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Computing, sciences and Communications (ICCSC), International Conference on

作者： Kavya Gupta Anurag Gupta Abha Kiran Raipoot Department ofCSE(AI&ML) KIET Group of Institutions Ghaziabad India Department of CSE(AI) KIET Group of Institutions Ghaziabad India School of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

With the widespread integration of machine learning (ml) into diverse applications, it has become paramount to scrutinize its implications for security and privacy. Despite the burgeoning body of work in privacy, there is a noticeable gap in research attention between the security and privacy aspects of ml, specifically supervised machine learning (S-ml). This paper bridges this gap by analyzing research papers focusing on privacy attacks against S-ml over the past years. This study offers a comprehensive overview of prevalent defense mechanisms and initiates a thoughtful discussion on unresolved challenges and prospective research directions. As ml continues to permeate various domains, the scientific community's interest has expanded beyond performance metrics to encompass broader concerns such as security, privacy, fairness, and explainability. This survey investigates state-of-the-art privacy-related attacks, elucidating common design patterns and distinctive characteristics. Through this exploration, we identify several open problems that demand further investigation, emphasizing the critical need to fortify privacy frameworks alongside the escalating ubiquity of ml technologies.

关键词： Surveys Measurement Privacy Data privacy Machine learning algorithms Quantum computing Organizations Machine learning Robustness Security

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Transfer Learning-based Emotion Detection System in Cultivating Workplace Harmony 20

Transfer Learning-based Emotion Detection System in Cultivat...

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20th CSI International Symposium on Artificial Intelligence and Signal Processing, aiSP 2024

作者： Shah, Premal P. Jadav, Nilesh Kumar Makwana, Vatsalkumar Trivedi, Harshal Bhatia, Jitendra Tanwar, Sudeep Shahinzadeh, Hossein Gujarat Technological University Adani Institute of Infrastructure Engineering Ahmedabad India Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad382481 India L. J. Institute of Engineering and Technology Department of Computer Engineering Ahmedabad India CEO Tushker AI Ahmedabad India Amirkabir University of Technology Department of Electrical Engineering Tehran Iran

ISBN: (纸本)9798350383942

The technological advancements made the organizations achieve more business profits and attain new horizons that significantly impact on employee stress and mental-well well-being. Moreover, due to the advent of Artificial intelligence (ai), which offers automotive tasks and efficiency gains, the employee get fear of losing their jobs. Due to this, the employee suffers from excessive stress and mental health issues. Therefore, emotion detection is imperative in such a stressful workplace environment. Inspired by this, we proposed a transfer learning-based emotion detection system to improve the workplace environment. For that, a facial emotion dataset is utilized, which comprises grayscale images of employee faces having emotions such as fear, neutral, anger, sadness, happiness, surprise, and disgust. Then, we used transfer learning-based pre-trained models with the intention of reducing the computational overhead of ai training. We employed ResNet, Inception, MobileNet, and EfficientNet which offer an effective accuracy while detecting the emotions of the employee. This strategic use of pre-existing models not only optimizes efficiency but also enhances the overall effectiveness of our facial emotion analysis system, ensuring a robust and accurate representation of diverse emotional states in employees. From the result analysis, we found that the ResNet outperforms other existing pre-trained models in terms of training accuracy (95.23%), training loss (0.41), and training time (2803 sec). © 2024 IEEE.

关键词： Learning systems

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Graph-Based Machine Learning Approaches for Fraud Detection in Financial Networks 7

Graph-Based Machine Learning Approaches for Fraud Detection ...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kotiyal, Arnav Hussein, Layth Deepak, A. Rana, Aryan Manjunatha Dixit, Krishna Kant Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic University College of technical engineering Department of computers Techniques engineering Najaf Iraq Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India Amity University Amity School of Engineering and Technology Uttar Pradesh Greater Noida India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Gla University Department of Electrical Engineering Mathura India Mlr Institute of Technology Department of CSE-AI & Ml Telangana Hyderabad India

ISBN: (纸本)9798350350067

Fraud detection in financial networks presents a significant challenge due to the complexity and volume of transactions. Traditional detection methods often struggle with scalability and accuracy when faced with evolving fraudulent schemes. In this paper, we explore the application of graph-based machine learning techniques to enhance fraud detection in financial networks. By representing financial transactions as graph structures, we capture the relationships between entities, allowing for the identification of suspicious patterns that may indicate fraudulent behavior. We examine several graph-based algorithms, including Graph Neural Networks (GNNs) and Random Walk-based approaches, and compare their performance with conventional machine learning methods. The use of graph representations facilitates the extraction of features such as transaction flows and network centrality, which are critical in identifying anomalies. Furthermore, we propose an enhanced detection model that integrates community detection techniques with supervised learning for improved accuracy. Our experimental results, conducted on a real-world financial dataset, demonstrate that graph-based models outperform traditional models in detecting fraud with higher precision and recall. The study highlights the potential of these techniques to provide scalable and robust solutions for real-time fraud detection, contributing to a more secure financial ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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Drone-Based Public Surveillance Using 3D Point Clouds and Neuro-Fuzzy Classifier

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computers, Materials & Continua 2025年第3期82卷 4759-4776页

作者： Yawar Abbas aisha Ahmed Alarfaj Ebtisam Abdullah Alabdulqader Asaad Algarni Ahmad Jalal Hui Liu Faculty of Computing and AI Air UniversityIslamabad44000Pakistan Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Department of Computer Science and Engineering College of InformaticsKorea UniversitySeoul02841Republic of Korea Cognitive Systems Lab University of BremenBremen28359Germany

Human Activity Recognition(HAR)in drone-captured videos has become popular because of the interest in various fields such as video surveillance,sports analysis,and human-robot ***,recognizing actions from such videos poses the following challenges:variations of human motion,the complexity of backdrops,motion blurs,occlusions,and restricted camera *** research presents a human activity recognition system to address these challenges by working with drones’red-green-blue(RGB)*** first step in the proposed system involves partitioning videos into frames and then using bilateral filtering to improve the quality of object foregrounds while reducing background interference before converting from RGB to grayscale *** YOLO(You Only Look Once)algorithm detects and extracts humans from each frame,obtaining their skeletons for further *** joint angles,displacement and velocity,histogram of oriented gradients(HOG),3D points,and geodesic Distance are *** features are optimized using Quadratic Discriminant Analysis(QDA)and utilized in a Neuro-Fuzzy Classifier(NFC)for activity ***-world evaluations on the Drone-Action,Unmanned Aerial Vehicle(UAV)-Gesture,and Okutama-Action datasets substantiate the proposed system’s superiority in accuracy rates over existing *** particular,the system obtains recognition rates of 93%for drone action,97%for UAV gestures,and 81%for Okutama-action,demonstrating the system’s reliability and ability to learn human activity from drone videos.

关键词： Activity recognition geodesic distance pattern recognition neuro fuzzy classifier

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Boli: A dataset for understanding stuttering experience and analyzing stuttered speech

Boli: A dataset for understanding stuttering experience and ...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Batra, Ashita Narang, Mannas Sharma, Neeraj Kumar Das, Pradip K. Department of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati781039 India Mehta Family School of Data Science and AI Indian Institute of Technology Guwahati Guwahati781039 India

ISBN: (纸本)9798350368741

There is a growing need for diverse, high-quality stuttered speech data, particularly in the context of Indian languages. This paper introduces Project Boli, a multi-lingual stuttered speech dataset designed to advance scientific understanding and technology development for individuals who stutter, particularly in India. The dataset constitutes (a) anonymized metadata (gender, age, country, mother tongue) and responses to a questionnaire about how stuttering affects their daily lives, (b) captures both read speech (using the Rainbow Passage) and spontaneous speech (through image description tasks) for each participant and (c) includes detailed annotations of five stutter types: blocks, prolongations, interjections, sound repetitions and word repetitions. We present a comprehensive analysis of the dataset, including the data collection procedure, experience summarization of people who stutter, severity assessment of stuttering events and technical validation of the collected data. The dataset is released as an open access to further speech technology development. © 2025 IEEE.

关键词： Indian stuttered speech dataset Intelligibility assessment read speech spontaneous speech Stuttering event detection

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air Quality Control using Machine Learning

Air Quality Control using Machine Learning

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. RamaKrishna Reddy D. Vidya Shree N. Vasu N. Muthukumaran K. Saketh Patel N. Vamshi Karthik Rao Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science Nalgonda Telangana Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

In recent times, the world has witnessed remarkable advancements driven by various technologies such as advanced computing, self-driving vehicles, and robot-assisted surgeries, among others. However, these technological strides have come with a significant environmental cost, contributing to the pollution of vital natural resources such as air and water. The contamination of air and water worsens health issues, particularly for individuals with respiratory ailments such as asthma and lung infections, posing risks to the overall population’s well-being. This research study has carried out a qualitative research and presents the challenges that demand careful navigation and interpretation in the near future.

关键词： Support vector machines Atmospheric modeling Data visualization Surgery Robot sensing systems Water pollution Decision trees

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Plant Leaf Disease Detection and Classification Using MobileNetV3 and Wavelet Kernel Extreme Learning Machine 3

Plant Leaf Disease Detection and Classification Using Mobile...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Shruthi, B.S. Narasimha Murthy, M.S. Prakash, Chidurala Sai Riad Al-Fatlawy, Ramy Sundaram, S. Meenakshi Malnad College Of Engineering Department Of Information Science Hassan India Bms Institute Of Technology And Management Department Of Computer Science And Engineering Bangalore India Sr University Department Of Electrical And Electronics Engineering Warangal India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Ai & Ds Bengaluru India

ISBN: (纸本)9798350318609

Detection of plant disease is the most important aspect in agriculture, aimed at identifying and diagnosing disease that affect plants. If the plant disease detected earlier, that helps to avoid risk from damaging plants and the soil. However, there is a difficulty to differentiate the diseased and healthy leaves due to similarity of disease symptoms. To solve this problem, an effective Wavelet Kernel Extreme Learning Machine (WKELM) classifier is proposed for distinguishing diseased and healthy plants. Images from the PlantVillage dataset are preprocessed using Bilateral filter and then segmented by Adaptive Gaussian Threshold (AGT) to separate the diseased portion from leaf. MobileNetV3 a Convolutional Neural Network (CNN) used for effective feature extraction with the hyper parameters which are tuned using effective Emperor Penguin Optimizer (EPO) and classification is done by the WKELM for accurate results of plant disease. The experimental results of the proposed model performed by the evaluation metrics achieved accuracy of 99.7%, recall of 99.6%, precision of 99.6%, F1-score of 99.7% which is greater than other existing deep learning models like CNN, Transfer learning with Deep CNN models, CNN - Visual Geometry Group (VGG). © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient Cell Segmentation in Microscopy Images using Sub-Sampling Integrated SVM 2

Efficient Cell Segmentation in Microscopy Images using Sub-S...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Ramprasath, J. Kumar, M. Arun Krishnaraj, N. Vignesh, C. Chandru Saranya, N. Ramprakash, P. Dr. Mahalingam College of Engineering and Technology Department of Information Technology Pollachi India Sri Krishna College of Technology Department of Electronics and Communication Engineering Coimbatore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Sri Krishna College of Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Technology Department of AI&DS Coimbatore India

ISBN: (纸本)9798331534950

Support Vector Machine (SVM) integrated with subsampling methods provides the latest approach to cell segmentation of microscopic images. This approach aims to increase computing efficiency and also maintain segmentation accuracy. To begin with, the processing load associated with large-scale microscopic images is reduced by employing subsampling techniques. On applying Support Vector Machine (SVM), classification of the picture patches into cell and non-cell portions based on the gathered attributes. The produced method is highly valuable for high-throughput analysis in medical and biological diagnostics as it provides competitive segmentation performance while drastically cutting down time for its process. Through a detailed analysis from rigorous testing and comparison with existing methods, efficiency and efficacy are demonstrated in accurately differentiating cells from microscopic images and extending solutions for vast biomedical applications. © 2024 IEEE.

关键词： Image segmentation

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Digital Evidence and Forensic Tools in Cyber Law: A Comprehensive Analysis 25

Digital Evidence and Forensic Tools in Cyber Law: A Comprehe...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Sherideh, Ala'A Saeb Al-Zou'bi, Samah Suleiman Alshraiedeh, Fuad Elsoud, Esraa Abu Afaneh, Suha Al-Mousa, Mohammad Rasmi Alazaidah, Raed Khouj, Mohammed Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Software Engineering Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The exponential rise in hacking, phishing, information theft, and electronic crime has become an actual concern with the increased use of digital devices and technology. By 2023 the number of IoT devices expectation will be 32.1 billion devices, leading to a significant increase in cybercrimes. These illegal behaviors are specifically connected to computers and communication technology. Consequently, there has been an urgent need for the development of cyber law which is a legal system closely related to digital evidence. This paper aim to provides delve into the significance and multifaceted applications of digital evidence within the context of cyber law, shedding light on the complexities and nuances inherent in its acceptance and reliability. The paper explores the importance and numerous uses of digital evidence in the context of cyber law, highlighting the challenges associated with its legitimacy and acceptance. Additionally, it focuses on the challenges that prevent digital evidence from being seamlessly incorporated into court processes and offers solutions and strategic advice to overcome these challenges. © 2024 IEEE.

关键词： Phishing

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Demystifying Cyber terrorism: Causes, Costs & its impact on individuals' psyche and public confidence

Demystifying Cyber terrorism: Causes, Costs & its impact on ...

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2023 International Conference on Recent Advances in Electrical, Electronics and Digital Healthcare Technologies, REEDCON 2023

作者： Zahra, Syed Rameem Saleem, Tausifa Jan Sonia Netaji Subhas University of Technology Department of Computer Science and Engineering Delhi India Indian Institute of Technology Delhi Department of Electrical Engineering Delhi India Shoolini University Yogananda School of Ai Computer and Data Science H.P Solan India

ISBN: (纸本)9781665493826

Just like the game for Information Technology (IT) security was turned topsy-turvy by the arrival of mobile devices a few decades ago, the Internet of Things (IoT) today is changing it again a big time. Data breaches, loss of productivity and profitability were quite a commonplace when mobile security became an issue. Today as IoT tries to connect everything to the internet, it intensifies the problem (more privacy violations, phishing/skimming attacks, security incidents) by creating a vast attack surface for the rogue players implying that important national (ports, airports, bridges, power-pants etc.) and cyber infrastructure could now be attacked. Moreover, if the network of your company has a weak point, it can easily be compromised to set off an attack. These Cyber terrorism attacks instigate identical responses from individuals as those for conventional terrorism attacks e.g. they heighten anxiety and stress, worsen feelings of vulnerability and harden the political attitudes of people. But this dimension is often overlooked by the policy makers. However, the increase in cyber terrorism cases has increased the research interest in the domain. Researchers are focusing on improving the security of internet and IoT devices. The field is still in its infancy. A lot needs to be done to mitigate these attacks. Motivated to demystify the repercussions of the cyber terrorism, this paper analyzes the distinctive characteristics of the most significant cyberattacks, investigates the expenditures incurred by the industries as a result of these cyber events, and then examines the impact of these attacks on the psychological health of the populace and the trust in public institutions. © 2023 IEEE.

关键词： Internet of things

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