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ProctorXam - Online Exam Proctoring Tool 1

ProctorXam - Online Exam Proctoring Tool

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1st IEEE International Conference on Electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Ashwini, S. Bhuvan Kumar, M.S. Chandra Sai, D. Sathya, V. Atria Institute of Technology Dept. of Computer Science & Engineering Bangalore India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371802

Robust exam monitoring solutions are now essential in light of the most notable Covid-19 outbreak and the increasing inclination towards virtual learning. The automated method for supervising online exams that is suggested in this paper is intended to lessen the difficulties associated with exam cheating. With the assistance of our system's clever face detection algorithms, instructors may efficiently identify suspicious conduct by monitoring students' web activity and camera feed in real-time. Unlike earlier systems, ours provides improved accuracy and functionality, guaranteeing the authenticity of online tests. In light of the difficulties presented by the epidemic, this research attempts to offer a workable option for academic institutions and people wishing to administer fair and secure online exams. © 2024 IEEE.

关键词： Face recognition

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Intelligent Route Planning for Post-Earthquake Rescues Using IoT 6

Intelligent Route Planning for Post-Earthquake Rescues Using...

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6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Tamzid, Rahat Mahee, Ehfaz Faisal Rahman, Ashikur Tamanna, Tahira Akhtar Rahman, Muntasir Hossain, Md. Mamun Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350385779

Starting rescue operations quickly after an earth-quake is the best way to save lives in such disasters. In the case of large-scale earthquakes, current post-earthquake response systems cannot determine which building or rescue route would save the most lives due to the lack of real-time data availability. Research gaps include a lack of automation in earthquake damage assessment of buildings, location identification, and estimation of the number of trapped people. To fill these gaps, this article proposed an innovative solution using advanced sensors and cloud services for post-earthquake rescue route planning systems. This system aims to improve rescue operations to save the maximum number of lives in the shortest time and minimize impacts by optimizing resource allocation and overall response efficiency. By filling the existing research gap, our system seeks to provide more responsive and effective solutions to post-earthquake emergencies. Tested in a simulated environment with 16 samples, our system successfully collects and processes data from IoT devices deployed in buildings, classifies structural damage, and estimates the number of people affected. Our approach significantly reduces the time required to initiate rescue operations by providing rescuers with optimized routes to disaster sites, which has potential implications for disaster management strategies. © 2024 IEEE.

关键词： Earthquakes

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

引用

2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulnerability Scanners 3

OpenVAS vs the Rest: Unveiling the Competitive Edge in Vulne...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Sharma, Mayank Desai, Divyanshu Arun, Aditya Ranganathan Priya, L. Rajagopalan, Narendran National Institute of Technology Dept. of Computer Science and Engineering Puducherry India

ISBN: (纸本)9798350381931

In an increasingly interconnected and digital world, robust vulnerability management is paramount to safeguarding critical information assets and maintaining the integrity of network infrastructures. This paper explores into the heart of vulnerability assessment by presenting a comprehensive analysis of OpenVAS, an open-source vulnerability scanning tool, and its distinct advantages over other competing solutions. OpenVAS has emerged as a standout contender in the realm of vulnerability scanners, owing to its unique features, exceptional performance, and a commitment to open-source principles. Our study begins by providing an overview of the vulnerability management landscape, highlighting the importance of timely and accurate vulnerability assessment. Key advantages examined in this paper include OpenVAS's extensive coverage of vulnerability databases, ease of customization, scalability, and its robust community support. Moreover, its open-source nature ensures transparency and flexibility, making it an attractive choice for both small businesses and large enterprises. This paper aims to serve as a valuable resource for cybersecurity practitioners, IT professionals, and decision-makers seeking a thorough understanding of vulnerability assessment tools. By shedding light on OpenVAS's unparalleled strengths, it provides insights that empower organizations to make informed choices for their cybersecurity posture. In an era where cyber threats evolve rapidly, OpenVAS emerges as the beacon of proactive vulnerability management, offering distinct advantages that redefine the standards for the industry. As cybersecurity assumes an increasingly critical role in the digital age, this paper underscores the significance of choosing a tool that not only detects vulnerabilities but also empowers organizations to address them effectively. © 2024 IEEE.

关键词： Cybersecurity

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An IoT Solution for Enhanced Tray Management Systems in Food Service and Industrial Settings

An IoT Solution for Enhanced Tray Management Systems in Food...

引用

2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Chaudhary, Vipin Kumar Singh, Suchita Singh, Nagendra Pratap Afreen, Sana Yadav, Manmohan Singh Gupta, Durgesh Kumar Lovely Professional University Dept. of Computer Science and Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science and Engineering Uttar Pradesh Gorakhpur India Dr. B R Ambedkar National Institute of Technology Dept. of Computer Science and Engineering Punjab Janlandhar India Telangana Hyderabad India Sharda University Dept. of Computer Science and Engineering Uttar Pradesh Greater Noida India Institute of Engineering and Technology Dept. of Computer Science and Engineering Uttar Pradesh Sitapur India

ISBN: (纸本)9798350385199

This study introduces TraySmart, a cutting-edge Internet of Things (IoT) tool meant to completely transform the effectiveness and administration of tray systems in industrial, food court, and cafeteria environments. TraySmart employs cutting-edge technologies like weight sensing, RFID tag reading, temperature monitoring, NFC for user interaction, and Wi-Fi Bluetooth for data transmission to tackle the widespread issues associated with tray management, such as inefficient operations, concerns about food safety, and less-than-ideal user experiences. The weight-sensing feature of the device makes it possible to identify objects on the tray and monitor the contents of the tray in real time. Reading RFID tags makes identifying and tracking items easier, improving inventory control and lowering loss. Food safety and quality assurance depend on food items maintaining their ideal temperatures, which is ensured via temperature monitoring. Furthermore, NFC technology enhances the eating experience by enabling users to engage with the smartphone for payments or feedback. The communication options on the device guarantee smooth data transfer to a central system, facilitating thorough data analysis and system optimization. This essay covers the creativity, design, and development of the TraySmart prototype, the difficulties faced, and the strategies used in the process. The report assesses TraySmart's effect on operational efficiency, food safety standards, and customer happiness by carefully examining the outcomes of pilot testing. With its scalable solution to improve tray management systems, TraySmart marks a significant leap in the application of IoT technology in the food service and industrial sectors. The results highlight how IoT gadgets like TraySmart have the power to revolutionize operational procedures and service delivery, establishing a new benchmark for innovation in the industry. © 2024 IEEE.

关键词： Customer satisfaction

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

引用

32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers 1

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Sarkar, Subhasish Kumar, Gautam National Institute of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer. © 2024 IEEE.

关键词： Plant diseases

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A Transformer Based Indian Signed Language Recognition 1

A Transformer Based Indian Signed Language Recognition

引用

1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Saproo, Varun Aggarwal, R.K. National Institute of Technology Kurukshetra Dept. of Computer Engineering Kurukshetra India

ISBN: (纸本)9798350365016

Effective communication is essential to human interaction. The people with hearing and speaking disability primarily rely on Signed Language for communication. Each Sign Language has its own grammar and vocabulary. Individuals who can speak and hear proficiently often struggles to understand and respond to Sign Language gestures, creating a barrier to meaningful communication. This paper attempts to bridge the communication gap by using sophisticated deep learning models like transformers. INCLUDE is a dataset tailored for the Indian population, focusing specifically on Indian Sign Language. Our work involves breaking down videos into frames and employing image processing techniques. Furthermore, we utilize pre-trained models, such as VGG16, EfficientNet, and MobileNet, to extract latent embedding, which are then processed through Transformer network for classification. Hyperparameter tuning is used to optimize model performance. This allowed us to reach an accuracy of 92 % which is better than existing approaches. The proposed method was also tested on LSA64 dataset giving an impressive accuracy of 99.34%. © 2024 IEEE.

关键词： Deep learning

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Fruit Identification using YOLO v8 and Faster R-CNN -A Commparative Study 2

Fruit Identification using YOLO v8 and Faster R-CNN -A Commp...

引用

2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Sawant, Kedar Shirwaikar, Rudresh D. Ugavekar, Raj Ravindra Chodankar, Chandrakant Shyam Tirodkar, Bhupesh Nilkant Padolkar, Ashwek Gawas, Pramesh Agnel Institute of Technology and Design Dept. Computer Engineering Mapusa India

ISBN: (纸本)9798350382952

Fruits are rich in nutrients, vitamins, and minerals and play a crucial role in both dietary health and a nation's economy. In India, diverse fruit cultivation necessitates efficient identification methods. The manual fruit identification processes are time-consuming and biased, leading to agricultural inefficiencies. Hence, the adoption of machine learning and deep learning has gained immense popularity in the automation of the fruit identification preprocess. This study evaluates the performance of DL models, Faster R-CNN and YOLO v8, on a dataset featuring five fruits namely Apple, Cashew Apple, Banana, Mango and Orange for identifying fruits with the comparison between them. While both models yield impressive results, Faster R-CNN generally exhibits higher mAP. However, YOLO v8 excels in precision for specific fruits like Cashew Apple and Banana. The choice between models involves a trade-off between mAP and precision, emphasizing the need for further optimization considering factors like dataset variations, computational performance, and real-world applicability. © 2024 IEEE.

关键词： Fruits

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Smart Agriculture Inventory Management System Using Machine Learning Algorithms 1

Smart Agriculture Inventory Management System Using Machine ...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Kanyal, Hoshiyar Singh Jain, Harshit Tyagi, Harsh Yadav, Satyarth Prakash ABES Institute of Technology Ghaziabad Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350353648

In the agricultural sector, smallholder farmers in India face significant challenges with post-harvest losses, fluctuating market prices, and unsustainable practices. This research proposes a mobile-based application designed specifically for farmers in region of India, focusing on specific crops. To reduce agricultural waste, the app features an optimal storage location based on crop type. To improve farmer income, the app includes a crop recommendation module that integrates historical yield data, weather forecasts, soil characteristics, and real-time market prices. Machine learning models, validated with field data, suggest high-yield, profitable crops and optimal sales timing. A market information hub provides real-time mandi prices and demand forecasts. With a user-friendly interface and visual explanations, the app addresses the technological constraints of smallholder farmers. This research aims to reduce agricultural waste, improve farmer income, and promote sustainable agriculture practices. © 2024 IEEE.

关键词： Agricultural economics

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