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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Adnan, Myasar Mundher Devi, S. Vaag Deepthi, V.S. Raghunathareddy, M.V. Yamsani, Nagendar The Islamic University Najaf Iraq City Engineering College Department of Artificial Intelligence and Machine Learning Karnataka Bangalore India Cyber Security Dayananda Sagar College of Engineering Department of Computer Science and Engineering Karnataka Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India SR University School of Computer Science and Artificial Intelligence Warangal India

ISBN: (纸本)9798350317558

The ransomware exploits the system data and takes off the significant information of the user without any intimation. Moreover, the ransomware furtively directs that information to the servers which are organized by the attackers. In recent years, many researchers and scientists discovered anti-malware products to identify known malware. But these methods are not robust to detect complicated and packed malware. To overcome these problems, the Sine Cosine Reptile Search Algorithm with Grid Search Support Vector machine (SCRSA-GSSVM) is proposed for ransomware detection and classification. The Drebin dataset is employed in this paper and min-max standardization is utilized aimed at preprocessing. The SCRSA is utilized for feature selection and GSSVM is utilized for classification. The SVM is tuned by GS which reduces the noise and false positives to enhance the model performance. Performance of SCRSA-GSSVM is assessed with presentation measure of accuracy, precision, recall and f1-score. The SCRSA-GSSVM attains 99.85% accuracy, 99.83% precision, 99.83% recall and 99.78% f1-score which is better when compared to Random Forest (RF) and artificial Neural Network (ANN). © 2024 IEEE.

关键词： +Support Vector machine Grid Search Min-max Normalization Ransomware Reptile Search Algorithm

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machine learning Approach: Consumer Buying Behavior Analysis

Machine Learning Approach: Consumer Buying Behavior Analysis

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2022 IEEE Pune Section International Conference, PuneCon 2022

作者： Sharma, Anjali Pratap, Aradhana Vyas, Kishan Mishra, Sashikala Symbiosis Institute Technology Of Artificial Intelligence and Machine Learning Pune India University Department of Computer Science and Engineering Pune India

ISBN: (纸本)9781665498975

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

关键词： K-means clustering

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One-Shot Vision, AI for Effortless Identification of Individuals with and Without Glasses 5

One-Shot Vision, AI for Effortless Identification of Individ...

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5th IEEE Global Conference for Advancement in technology, GCAT 2024

作者： Tejesh, Gangadhar Karchi, Rashmi Hatture, Sanjeevakumar Sankanagoudra, Asharani Saini, Rashmi Nagarjuna College of Engineering and Technology Department of Artificial Intelligence and Machine Learning Karnataka Bengaluru India Nagarjuna College of Engineering and Technology Department of Information Science and Engineering Karnataka Bengaluru India G. B. Pant Institute of Engineering and Technology Department of Computer Science Uttarakhand Pauri Garhwal246194 India

ISBN: (纸本)9798350376685

Facial recognition is a challenging task, especially in the wild where there exist a substantial amount of variation in lighting, pose, and expression. One-shot knowledge acquisition is a form of machine learning that allows a model to learn to recognize a new class Upon observing just a single instance of that class. the proposed work of a one-shot learning strategy for facial recognition system that is specifically designed to detect persons with or without spectacles. The proposed work approach has the capability to achieve high accuracy on a limited dataset of labeled images, and possesses the capacity to adjust to unfamiliar images that were not encountered in prior training stages. In this work, One-shot learning aims to recognize individuals based on a limited number of examples with and without specs, making it suitable for scenarios where acquiring a large dataset is challenging. © 2024 IEEE.

关键词： Zero-shot learning

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Passive RFID Encryption and Decryption using CLI 7

Passive RFID Encryption and Decryption using CLI

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Suhas Kumar, S.J. Satyaa, Sudarshan Vishwamalyan, J.S. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350341485

The implementation of the concept of RFID encryption to make a system that prevents the attack on the data of any user or organization on the cloud. The built application has a command line interface. The command line interface is used here instead of a web server to prevent data leakage during data transmission and reduce the risk of a rogue web server. The command line interface takes in an input image and encrypts it, producing an encrypted image and a key file. The CLI also decrypts the previously encrypted image using the key file. This app as a whole solves the problem of users and organizations that store images on the cloud, using this app the user can store encrypted images on the web while the low storage key file is on the user/organization's on-site server. This provides a 2-tier security mechanism via which an adversary will need to hack both the organization server and the cloud server to compromise the data, this way the image data is secure without any additional cost or overhead to the organization. an RFID encryption algorithm is developed along with a non-network-based CLI that secures the organization's RFID data and helps implement a secure cloud-bursting architecture. In this project, an algorithm is developed by combining both the XOR-based encryption algorithm and non-network-based CLI to secure the RFID of the user/organization. Most encryption algorithms nowadays use web-based servers to encrypt the RFID and produce the key file. This makes web-based encryption insecure and vulnerable to a man-in-the-middle attack. Therefore, this app comes to the rescue as it provides a non-network-based CLI by which there is no need for the actual RFID to go outside of the local machine, thus making this app and its architecture secure and robust. © 2023 IEEE.

关键词： Radio frequency identification (RFID)

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Smart Attendance Management using a Self-Supervised learning Approach 5

Smart Attendance Management using a Self-Supervised Learning...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Vikram, D. Ajina, H. Gokilapriya, S. Malini, M. Rathinam Technical Campus Department of Computer Science and Engineering Coimbatore India Rathinam Technical Campus Department of Artificial Intelligence and Data Science Coimbatore India Kalingarkarunanidhi Institute of Technology Department of Artificial Intelligence and Machine Learning Coimbatore India Sri G.V.G Visalakshi College for Women Department of Information Technology Udumalpet India

ISBN: (纸本)9798350367171

Organizational efficiency is significantly influenced by automated attendance management systems, yet traditional methods often lack flexibility and reliability. This study proposes a novel approach to transform the Smart Attendance Management System (SAMS) by leveraging recent advances in Self-Supervised learning (SSL). The proposed method enhances accuracy and adaptability without explicit supervision by integrating SSL techniques at multiple stages, including feature extraction, anomaly detection, and data preparation. The proposed system learns meaningful representations from unlabeled attendance data through SSL-based data preprocessing, thereby improving the performance of downstream tasks. Feature extraction by utilizing SSL enables the proposed system to extract robust features enhancing both anomaly detection and attendance tracking. Anomaly detection models trained with SSL frameworks can effectively identify unusual attendance patterns. Additionally, SSL-based continuous learning ensures that the proposed system can adapt to the changing user behaviors and various situations. Supported by SSL, the proposed SAMS offers a more precise, efficient, and flexible solution for managing attendance, positioning it to meet the evolving demands of contemporary businesses across various industries. © 2024 IEEE.

关键词： Self-supervised learning

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Solar Power Plant Capacity Monitoring Using Random Forest machine learning Algorithm

Solar Power Plant Capacity Monitoring Using Random Forest Ma...

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IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

作者： Emmanuel Ede Ogar Surachai Chaitusaney Watit Benjapolakul Department of Electrical Engineering Faculty of Engineering Center of Excellence in Artificial Intelligence Machine Learning and Smart Grid Technology Chulalongkorn University Bangkok Thailand

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

The natural abundance of solar energy and its favorable environmental impacts have led to growing acceptance and utilization as the world transitions from a fossil fuel-based economy to a cleaner, more sustainable, and ecologically friendly alternative. An increasing number of solar photovoltaic projects have emerged to capitalize on these benefits. However, factors such as weather conditions, exposure to natural elements, device deterioration, improper installations, and maintenance concerns can eventually compromise these systems' power-generating performance and dependability over time. This study demonstrates how the random forest machine learning classifier can be used to monitor and categorize the performance of solar PV plants to ensure their reliability and efficiency. The study collected and preprocessed a field dataset containing various environmental and electrical parameters. These parameters include plant capacity, active power, generating capacity, ambient temperature, global radiation, and module temperature. This data was used to train a random forest machine learning algorithm to categorize the operational condition of the solar power plant. The results demonstrate that the model efficiently classified the operational condition of the solar power plant by effectively identifying conditions such as optimal mode, moderate sub-optimal mode, severe sub-optimal mode, critical mode, emergency mode, and offline mode, achieving 99.92% accuracy on the test set. The proposed method provides an effective technique for estimating and monitoring the generating capacity and operational condition of solar power plants in near real time, thereby serving as a distinctive instrument for boosting the efficiency and reliability of solar power generation.

关键词： Temperature sensors Temperature measurement Performance evaluation machine learning algorithms Accuracy Solar energy Real-time systems Classification algorithms Monitoring Random forests

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational intelligence and Computing Applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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Video Chat using WebRTC with In-built Sign Language Detection 3

Video Chat using WebRTC with In-built Sign Language Detectio...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Sailesh Kumar, K.L. Srivatsan, M. Sendhan, A. Saran, J.U. Vidhya Sagar, P.V. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798350306989

Many research efforts into video conferencing technology have focused on audio-only interactions. Text chat, on the other hand, has many uses and is often used in conjunction with the audio channel in multimedia conferencing systems because of the many benefits it offers to users in a variety of contexts. Video chat on the other hand has been providing participants to participate not only through voice but also through visual clues, creating a richer and more immersive communication experience. With the development of technology, video chat has transformed how people cooperate and communicate across distances by becoming more accessible and user-friendly. This study details a prototype program that uses video chat with in-built sign language identification to help the hearing-impaired and the speech-impaired. The prototype is constructed from WebRTC to provide realtime communication for the application, on top of which the object detection model from Tensor Flow is integrated to provide hand sign detection and recognition for the user. We focus our testing on the bandwidth and sign language detection needs of customers in order to provide improved support for video chat. We then talk about how this evaluation's findings can inform the evolution of video chatting software. © 2023 IEEE.

关键词： Object detection

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Facial Expression Recognition Model Depending on Optimized Support Vector machine

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Computers, Materials & Continua 2023年第7期76卷 499-515页

作者： Amel Ali Alhussan Fatma M.Talaat El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Mona Alnaggar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Machine Learning&Information Retrieval Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Robotics and Intelligent Machines Department Faculty of Artificial IntelligenceKafrelsheikh UniversityKafrelsheikh33516Egypt

In computer vision,emotion recognition using facial expression images is considered an important research *** learning advances in recent years have aided in attaining improved results in this *** to recent studies,multiple facial expressions may be included in facial photographs representing a particular type of *** is feasible and useful to convert face photos into collections of visual words and carry out global expression *** main contribution of this paper is to propose a facial expression recognitionmodel(FERM)depending on an optimized Support Vector machine(SVM).To test the performance of the proposed model(FERM),AffectNet is *** uses 1250 emotion-related keywords in six different languages to search three major search engines and get over 1,000,000 facial photos *** FERM is composed of three main phases:(i)the Data preparation phase,(ii)Applying grid search for optimization,and(iii)the categorization *** discriminant analysis(LDA)is used to categorize the data into eight labels(neutral,happy,sad,surprised,fear,disgust,angry,and contempt).Due to using LDA,the performance of categorization via SVM has been obviously *** search is used to find the optimal values for hyperparameters of SVM(C and gamma).The proposed optimized SVM algorithm has achieved an accuracy of 99%and a 98%F1 score.

关键词： Facial expression recognition machine learning linear dis-criminant analysis(LDA) support vector machine(SVM) grid search

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Performance Assessment of Supervised machine learning-Based Approaches for Photovoltaic Diagnosis

Performance Assessment of Supervised Machine Learning-Based ...

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International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information technology, ECTI-CON

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

There has been a notable increase in the adoption of solar energy as a sustainable energy choice in recent years due to its positive environmental impact, and widespread availability. However, factors such as weather, component deterioration, improper installation, and maintenance concerns gradually create conditions in the system that threaten its performance and reliability. This study provides a performance evaluation of different supervised machine learning methods for diagnosing photovoltaic cells. Solar power plant datasets containing environmental and electrical parameters were collected, pre-processed, and used to train various machine learning models, including Decision Tree, Long-Short Term Memory, Multilayer Perceptron, Temporal Convolutional Network, Convolutional Neural Network, Naïve Bayes, Support Vector machine, K-Nearest Neighbor, Random Forest, and Linear Discriminant Analysis algorithms. The models are evaluated using accuracy, precision, recall, f1-score, and confusion matrix metrics. Results demonstrated that the models diagnosed short circuit, abnormal voltage, partial shading, and hotspot faults with varying degrees of accuracy.

关键词： Support vector machines Photovoltaic systems Accuracy Photovoltaic cells Neural networks Voltage Convolutional neural networks

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