in Examination Hall." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1119–23. http://dx.doi.org/10.22214/ijraset.2022.41456.

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Abstract: One of the most difficult problems in computer vision is detecting faces and eyes. The purpose of this work is to give a review of the available literature on face and eye detection, as well as assessment of gaze. With the growing popularity of systems based on face and eye detection in a range of disciplines in recent years, academia and industry have paid close attention to this topic. Face and eye identification has been the subject of numerous investigations. Face and eye detection systems have made significant process despite numerous challenges such as varying illumination conditions, wearing glasses, having facial hair or moustache on the face, and varying orientation poses or occlusion of the face. We categorize face detection models and look at basic face detection methods in this paper. We categorize face detection models and look at basic face detection methos in this paper. Then we’ll go through eye detection and estimation techniques. Keywords: Image Processing, Face Detection, Eye Detection, Gaze Estimation
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Thanvanthri, Srinedhi, and Shivani Ramakrishnan. "Performance of Text Classification Methods in Detection of Hate Speech in Media." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 354–58. http://dx.doi.org/10.22214/ijraset.2022.40567.

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Abstract: With the increased popularity of social media sites like Twitter and Instagram over the years, it has become easier for users of the sites to remain anonymous while taking part in hate speech against various peoples and communities. As a result, in an effort to curb such hate speech online, detection of the same has gained a lot more attention of late. Since curbing the growing amount of hate speech online by manual methods is not feasible, detection and control via Natural Language Processing and Deep Learning methods has gained popularity. In this paper, we evaluate the performance of a sequential model with the Universal Sentence Encoder against the RoBERTa method on different datasets for hate speech detection. The result of this study has shown a greater performance overall from using a Sequential model with a multilingual USE layer. Keywords: Hate Speech Detection, RoBERTa, Universal Sentence Encoder, Sequential model.
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Haimovich, Daniel, Dima Karamshuk, Thomas J. Leeper, Evgeniy Riabenko, and Milan Vojnovic. "Popularity prediction for social media over arbitrary time horizons." Proceedings of the VLDB Endowment 15, no. 4 (December 2021): 841–49. http://dx.doi.org/10.14778/3503585.3503593.

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Predicting the popularity of social media content in real time requires approaches that efficiently operate at global scale. Popularity prediction is important for many applications, including detection of harmful viral content to enable timely content moderation. The prediction task is difficult because views result from interactions between user interests, content features, resharing, feed ranking, and network structure. We consider the problem of accurately predicting popularity both at any given prediction time since a content item's creation and for arbitrary time horizons into the future. In order to achieve high accuracy for different prediction time horizons, it is essential for models to use static features (of content and user) as well as observed popularity growth up to prediction time. We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content's popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth. This results in a highly scalable method for popularity prediction over arbitrary prediction time horizons that also achieves a high degree of accuracy, compared to several leading baselines, on a dataset of public page content on Facebook over a two-month period, covering billions of content views and hundreds of thousands of distinct content items. The model has shown competitive prediction accuracy against a strong baseline that consists of separately trained models for specific prediction time horizons.
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Pattanaik, Debasish, Sarat Chandra Swain, Indu Sekhar Samanta, Ritesh Dash, and Kunjabihari Swain. "Power Quality Disturbance Detection and Monitoring of Solar Integrated Micro-Grid." WSEAS TRANSACTIONS ON POWER SYSTEMS 17 (October 6, 2022): 306–15. http://dx.doi.org/10.37394/232016.2022.17.31.

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Due to the popularity of microgrids and power quality disturbances (PQD) induced by renewable energies, monitoring in microgrids has risen in popularity in recent years. For monitoring the PQD, many strategies based on artificial intelligence have been proposed. However, when the electrical parameters change, the need to retrain the Artificial neural network (ANN) becomes a significant issue. This paper presents a new approach to the power quality disturbance detection and monitoring of integrated solar microgrids. The power quality event detection is accomplished by analyzing the frequency signal with Wavelet transformation (WT). The classification of power quality disturbance is achieved based on the features. For the classification of PQDs, the retrieved features are fed into a Convolutional neural network (CNN) classifier.
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Liu, Xiao, Wenjun Wang, Dongxiao He, Pengfei Jiao, Di Jin, and Carlo Vittorio Cannistraci. "Semi-supervised community detection based on non-negative matrix factorization with node popularity." Information Sciences 381 (March 2017): 304–21. http://dx.doi.org/10.1016/j.ins.2016.11.028.

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15

Shaheer, Rizana, and Malu U. "Real-Time Video Violence Detection Using CNN." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2586–90. http://dx.doi.org/10.22214/ijraset.2023.52182.

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Abstract: In order to effectively enforce the law and keep cities secure, monitoring technologies that detect violent events are becoming increasingly important. In computer vision, the practice of action recognition has gained popularity. In the field of computer vision, action recognition has gained popularity. The action recognition group, however, has mainly concentrated on straightforward activities like clapping, walking, jogging, etc. Comparatively little study has been done on identifying specific occurrences that have immediate practical applications, like fighting or violent behaviors in general. The responsiveness, precision, and flexibility of violent event detectors are indicators of their effectiveness across a range of video sources. This capacity might be helpful in specific video surveillance situations. Several research focused on violence identification with an emphasis on speed, accuracy, or both while ignoring the generalizability of various video source types. In this paper, a deeplearning-based real-time violence detector has been proposed. CNN serves as an extractor of spatial features in the suggested model. Here, a convolutional neural network (CNN) architecture called MobileNet V2 is utilized to extract frame-level information from a video, and LSTM, which focuses on the three factors (overall generality, accuracy, and quick reaction) as a temporal relation learning approach.
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Shyla and Vishal Bhatnagar. "Comprehensive Examination of Network Intrusion Detection Models on Data Science." International Journal of Information Retrieval Research 11, no. 4 (October 2021): 14–40. http://dx.doi.org/10.4018/ijirr.2021100102.

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The increased requirement of data science in recent times has given rise to the concept of data security, which has become a major issue; thus, the amalgamation of data science methodology with intrusion detection systems as a field of research has acquired a lot of prominence. The level of access to the information system and its visibility to user pursuit was required to operate securely. Intrusion detection has been gaining popularity in the area of data science to incorporate the overall information security infrastructure, where regular operations depend upon shared use of information. The problems are to build an intrusion detection system efficient enough for detecting attacks and to reduce the false positives with a high detection rate. In this paper, the authors analyse various techniques of intrusion detection combined with data science, which will help in understanding the best fit technique under different circumstances.
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Iqbal, Nafees, Syed Abid Ali, Iqra Munir, Saima Khan, Khurshid Ayub, Mariya al-Rashida, Muhammad Islam, Zahid Shafiq, Ralf Ludwig, and Abdul Hameed. "Acridinedione as selective flouride ion chemosensor: a detailed spectroscopic and quantum mechanical investigation." RSC Advances 8, no. 4 (2018): 1993–2003. http://dx.doi.org/10.1039/c7ra11974g.

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18

Ramotsoela, Daniel T., Gerhard P. Hancke, and Adnan M. Abu-Mahfouz. "Practical Challenges of Attack Detection in Microgrids Using Machine Learning." Journal of Sensor and Actuator Networks 12, no. 1 (January 18, 2023): 7. http://dx.doi.org/10.3390/jsan12010007.

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The move towards renewable energy and technological advancements in the generation, distribution and transmission of electricity have increased the popularity of microgrids. The popularity of these decentralised applications has coincided with advancements in the field of telecommunications allowing for the efficient implementation of these applications. This convenience has, however, also coincided with an increase in the attack surface of these systems, resulting in an increase in the number of cyber-attacks against them. Preventative network security mechanisms alone are not enough to protect these systems as a critical design feature is system resilience, so intrusion detection and prevention system are required. The practical consideration for the implementation of the proposed schemes in practice is, however, neglected in the literature. This paper attempts to address this by generalising these considerations and using the lessons learned from water distribution systems as a case study. It was found that the considerations are similar irrespective of the application environment even though context-specific information is a requirement for effective deployment.
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Huang, Hsin Haou, and Chun Kun Chiang. "Damage Localization in Plate Structures Based on Baseline-Free Method of Lamb Wave Using Mobile Transducer Set." Key Engineering Materials 970 (December 15, 2023): 119–23. http://dx.doi.org/10.4028/p-kvttx1.

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Ultrasonic Lamb waves have gained popularity in non-destructive testing of plate-like structures due to their advantages such as low attenuation, high sensitivity, and wide detection range. This paper presents a novel baseline-free method for inspecting curved plate-like structures based on reciprocity loss. The method combines a modified damage imaging algorithm, a baseline-free detection method based on time reciprocity, and a calculation method for damage index values using the analysis of the focus position of time reciprocity signals. Experimental results demonstrate favorable effectiveness of the baseline-free method in detecting and locating multiple defects in the curved plate made of composite laminate.
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AlKhonaini, Arwa, Tarek Sheltami, Ashraf Mahmoud, and Muhammad Imam. "UAV Detection Using Reinforcement Learning." Sensors 24, no. 6 (March 14, 2024): 1870. http://dx.doi.org/10.3390/s24061870.

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Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.
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C, Saranya, Santosh Kumar, Lokesh S, and Ram Ratan. "Spam Detection on Social Media Platform." International Journal of Innovative Research in Advanced Engineering 10, no. 06 (June 23, 2023): 355–61. http://dx.doi.org/10.26562/ijirae.2023.v1006.20.

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Social Media has become an essential platform for communication and information sharing. With its widespread popularity, the problem of spam has also become increasingly common. In this paper, we propose an effective machine learning- based approach for spam detection on social media. Our approach uses various features and classifiers to distinguish spam messages from legitimate ones. The features used include lexical, syntactic, and semantic features, while the classifiers used include decision trees, naive Bayes, and support vector machines. Our experimental results demonstrate that the proposed approach outperforms existing spam detection methods on social media.
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Wang, Zhen Qi, and Dan Kai Zhang. "HIDS and NIDS Hybrid Intrusion Detection System Model Design." Advanced Engineering Forum 6-7 (September 2012): 991–94. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.991.

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With the popularity of Internet applications, network security has become one of the issues affecting the world economy. Currently, there is a large space to develop for intrusion detection systems as a relatively new field. For the faults of HIDS or NIDS network intrusion detection system, Papers has designed a hybrid HIDS and NIDS intrusion detection system model, and the introduction of Agent systems, finally through analysis the hybrid model of intrusion detection system, we can acquire its advantages.
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Vanjari, Prof S. P., Priyanka Rekhawar, Ketki Shinde, Sakshi Shinde, and Prajkta Shelke. "Fraud Apps Detection Using Sentiment Analysis and Spam Filtering." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1975–77. http://dx.doi.org/10.22214/ijraset.2023.49724.

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Abstract: In the mobile app industry, ranking fraud is the practice of engaging in dishonest or deceitful behavior with the intention of artificially boosting an App's position on a popularity list. In fact, ranking fraud by app developers is becoming more and more common. These practices include inflating their apps' sales or uploading fake app reviews. Although the significance of preventing ranking fraud has long been understood, little knowledge and research have been done in this field. In order to do this, we present a comprehensive analysis of fraud app detection using sentiment analysis and spam filtering in this study and suggest a system for detecting it in mobile apps.
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Bhaskar, Navaneeth, Priyanka Tupe-Waghmare, Shobha S. Nikam, and Rakhi Khedkar. "Computer-aided automated detection of kidney disease using supervised learning technique." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 5 (October 1, 2023): 5932. http://dx.doi.org/10.11591/ijece.v13i5.pp5932-5941.

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<p>In this paper, we propose an efficient home-based system for monitoring chronic kidney disease (CKD). As non-invasive disease identification approaches are gaining popularity nowadays, the proposed system is designed to detect kidney disease from saliva samples. Salivary diagnosis has advanced its popularity over the last few years due to the non-invasive sample collection technique. The use of salivary components to monitor and detect kidney disease is investigated through an experimental investigation. We measured the amount of urea in the saliva sample to detect CKD. Further, this article explains the use of predictive analysis using machine learning techniques and data analytics in remote healthcare management. The proposed health monitoring system classified the samples with an accuracy of 97.1%. With internet facilities available everywhere, this methodology can offer better healthcare services, with real-time decision support in remote monitoring platform.</p>
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Liang, Chao, Bharanidharan Shanmugam, Sami Azam, Asif Karim, Ashraful Islam, Mazdak Zamani, Sanaz Kavianpour, and Norbik Bashah Idris. "Intrusion Detection System for the Internet of Things Based on Blockchain and Multi-Agent Systems." Electronics 9, no. 7 (July 10, 2020): 1120. http://dx.doi.org/10.3390/electronics9071120.

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With the popularity of Internet of Things (IoT) technology, the security of the IoT network has become an important issue. Traditional intrusion detection systems have their limitations when applied to the IoT network due to resource constraints and the complexity. This research focusses on the design, implementation and testing of an intrusion detection system which uses a hybrid placement strategy based on a multi-agent system, blockchain and deep learning algorithms. The system consists of the following modules: data collection, data management, analysis, and response. The National security lab–knowledge discovery and data mining NSL-KDD dataset is used to test the system. The results demonstrate the efficiency of deep learning algorithms when detecting attacks from the transport layer. The experiment indicates that deep learning algorithms are suitable for intrusion detection in IoT network environment.
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Zhang, Huaizu, Chengbin Xia, Guangfu Feng, and Jun Fang. "Hospitals and Laboratories on Paper-Based Sensors: A Mini Review." Sensors 21, no. 18 (September 7, 2021): 5998. http://dx.doi.org/10.3390/s21185998.

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With characters of low cost, portability, easy disposal, and high accuracy, as well as bulky reduced laboratory equipment, paper-based sensors are getting increasing attention for reliable indoor/outdoor onsite detection with nonexpert operation. They have become powerful analysis tools in trace detection with ultra-low detection limits and extremely high accuracy, resulting in their great popularity in medical detection, environmental inspection, and other applications. Herein, we summarize and generalize the recently reported paper-based sensors based on their application for mechanics, biomolecules, food safety, and environmental inspection. Based on the biological, physical, and chemical analytes-sensitive electrical or optical signals, extensive detections of a large number of factors such as humidity, pressure, nucleic acid, protein, sugar, biomarkers, metal ions, and organic/inorganic chemical substances have been reported via paper-based sensors. Challenges faced by the current paper-based sensors from the fundamental problems and practical applications are subsequently analyzed; thus, the future directions of paper-based sensors are specified for their rapid handheld testing.
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Fang, Tianqi, Xuanyu He, and Lizhe Xu. "Pulmonary inflammation region detection algorithms based on deep learning: a review." Highlights in Science, Engineering and Technology 4 (July 26, 2022): 273–79. http://dx.doi.org/10.54097/hset.v4i.914.

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With the popularity and development of object detection in deep learning, it is used in more and more industries, including the field of medical science. This paper summarizes the target detection algorithms for the pneumonia region. Firstly, this paper briefly introduced the existing detection methods of target detection and summarized the main pneumonia datasets and image preprocessing methods. Then we focused on the framework composition and detection effect of the main model. Finally, through experimental analysis, we proposed to apply some new models to the detection of pneumonia and used some improvements and techniques to improve the detection effect.
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Cao, Gaofeng, Huan Zhang, Jianbo Zheng, Li Kuang, and Yu Duan. "An Outlier Degree Shilling Attack Detection Algorithm Based on Dynamic Feature Selection." International Journal of Software Engineering and Knowledge Engineering 29, no. 08 (August 2019): 1159–78. http://dx.doi.org/10.1142/s0218194019500360.

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Recommender system is widely used in various fields for dealing with information overload effectively, and collaborative filtering plays a vital role in the system. However, recommender system suffers from its vulnerabilities by malicious attacks significantly, especially, shilling attacks because of the open nature of recommender system and the dependence on data. Therefore, detecting shilling attack has become an important issue to ensure the security of recommender system. Most of the existing methods of detecting shilling attack are based on user ratings, and one limitation is that they are likely to be interfered by obfuscation techniques. Moreover, traditional detection algorithms cannot handle different types of shilling attacks flexibly. In order to solve the problems, we proposed an outlier degree shilling attack detection algorithm by using dynamic feature selection. Considering the differences when users choose items, we combined rating-based indicators with user popularity, and utilized the information entropy to select detection indicators dynamically. Therefore, a variety of shilling attack models can be dealt with flexibility in this way. The experiments show that the proposed algorithm can achieve better detection performance and interference immunity.
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Singh, Raman, Harish Kumar, Ravinder Kumar Singla, and Ramachandran Ramkumar Ketti. "Internet attacks and intrusion detection system." Online Information Review 41, no. 2 (April 10, 2017): 171–84. http://dx.doi.org/10.1108/oir-12-2015-0394.

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Purpose The paper addresses various cyber threats and their effects on the internet. A review of the literature on intrusion detection systems (IDSs) as a means of mitigating internet attacks is presented, and gaps in the research are identified. The purpose of this paper is to identify the limitations of the current research and presents future directions for intrusion/malware detection research. Design/methodology/approach The paper presents a review of the research literature on IDSs, prior to identifying research gaps and limitations and suggesting future directions. Findings The popularity of the internet makes it vulnerable against various cyber-attacks. Ongoing research on intrusion detection methods aims to overcome the limitations of earlier approaches to internet security. However, findings from the literature review indicate a number of different limitations of existing techniques: poor accuracy, high detection time, and low flexibility in detecting zero-day attacks. Originality/value This paper provides a review of major issues in intrusion detection approaches. On the basis of a systematic and detailed review of the literature, various research limitations are discovered. Clear and concise directions for future research are provided.
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Masand, Abhishek, Suryansh Chauhan, and Tarun Jain. "Depression Identification Through Tweet Clusters." International Journal of Software Innovation 10, no. 1 (January 2022): 1–14. http://dx.doi.org/10.4018/ijsi.297916.

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Over the past few years, the awareness and popularity of Mental health have been on a rapid rise and people are becoming more aware of the surrounding problems. This has helped for mental illnesses like Depression to become recognized and be treated appropriately. Social media has played an integral part in this uproar due to its increased popularity and ease of use. This has allowed people to spread awareness, seek help and vent out their emotions. Our paper is a comparative study of different models for detecting depression with real-time Twitter data and proposing the best performing model. For depression detection, a collection of tweets per user spread over time was used. The data was augmented and then passed through the deep learning model to identify depression in Twitter users based on their Time-Distributed tweets. The proposed model achieved an accuracy of over 90%.
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Wang, Benyou, and Li Gu. "Detection of Network Intrusion Threat Based on the Probabilistic Neural Network Model." Information Technology and Control 48, no. 4 (December 18, 2019): 618–25. http://dx.doi.org/10.5755/j01.itc.48.4.24036.

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With the popularity of the Internet, people's lives are becoming more and more convenient. However, the network security problems are becoming increasingly serious. This paper, aiming to better protect users’ network security from the internal and external malicious attacks, briefly introduces the probabilistic neural network and principal component analysis method, and combines them for detection of network intrusion data. Simulation analysis of Probabilistic Neural Network (PNN) and Principal Component Analysis-Probabilistic Neural Network (PCA-PNN) are carried out in MATLAB software. The results suggest that the Principal Component Analysis (PCA) algorithm greatly reduce the dimension of the original data and the amount of calculation. Compared with PNN, PCA-PNN has higher accuracy and precision rate, lower false alarm rate, and faster detecting speed. Moreover, PCA-PNN has better detecting performance when there are few training samples. In summary, PCA-PNN can be used for the detection of network intrusion threat.
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Revathy, S., and S. Sathya Priya. "Enhancing the Efficiency of Attack Detection System Using Feature selection and Feature Discretization Methods." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 4s (April 3, 2023): 156–60. http://dx.doi.org/10.17762/ijritcc.v11i4s.6322.

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Intrusion detection technologies have grown in popularity in recent years using machine learning. The variety of new security attacks are increasing, necessitating the development of effective and intelligent countermeasures. The existing intrusion detection system (IDS) uses Signature or Anomaly based detection systems with machine learning algorithms to detect malicious activities. The Signature-based detection rely only on signatures that have been pre-programmed into the systems, detect known attacks and cannot detect any new or unusual activity. The Anomaly based detection using supervised machine learning algorithm detects only known threats. To address this issue, the proposed model employs an unsupervised machine learning approach for detecting attacks. This approach combines the Sub Space Clustering and One Class Support Vector Machine algorithms and utilizes feature selection methods such as Chi-square, as well as Feature Discretization Methods like Equal Width Discretization to identify both known and undiscovered assaults. The results of the experiments using proposed model outperforms several of the existing system in terms of detection rate and accuracy and decrease in the computational time.
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Sharma, Divyeh. "Facial Landmark Detection with Sentiment Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3488–92. http://dx.doi.org/10.22214/ijraset.2022.44687.

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Abstract: Facial landmark detection is gaining more importance and popularity as its requirement and applications increase tremendously. Facial landmark detection has always been an exciting field of research cause of many potential applications encompassing facial recognition, sentiment recognition, and facial expression analysis. Innumerous datasets are extracted, cleaned, and explored for training Facial Landmark detection at various angles and setups, resulting in successful working in occult faces and multiple facial mapping in real-time. Facial Landmark detection, coupled with one of the robust applications of sentiment recognition, becomes an excellent tool for the user with the potential to add many applications further in the future.
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Amirul Asyraf Zhahir, Siti Munirah Mohd, Mohd Ilias M Shuhud, Bahari Idrus, Hishamuddin Zainuddin, Nurhidaya Mohamad Jan, and Mohamed Ridza Wahiddin. "Entanglement Detection: A Scoping Review." Journal of Advanced Research in Applied Sciences and Engineering Technology 42, no. 2 (April 3, 2024): 209–20. http://dx.doi.org/10.37934/araset.42.2.209220.

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Quantum entanglement is a critical physical process in quantum mechanics and quantum information theory. It is a required process in quantum computing, quantum teleportation, and quantum cryptography. Entanglement detection affects the performance of quantum information processing tasks. Entanglement detection has grown in popularity over the years, and various entanglement detection methods are available, though some have application and system scale limitations. This scoping review sought to identify various measurement methods for entanglement detection in both bipartite and multipartite entanglement systems. Secondary resource indexed literatures were selected based on specific keywords from literatures published between 2017 and 2021. The goal of this study is to present a proposed conceptual framework of entanglement detection based on previous work as a guidance and reference founded on one’s specific requirements.
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Muise, Christian. "Characterizing and Computing All Delete-Relaxed Dead-ends." Inteligencia Artificial 21, no. 62 (September 18, 2018): 67. http://dx.doi.org/10.4114/intartif.vol21iss62pp67-74.

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Dead-end detection is a key challenge in automated planning, and it is rapidly growing in popularity. Effective dead-end detection techniques can have a large impact on the strength of a planner, and so the effective computation of dead-ends is central to many planning approaches. One of the better understood techniques for detecting dead-ends is to focus on the delete relaxation of a planning problem, where dead-end detection is a polynomial-time operation. In this work, we provide a logical characterization for not just a single dead-end, but for every delete-relaxed dead-end in a planning problem. With a logical representation in hand, one could compile the representation into a form amenable to effective reasoning. We lay the ground-work for this larger vision and provide a preliminary evaluation to this end
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Bura, Deepa, Amit Choudhary, and Rakesh Kumar Singh. "A Novel UML Based Approach for Early Detection of Change Prone Classes." International Journal of Open Source Software and Processes 8, no. 3 (July 2017): 1–23. http://dx.doi.org/10.4018/ijossp.2017070101.

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This article describes how predicting change-prone classes is essential for effective development of software. Evaluating changes from one release of software to the next can enhance software quality. This article proposes an efficient novel-based approach for predicting changes early in the object-oriented software. Earlier researchers have calculated change prone classes using static characteristics such as source line of code e.g. added, deleted and modified. This research work proposes to use dynamic metrics such as execution duration, run time information, regularity, class dependency and popularity for predicting change prone classes. Execution duration and run time information are evaluated directly from the software. Class dependency is obtained from UML2.0 class and sequence diagrams. Regularity and popularity is acquired from frequent item set mining algorithms and an ABC algorithm. For classifying the class as change-prone or non-change-prone class an Interactive Dichotomizer version 3 (ID3) algorithm is used. Further validation of the results is done using two open source software, OpenClinic and OpenHospital.
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M, Senthil Raja, Arun Raj L, and Arun A. "Detection of Depression among Social Media Users with Machine Learning." Webology 19, no. 1 (January 20, 2022): 250–57. http://dx.doi.org/10.14704/web/v19i1/web19019.

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Mental illnesses are a significant and growing public health concern. They have the potential to tremendously affect a person’s life. Depression, in particular, is one of the major reasons for suicide. In recent times, the popularity of social media websites has burgeoned as they are platforms that facilitate discussion and free-flowing conversation about a plethora of topics. Information and dialogue about subjects like mental health, which are still considered as a taboo in various cultures, are becoming more and more accessible. The objective of this paper is to review and comprehensively compare various previously employed Natural Language Processing techniques for the purpose of classification of social media text posts as those written by depressed individuals. Furthermore, pros, cons, and evaluation metrics of these techniques, along with the challenges faced and future directions in this area of research are also summarized.
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Sharma, Sandeep, Prachi ., Rita Chhikara, and Kavita Khanna. "An efficient Android malware detection method using Borutashap algorithm." International Journal of Experimental Research and Review 34, Special Vo (October 30, 2023): 86–96. http://dx.doi.org/10.52756/ijerr.2023.v34spl.009.

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The Android operating system captures the largest global smartphone market share. However, its popularity and open-source nature have garnered the attention of cybercriminals. The landscape of Android malware has evolved significantly over time. Traditional techniques for detecting Android malware are encountering difficulties in keeping up with this evolution. Specifically, methods that rely on extracting various features from Android applications are becoming difficult to implement as high-dimensional feature sets incur huge computational overheads when employed with machine learning algorithms. Therefore, this research proposes using Bortua and BorutaShap feature selection algorithms to choose features that contribute to detecting malicious Android applications. It uses static and dynamic features of Android applications to create a detection model for verification and evaluation of the mentioned algorithms. Experimental results showed that Bortua and BorutaShap algorithms offer promising results by achieving the highest accuracy of approximately 99%.
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Mazri, Ammar, and Merouane Mehdi. "A NEW APPROACH TO DETECT P2P TRAFFIC BASED ON SIGNATURES ANALYSIS." RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218 5, no. 3 (March 6, 2024): e534994. http://dx.doi.org/10.47820/recima21.v5i3.4994.

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In recent years, peer-to-peer (P2P) networks have gained more popularity in the form of file-sharing applications, such as uTorrent and eMule, that use BitTorrent and eDonkey protocols. With such popularity comes security risks and external attacks; the latter is often associated with information hacking. In this paper, we will introduce a new way to monitor and detect the use of each of the P2P applications within the corporate network. Based on the inspection of traffic packets in order to extract digital signatures of these applications using the open-source packet analysis program "Wireshark," in addition to using the well-known Snort intrusion detection system (IDS) with a number of adequate and new rules, this solution can allow us to receive powerful warning messages that detect the presence of P2P applications inside the network. We implemented our rules in Snort IDS. Over a period of time, this solution allowed us to achieve 96% effectiveness in detecting the presence of P2P applications.
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Nashikkar, Siddharth. "Social Network Mental Disorders Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (September 30, 2023): 1683–92. http://dx.doi.org/10.22214/ijraset.2023.55901.

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Abstract: With the rise in popularity of online social networks (OSNs), new terms such as Phubbing and Nomophobia have been introduced, and Social network mental illnesses (SNMDs) such as Information Overload and Net Compulsion have been noted Studies show that 1 in 8 Americans experience it suffer from problems internet use, as well as SNMD Overuse, depression, social withdrawal, and other negative behaviors can occur if SNMDs are socially relevant and frequent for them interactive users through online social media. Internet addiction (IAD) is a behavioral disorder, and research on depression in online social networks is increasing unlike most previous attention-grabbing research emphasizes individual behaviors and entries but does not fully explore social network structures and psychological possibilities we propose a learning approach for those that require a thorough examination of OSN topologies
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Mitbavkar, Tejashri, Swarangi Pedamkar, Saloni Kuvalekar, and Prof Kumud Wasnik. "Fake Product Detection Using Blockchain." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 1879–85. http://dx.doi.org/10.22214/ijraset.2023.50409.

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Abstract: Many applications have been developed using blockchain in recent years as its popularity has grown. The cryptocurrency Bitcoin is a well-known example of a Blockchain application since it not only solves the issue of double spendingin an efficient manner but also has the ability to independently verify the veracity of transactional data. As a result, any application that uses Blockchain technology as its fundamental architecture guarantees the integrity of its data. Since Blockchain technology is decentralised, consumers do not fully depend on merchants to verify the authenticity of their purchases. We outline a decentralised Blockchain system with product anti- counterfeiting so that producers can utilise it to deliver authentic goods without having to oversee directly owned storefronts, whichcan significantly reduce the cost of product quality assurance.
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Rohankar, A. W., Shantanu Pathak, Mrinal K. Naskar, and Amitava Mukherjee. "Audio Streaming with Silence Detection Using 802.15.4 Radios." ISRN Sensor Networks 2012 (December 10, 2012): 1–5. http://dx.doi.org/10.5402/2012/590651.

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Short-range radios with low data rate are gaining popularity due to their abundant commercial availability. It is imperative that high-speed multimedia would be an attractive application field with these radios. Audio over 802.15.4 compliant radios is a challenging task to achieve. This paper describes a real-time implementation of audio communication using 802.15.4 radios. Silence detection and soft ADPCM are the main features of our work. Our results show that silence detection improves bandwidth optimization and audio communication performance over low bit-rate radios.
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Azrour, Mourade, Mohammed Ouanan, and Yousef Farhaoui. "Survey of Detection SIP Malformed Messages." Indonesian Journal of Electrical Engineering and Computer Science 7, no. 2 (August 1, 2017): 457. http://dx.doi.org/10.11591/ijeecs.v7.i2.pp457-465.

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Session Initiation Protocol (SIP) is an application layer protocol designed to control and establish multimedia sessions over internet. SIP gaining more and more popularity as it is used by numerous applications such as telephony over IP(ToIP). SIP is a text based protocol built on the base of the HTTP and SMTP protocols. SIP suffers from certain security threats which need to be resolved in order to make it a more efficient signaling protocol. In this work, we review the proposed works aimed to detect SIP malformed messages that can cause security problem. Then, we classify the type of malformed SIP message and compare between the mechanisms used to reinforce the detection of malformed SIP message attack.
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Timokhin, Stanislav, Mohammad Sadrani, and Constantinos Antoniou. "Predicting Venue Popularity Using Crowd-Sourced and Passive Sensor Data." Smart Cities 3, no. 3 (August 6, 2020): 818–41. http://dx.doi.org/10.3390/smartcities3030042.

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Efficient and reliable mobility pattern identification is essential for transport planning research. In order to infer mobility patterns, however, a large amount of spatiotemporal data is needed, which is not always available. Hence, location-based social networks (LBSNs) have received considerable attention as a potential data provider. The aim of this study is to investigate the possibility of using several different auxiliary information sources for venue popularity modeling and provide an alternative venue popularity measuring approach. Initially, data from widely used services, such as Google Maps, Yelp and OpenStreetMap (OSM), are used to model venue popularity. To estimate hourly venue occupancy, two different classes of model are used, including linear regression with lasso regularization and gradient boosted regression (GBR). The predictions are made based on venue-related parameters (e.g., rating, comments) and locational properties (e.g., stores, hotels, attractions). Results show that the prediction can be improved using GBR with a logarithmic transformation of the dependent variables. To investigate the quality of social media-based models by obtaining WiFi-based ground truth data, a microcontroller setup is developed to measure the actual number of people attending venues using WiFi presence detection, demonstrating that the similarity between the results of WiFi data collection and Google “Popular Times” is relatively promising.
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Zhang, Yuxing, Jinchen Song, Yuehan Jiang, and Hongjun Li. "Online Video Anomaly Detection." Sensors 23, no. 17 (August 26, 2023): 7442. http://dx.doi.org/10.3390/s23177442.

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With the popularity of video surveillance technology, people are paying more and more attention to how to detect abnormal states or events in videos in time. Therefore, real-time, automatic and accurate detection of abnormal events has become the main goal of video-based surveillance systems. To achieve this goal, many researchers have conducted in-depth research on online video anomaly detection. This paper presents the background of the research in this field and briefly explains the research methods of offline video anomaly detection. Then, we sort out and classify the research methods of online video anomaly detection and expound on the basic ideas and characteristics of each method. In addition, we summarize the datasets commonly used in online video anomaly detection and compare and analyze the performance of the current mainstream algorithms according to the evaluation criteria of each dataset. Finally, we summarize the future trends in the field of online video anomaly detection.
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Mohamed Elmahalwy, Amina, Hayam M. Mousa, and Khalid M. Amin. "New hybrid ensemble method for anomaly detection in data science." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 3 (June 1, 2023): 3498. http://dx.doi.org/10.11591/ijece.v13i3.pp3498-3508.

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Anomaly detection is a significant research area in data science. Anomaly detection is used to find unusual points or uncommon events in data streams. It is gaining popularity not only in the business world but also in different of other fields, such as cyber security, fraud detection for financial systems, and healthcare. Detecting anomalies could be useful to find new knowledge in the data. This study aims to build an effective model to protect the data from these anomalies. We propose a new hyper ensemble machine learning method that combines the predictions from two methodologies the outcomes of isolation forest-k-means and random forest using a voting majority. Several available datasets, including KDD Cup-99, Credit Card, Wisconsin Prognosis Breast Cancer (WPBC), Forest Cover, and Pima, were used to evaluate the proposed method. The experimental results exhibit that our proposed model gives the highest realization in terms of receiver operating characteristic performance, accuracy, precision, and recall. Our approach is more efficient in detecting anomalies than other approaches. The highest accuracy rate achieved is 99.9%, compared to accuracy without a voting method, which achieves 97%.
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Wahab, Abbas A., N. Fatimah Abdullah, and M. A. H. Rasid. "Mechanical Fault Detection on Electrical Machine: Thermal Analysis of Small Brushed DC Motor with Faulty Bearing." MATEC Web of Conferences 225 (2018): 05012. http://dx.doi.org/10.1051/matecconf/201822505012.

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Direct current motors (DC motor) are used in the small electric devices commonly. DC motor are cheap and easy to install, thus their popularity. Despite the popularity, faults occur which make diagnosis and detection of faults very important. It avoids financial loss and unexpected shutdown operation causes by these faults. This paper presents an analysis of temperature profile of the much famous small Brushed DC motor with a faulty bearing. The temperature data of healthy DC motor and DC motor with faulty bearing were measured by thermocouple and recorded using data logger in real time until steady state temperature, under different load. The analysis on the steady state temperature allow to conclude that bearing fault can clearly be recognised through characteristics temperature difference with a healthy motor.
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M. P, Milan. "CHALLENGES IN FACE RECOGNITION TECHNIQUE." Journal of University of Shanghai for Science and Technology 23, no. 07 (July 24, 2021): 1201–4. http://dx.doi.org/10.51201/jusst/21/07253.

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Face detection is an application that is able of detecting, track, and recognizing human faces from an angle or video captured by a camera. A lot of advances have been made up in the domain of face recognition for security, identification, and appearance purpose, but still, difficult to able to beat humans alike accuracy. There are various problems in human facial presence such as; lighting conditions, image noise, scale, presentation, etc. Unconstrained face detection remains a difficult problem due to intra-class variations acquired by occlusion, disguise, capricious orientations, facial expressions, age variations…etc. The detection rate of face recognition algorithms is actually low in these conditions. With the popularity of AI in recent years, a mass number of enterprises deployed AI algorithms in absolute life settings. it is complete that face patterns observed by robots depend generally on variations such as pose, light environment, location.
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Singh, Archana, and Rakesh Kumar. "Machine Learning and Deep Learning Approaches for detecting Alzheimer’s Disease (AD): A Review." International Journal of Engineering Research in Computer Science and Engineering 9, no. 7 (July 21, 2022): 63–68. http://dx.doi.org/10.36647/ijercse/09.07.art014.

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Alzheimer's disease (AD) is the most prevalent chronic disease among the elderly, with a high prevalence. In the treatment of AD, early diagnosis of the patient plays an important role because of severe damage to the brain later. Deep learning (DL) and Machine Learning (ML) have gained popularity and success in the field of medical imaging in recent years. It has become the dominant way of assessing medical pictures, and it has also sparked considerable interest in the diagnosis of Alzheimer's disease. The deep and machine models are more precise and efficient for AD detection than ordinary machine learning techniques. This study provides AD-related biomarkers and feature extraction methods, discusses the use of the machine and deep learning approaches in AD detection, and analyses and summarises AD detection methodologies and models. The results suggest that DL and ML technology performs well in detecting AD.
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Marazqah Btoush, Eyad Abdel Latif, Xujuan Zhou, Raj Gururajan, Ka Ching Chan, Rohan Genrich, and Prema Sankaran. "A systematic review of literature on credit card cyber fraud detection using machine and deep learning." PeerJ Computer Science 9 (April 17, 2023): e1278. http://dx.doi.org/10.7717/peerj-cs.1278.

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The increasing spread of cyberattacks and crimes makes cyber security a top priority in the banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional anomaly detection and rule-based techniques are two of the most common utilized approaches for detecting cyber fraud, however, they are the most time-consuming, resource-intensive, and inaccurate. Machine learning is one of the techniques gaining popularity and playing a significant role in this field. This study examines and synthesizes previous studies on the credit card cyber fraud detection. This review focuses specifically on exploring machine learning/deep learning approaches. In our review, we identified 181 research articles, published from 2019 to 2021. For the benefit of researchers, review of machine learning/deep learning techniques and their relevance in credit card cyber fraud detection is presented. Our review provides direction for choosing the most suitable techniques. This review also discusses the major problems, gaps, and limits in detecting cyber fraud in credit card and recommend research directions for the future. This comprehensive review enables researchers and banking industry to conduct innovation projects for cyber fraud detection.
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