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Smart Class on Android: A Handy Classroom Scheduling Solution

Smart Class on Android: A Handy Classroom Scheduling Solutio...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Vishnuraja Palanisamy M. Thiyagarajan r. Bhavani Arun Anthonisamy K. Manivannan S. Vaishnavi Department of Artificial Intelligence and Data Science Builders Engineering College Kangeyam Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy Tamil Nadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamil Nadu India Department of Information Science and Engineering School of Computer Science & Engineering JAIN (Deemed-to-be) University Bengaluru Karnataka India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education (MAHE) Manipal India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

Classroom scheduling is vital but difficult for educational institutions, especially those with large student populations and diverse needs. Traditional methods using outdated software or manual processes often cause inefficiencies, disagreements, and administrative workload. In low-resource regions without access to cutting-edge technology and infrastructure, these issues are severe. This paper introduces Android-based SmartClass Mobile to transform classroom scheduling. The program's SOA-based functionality offers flexibility, adaptability, and integration with institution-specific procedures. The Heuristic algorithm and Solution Space Navigation (SSN) tackle scheduling problems such as room availability, multimedia needs, and seating capacities. SSN dynamically resolves conflicts by exploring alternate configurations, while the Heuristic Algorithm assigns time slots and classrooms depending on availability and priority. SmartClass Mobile analysis indicates benefits. The solution reduces conflicts, increases user satisfaction, and decreases scheduling time by 50% compared to manual methods with 85% test user approval. due to its mobile-first approach, the initiative is open to urban and rural institutions. dependence on internet access for some functions is a downside, especially in resource-constrained contexts. The findings suggest that SmartClass Mobile provides a scalable, effective, and simple classroom scheduling solution. Mobile technology, advanced algorithms, and SOA concepts make it valuable for school administrators. Additions like AI and machine learning will boost its potential.

关键词： Navigation Heuristic algorithms Software algorithms Manuals Transforms Scheduling Software real-time systems Mobile applications reliability

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Customized Billboard Advertisement through Age and Gender Identification

Customized Billboard Advertisement through Age and Gender Id...

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distributed Systems, Computer Networks and Cybersecurity (ICdSCNC), International Conference on

作者： Shanmugasundaram Hariharan Sreenivasulu Gogula Vinay Kukreja r. d. S Terese Sheeba V. Perumal Jose P Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Centre for Research Impact & Outcome Chitkara University Rajpura India Department of Electronics and Communication Engineering DMI Engineering College Chennai India Dept. of Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSd, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the advertising screen, the system is able to identify the age and gender of passersby based on the faces that are detected and processed by trained models. Customers can personalize adverts to certain groups by accessing the classification results via a website, which is accessed and stored in real-time on a database server. The system has a motion sensor for energy economy and runs continuously, updating every two seconds. With 91.7% and 95.5% accuracy rates for age and gender classification, respectively, the technology guarantees accurate content delivery. It also has the capacity to show news and guidance in crowded places, thereby fostering societal awareness and knowledge. The work well analyzes the gender detection using conventional methods and reveled it significance.

关键词： deep learning Adaptation models Accuracy Webcams Biological system modeling display systems real-time systems Motion detection Servers Faces

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Convolutional Neural Networks for Enhanced real-Time Traffic Incident detection and Image Classification

Convolutional Neural Networks for Enhanced Real-Time Traffic...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Monisha J L. ramalingam S. Mathankumar Narayanasamy P M. rajmohan S. Murugan Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Loyola Institute of Technology Chennai Tamil Nadu India Department of Biomedical Engineering Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electronics and Communication Engineering Hindustan Institute of Technology and Science Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Traffic incident detection and classification are essential aspects of enhancing road safety and efficiency. A new approach to enhancing real-time traffic incident detection and image classification with the help of Convolutional Neural Networks (CNNs), viz., Alex Net, is presented in this paper. Alex Net, a deep CNN architecture that is widely used for image recognition, is employed to classify traffic images and videos for incident identification. The system makes use of the ability of the model to learn robust features to identify images as different events, such as accidents, roadblocks, and vehicle breakdowns. Low latency and high accuracy incident identification is done by the system utilizing real-time traffic camera data and Alex Net. differentiation between different types of events. This is for the sake of enhancing reaction time, enabling effective traffic management, and differentiating between events. AlexNet enables 92% accuracy in real-time traffic detection and image classification, enhancing the efficiency of intelligent transportation systems. The results demonstrate how deep learning (dL) methods can revolutionise traffic monitoring systems and improve safety protocols for drivers.

关键词： deep learning Accuracy Electric breakdown real-time systems data models Safety Convolutional neural networks Image classification Accidents Videos

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Enabling Protection for Critical Infrastructure through Security and Privacy in the Industrial Internet of Things

Enabling Protection for Critical Infrastructure through Secu...

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International Conference on Communication and Electronics Systems (ICCES)

作者： B. Yasotha T. Vijayanandh P Thirumoorthy M.Sangeetha Priya Sasidaran S S.B. Prakalya Department of Data Science and Business Systems SRMIST Kattankulathur Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Dhanalakshmi College of Engineering Chennai India Department of Computer Science and Engineering (Cyber Security) Saveetha Engineering College Chennai Department of Information Technology Panimalar Engineering College Chennai Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Industrial Internet of Things can improve critical infrastructure in energy, transportation, and manufacturing. However, IIoT device and system integration opened security weaknesses that bad actors may exploit, causing huge disruptions and financial losses. IoT network security and privacy must be prioritized to protect key infrastructure. IIoT security issues and solutions are examined in this research. First, we take a look at the state of security and the unique risks that industrial IoT devices are up against, including hacking, illegal access, and cyber-physical assaults. Then, to lessen the impact of these dangers, we investigate cutting-edge security solutions, such as authentication, encryption, and access control systems. Building a robust security architecture specifically for IIoT applications is one of the main goals of the project. To improve safety and anonymity, this system uses cutting-edge innovation like blockchain, AI, and edge computing. We also talk about how privacy-preserving methods like differential privacy and anonymization are important for keeping sensitive information safe while allowing data sharing for analysis and operations. We assess the efficacy of the suggested security protocols and procedures using a battery of case studies and simulations. reducing the attack surface and improving the resilience of IIoT systems can be achieved by a multi-layered security approach that includes effective privacy protections. We conclude by outlining our research's larger implications, which include the importance of standardization, regulatory compliance, and stakeholder collaboration in ensuring the safe and privacy-protecting deployment of IIoT technologies in vital infrastructure. Protecting vital infrastructure from cyber threats is our ultimate goal, and our work helps build IIoT systems that are more secure and dependable.

关键词： Privacy Collaboration Standardization Critical infrastructure Encryption Security Stakeholders Protection Computer crime Industrial Internet of Things

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Enhancement of Security in IoT using Secure Hash Cryptography Algorithm with double Encryption (SHCA-dE)

Enhancement of Security in IoT using Secure Hash Cryptograph...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G. Uganya d. rajalakshmi ramarao Vangapandu M. dinesh Jayant Giri Mahmoud Odeh Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of Computer Science and Engineering Vignan's Institute of Engineering for Women Vishakhapatnam Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur Division of Research and Development Lovely Professional University Phagwara India Department of Cyber Security Faculty of Information Technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

The “Internet of things” is the developing technology to transfer the world into smart innovation. “IoT” security is the most problematic issue in recent applications due to its properties including ‘centralized architecture’, ‘limited capacity devices’, and ‘ambiguity’. So, blockchain with “IoT” applications provides secure data communication compared to traditional “IoT” applications. Blockchain is a recent growing technology that has features including ‘decentralized structure’, ‘transparency’, ‘immutable records’, ‘increased capacity’, and ‘quick backtracks’. The ‘cryptography hash algorithm’ is very important to provide secure data transmission. It converted the different size input to fixed-size unalterable output to enhance the security. But still, it needs efficient algorithm to secure data transmission with safeguard of private key. The objective of this paper is to propose a secure hash cryptography algorithm with double encryption (SHCA-dE) to protect the private key message. Then, this proposed algorithm has been analyzed with Md5, SHA-256, and SHA3-512. From this analysis, the SHA3-512 performs well with proposed algorithm.

关键词： Hash functions Technological innovation Accuracy Public key cryptography Blockchains Encryption Internet of Things data communication

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Anomaly detection in Electrocardiogram Signals using Autoencoders

Anomaly Detection in Electrocardiogram Signals using Autoenc...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： J. Preetha G. Brindha M.r Pavithra C. Ezhilazhagan S Yuvaraj S. Sujatha Department of Artificial Intelligence & Data Science Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Electronics and Instrumentation Engineering Meenakshi College of Engineering Chennai Tamil Nadu India Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

One potential use of autoencoders in healthcare is the identification of anomalies in electrocardiogram (ECG) readings. The primary objective of this research is to design a reliable system that can detect irregularities in electrocardiogram data automatically. The goal is to improve the efficiency and accuracy of anomaly detection using autoencoders so that cardiovascular problems may be diagnosed and treated early. The objective is to prove that the suggested approach can correctly detect a variety of abnormalities, such as arrhythmias and other cardiac abnormalities, using electrocardiogram (ECG) data by conducting extensive experiments and validations. This study aims to provide a dependable tool for healthcare practitioners to evaluate ECG data effectively and quickly detect possible health issues, therefore contributing to the improvement of medical diagnostics. The goal of our effort is to make clinical decision-making in cardiac care settings more effective and to improve patient outcomes. The PTB diagnostic ECG database yielded five results, ranging from 0.57 to 0.92, for each of the five patients in the sample.

关键词： Accuracy databases Soft sensors Autoencoders Medical treatment Medical services Electrocardiography reliability engineering distance measurement Anomaly detection

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Predicting Crops Based on Soil and Environmental Characteristics Using ArIMA and LSTM Model

Predicting Crops Based on Soil and Environmental Characteris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A d. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and rFE select features. The model was trained using a hybrid ArIMA-LSTM technique, which blends deep learning and time-series forecasting. The ArIMA-LSTM model outperforms solo ArIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ArIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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Machine Learning Method for recognizing Human Eye Pupil region

Machine Learning Method for Recognizing Human Eye Pupil Regi...

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Augmented Intelligence and Sustainable Systems (ICAISS), International Conference on

作者： r. rajkumar Gokulavani d rajesh K Nagaiah Apuri T. Chandrasekar r Benazir Begam Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai Tamilnadu India Department of Electronics and Communication Engineering Knowledge Institute of Technology Salem Tamilnadu India Department of CSD (Data Science) Marri Laxman Reddy Institute of Technology and Management Dhundigal Medchal Telangana India Department of Electrical and Electronics Engineering Velammal College of Engineering and Technology Madurai Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai Tamilnadu India

This research study describes how machine learning techniques can be employed to segment human pupils. The objective is to recognize and isolate the pupil region in ocular images. The aspects of a methodology are data set preparation, feature extraction, machine learning model selection, model training, model optimization, model testing, model evaluation, and finally post-processing. during preprocessing, this identifies and designates pupil regions in the dataset to obtain high-quality eye images. This compares two machine learning models, the Support Vector Machine (SVM) and the random Forest, after identifying the most significant image characteristics. After being trained and fine-tuned using the dataset, models are evaluated based on metrics such as precision, recall, F1 score, and accuracy. In post-processing, the segmented pupil regions are modified after the initial segmentation. random Forest attained 96.27 accuracy, while Support Vector Machine (SVM) obtained 95.17. This research contributes to the advancement of computer vision and is beneficial for software that employs human vision.

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Enhancement of IoT based Flood detection and Prevention using Arduino UNO with WiFi Module

Enhancement of IoT based Flood Detection and Prevention usin...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： r. M. dilip Charaan J. Shobana P. Krishnamoorthy B. Anni Princy r. J. Abinaya K. Murugesan Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Tamil Nadu India Department of Computer and Communication Engineering Panimalar Engineering College Tamil Nadu India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Tamil Nadu India Department of Electronics and Communication Engineering Loyola Institute of Technology Tamil Nadu India

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT’s WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does.

关键词： dams Storms Sensor systems real-time systems Sensors rivers Floods

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A Novel development of Artificial Intelligence Enabled Learning Methodology for Human depression Prediction Scheme

A Novel Development of Artificial Intelligence Enabled Learn...

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Intelligent Systems for Cybersecurity (ISCS), International Conference on

作者： G. ramkumar r Priyadharshini K. Selvi K. Kalaiselvi S. rathika N. Kanagavalli Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Information Technology R.M.K Engineering College Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

Human depression Prediction is essential for several reasons, primarily centered around improving mental health outcomes and providing timely interventions. Firstly, early detection of depression allows for prompt and targeted interventions, enabling individuals to receive appropriate support and treatment before their condition worsens. The Human depression Prediction Scheme (HdPS) introduces an innovative method for predicting depression by synergizing the capabilities of deep learning through the LeNet architecture with the optimization prowess of the Grey Wolf Optimization (GWO) algorithm. The proposed scheme achieves a commendable accuracy of 95%, attesting to its effectiveness in discerning depressive tendencies. Notably, the HdPS is implemented in Google Colab, emphasizing its accessibility and ease of use. This integration of advanced technologies and optimization techniques positions HdPS as a promising tool for early detection of depression, offering both high accuracy and practical implementation in a widely accessible computing environment.

关键词： deep learning Accuracy Mental health Computer architecture depression Prediction algorithms Internet

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