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Cheating Detection in Online Exam: A Comprehensive Survey 1

8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Bhowmik, Pratya Das, Smita National Institute of Technology Agartala Department of Computer Science and Engineering Tripura Agartala India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

Due to COVID-19, many educational institutes throughout the world shifted their means of teaching from offline to online. Online examination became more challenging while testing the academic skills or depth of knowledge of a student. The alarming situation ascends when a student practices unfair means during an online exam. In this paper, a survey is carried out based on recent research works where initially the reason behind cheating in exams is analyzed. Subsequently, detection measures for cheating during and after exams have also been discussed. Finally, various prevention measures for cheating have been studied to identify new research challenges in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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A Double Layer Encryption for Communication using Cryptographic Algorithms 2

A Double Layer Encryption for Communication using Cryptograp...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Ragavan, K. Mohankumar, B. Vivekrabinson, K. Arun, R. Arumuga School of Computer Science and Engineering Vellore Institute of Technology Vellore India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350328202

Cryptography is a fundamental field in computer science that focuses on securing communication and protecting sensitive information from unauthorized access. It involves the use of mathematical algorithms and techniques to transform plaintext into ciphertext, rendering it unintelligible to anyone without the proper decryption key. Cryptographic systems play a crucial role in ensuring confidentiality, integrity, and authenticity of data in various domains, including finance, healthcare, e-commerce, and communication networks. In this paper, a double-layer encryption method is proposed that uses the Vigenere and Polybius ciphers to secure communication. The Vigenere cipher is a classical method that uses a keyword to encrypt plaintext, while the Polybius cipher is another classical cipher that uses a grid to map letters to two-digit numbers to encrypt data. By combining these two ciphers, the proposed algorithm aims to enhance the security of the data by introducing a more complex and secure encryption method. The proposed method is implemented using Python programming language with web implementation. The web implementation of the algorithm uses Flask, a Python web framework. This allows users to encrypt and decrypt their data through a user-friendly interface, making it more accessible and practical for real-world use. Based on the obtained results demonstrate that the proposed method provides a higher level of security compared to using each cipher individually, without significantly impacting the encryption and decryption speed. Overall, the proposed double-layer encryption method can be a useful tool for secure communication in various applications. It contributes to the field of cryptography by introducing a novel approach that enhances data security without compromising usability. © 2024 IEEE.

关键词： Cryptography

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Intelligent Satin Bowerbird Optimizer Based Compression Technique for Remote Sensing Images

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computers, Materials & Continua 2022年第8期72卷 2683-2696页

作者： M.Saravanan J.Jayanthi U.Sakthi R.Rajkumar Gyanendra Prasad Joshi L.Minh Dang Hyeonjoon Moon Department of Networking and Communications School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Electronics and Instrumentation Engineering Kongu Engineering CollegePerundurai638060India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

Due to latest advancements in the field of remote sensing,it becomes easier to acquire high quality images by the use of various satellites along with the sensing *** the massive quantity of data poses a challenging issue to store and effectively transmit the remote sensing ***,image compression techniques can be utilized to process remote sensing *** this aspect,vector quantization(VQ)can be employed for image compression and the widely applied VQ approach is Linde–Buzo–Gray(LBG)which creates a local optimum codebook for image *** process of constructing the codebook can be treated as the optimization issue and the metaheuristic algorithms can be utilized for resolving *** this motivation,this article presents an intelligent satin bowerbird optimizer based compression technique(ISBO-CT)for remote sensing *** goal of the ISBO-CT technique is to proficiently compress the remote sensing images by the effective design of ***,the ISBO-CT technique makes use of satin bowerbird optimizer(SBO)with LBG approach is *** design of SBO algorithm for remote sensing image compression depicts the novelty of the *** showcase the enhanced efficiency of ISBO-CT approach,an extensive range of simulations were applied and the outcomes reported the optimum performance of ISBO-CT technique related to the recent state of art image compression approaches.

关键词： Remote sensing images image compression vector quantization sand bowerbird optimizer metaheuristics space savings

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Analysis and Impact of Electromagnetic Field Leakage in WPT on Central Nervous System: A Neural Network Approach 4

Analysis and Impact of Electromagnetic Field Leakage in WPT ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Shukla, Garima Awasthi, Vanshaj Goswami, Prafull Singh, Sofia Amity School of Engineering and Technology Amity University Department of Computer Science and Engineering Maharashtra Mumbai India Indian Institute of Information and Technology Department of Computer Science and Engineering Sonipat India Amity School of Engineering and Technology Amity University Department of Artificial Intelligence Uttar Pradesh Noida India

ISBN: (纸本)9798350383997

The potential for wireless power transfer (WPT) technologies to transform the charging capabilities of a wide range of applications, from consumer electronics to medical devices has attracted a lot of attention. But uncertainties remain about how these technology's electromagnetic field (EMF) leakage affects human health, especially the central nervous system (CNS). To find a way to assess and forecast all of these consequences, this study explores at the possible effects of EMF leakage from WPT systems on the central nervous system (CNS) and suggests a distinctive approach that employs the applications for neural networks. Using real- world data and computational models, this study will produce an accurate model of EMF absorption and propagation in biological tissues, with a specific focus on the central nervous system. By focusing insights into potential risks to health and informing the emergence of requirements which assure the safe implementation of wireless power technologies, the research will enhance our understanding of the connections between EMF exposure and the central nervous system. The ultimate objective of this study is to encourage safer and more sustainable wireless charging technologies by enhancing the accuracy and efficiency of risk assessment in circumstances of EMF exposure from WPT systems through the implementation of Machine Learning (ML) strategies © 2024 IEEE.

关键词： Inductive power transmission

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Leveraging Innovative Technologies for Ransomware Prevention in Healthcare: A Case Study of AIIMS and Beyond

Leveraging Innovative Technologies for Ransomware Prevention...

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Dubey, Ateen Tiwari, Geetika Dixit, Anshika Mishra, Ananya Pandey, Mohit Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur209305 India

ISBN: (纸本)9789819706402

The paper explores the challenges posed by ransomware attacks in the healthcare sector within the context of the digital transformation of healthcare. It examines real-world incidents, such as those at AIIMS hospital, to highlight the disruptive nature of ransomware attacks and underscores the importance of proactive defense strategies. The study introduces three innovative approaches to ransomware prevention in healthcare: blockchain, machine learning, and software-defined networking (SDN). Each approach is analyzed in terms of its role in safeguarding healthcare data. Blockchain ensures data integrity and access control through decentralization, while machine learning enhances threat detection by identifying unusual behaviors, potentially indicative of ransomware. SDN provides dynamic network segmentation, real-time responses, and centralized security updates to counteract attacks. The paper concludes by summarizing the benefits and challenges associated with these methods and emphasizes the necessity of collaboration among healthcare professionals, technologists, and policymakers for effective implementation. These innovations are crucial for the healthcare industry to navigate the evolving cybersecurity landscape and safeguard patient data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software defined networking

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Bridging Energy Holes in Underwater Acoustic Sensor Networks: A Call for Adaptive Routing Protocols

Bridging Energy Holes in Underwater Acoustic Sensor Networks...

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2024 IEEE Bangalore Humanitarian technology Conference, B-HTC 2024

作者： Gadagkar, Akhilraj V. Chandavarkar, B.R. National Institute of Technology Karnataka Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798350344509

Underwater Acoustic Sensor Networks (UASNs) has significantly attracted the attention of researchers with many of its uses in aquatic environments, such as tactical surveillance, pollution monitoring, underwater resource exploration, and catastrophe detection. An important and difficult problem with UASNs is the rapid depletion of energy due to the high power requirements for acoustic communication, while the battery capacity of the sensors is restricted. However, if the battery energy consumption is unbalanced, it can cause nodes to deplete their energy reserves quickly. This results in the formation of energy sink hole, which divide the whole network and damage the accuracy of the monitoring data. In severe cases, it may even cause the entire network to fail. However, ensuring that energy is evenly distributed across nodes may significantly increase the longevity of these networks. Therefore, it is imperative to implement energy-conserving protocols that can efficiently handle the restricted energy allocation while considering the distinctive characteristics of the underwater environment. © 2024 IEEE.

关键词： Energy utilization

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Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 389-398页

作者： Vaithianathan, V. Rebecca, B. Begum, M. Fathima Sundarraj, Antony Allwyn Kumar, M. Prema Kiran, Ajmeera Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai 603110 India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana Dundigal 500043 India Assistant Professor Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology and Science Andhra Pradesh-517325 Madanapalle India Department of Food Technology Sri Shakthi Institute of Engineering and Technology Coimbatore Tamilnadu Coimbatore India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The primary sector of our Indian economy is agriculture. Today’s world’s most important and vital industries are agriculture and irrigation. Information and communication technology must be used in our agriculture sectors in order to support farmers and agriculturalists in improving their practices across the entire crop-growing and post-harvest processes. It is still challenging to define processed food, even in light of mounting research that suggests consuming more ultra-processed food has negative health effects. This research proposes novel technique in smart agriculture model based on cloud machine learning in cloud machine learning and healthcare system. By leveraging IoT and cloud-based data to create data-driven product decisions, cloud computing in agriculture can assist farmers with smart agriculture practices. Here, the input has been collected as food quality based on smart agriculture using a cloud model as well as processed for noise removal and normalisation. Then, based on this data, the healthcare security system has been analysed using reinforcement adversarial stacked encoder with Markov fuzzy algorithm. The experimental analysis has been carried out for various food quality datasets in terms of training accuracy, random precision, normalised correlation ratio, F-1 score, and AUC. The proposed technique achieved training accuracy of 96%, F-1 score of 94%, random precision of 97%, normalised correlation ratio of 93%, and AUC of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial stacked encoder Cloud machine learning Cloud machine learning Healthcare system Smart agriculture

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Performance Evaluation of Meta-data features for Spam SMS Classification using Sequential Models

Performance Evaluation of Meta-data features for Spam SMS Cl...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Prakash, K. Sakthi Abirami, A.M. Singh, Supriya Ramanujam, E. Thiagarajar College of Engineering Department of Information Technology Madurai India National Institute of Technology Silchar Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798350348460

The escalating threat of cybercrime, particularly in the form of spam, fraudulent messages, and phishing attacks, poses a substantial risk to individuals who may unknowingly divulge sensitive information to malicious user. This paper addresses the urgent need for a robust spam filtering model capable of detecting and preventing these deceptive messages before reaching users' inboxes or SMS folders. Focusing on the English spam SMS dataset from the UCI repository, we propose a sequential learning-based spam message filtering framework using the meta-data feature of SMS message. The performance of the meta-data features are compared with the various other methods such as vectorization, sequential model with embedding layers. Performance shown that the Meta-data feature vectors doesn't depend on any parameters for the process of vectorization and it leverages the performance of the sequential models. © 2024 IEEE.

关键词： Deep Learning Ham hand-crafted features meta-data Sequential models SMS Spam

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Cloud-Based Cancer Prediction System for Enhancing Security and Authentication Using Deep Learning

Cloud-Based Cancer Prediction System for Enhancing Security ...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kiran Kumar, P.N. Neha, B.S. Sree, K. Theja Harshitha, K.M. Prathusha, K. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Cloud-based applications are ubiquitously employed across diverse fields within the ever-evolving landscape of technology, serving as integral roots for innovation and efficiency. A meticulous evaluation of the skin cancer prediction has been done in this research, with a focus on deep neural network-based approaches. By scrutinizing a subset of 187 studies leveraging image processing techniques alongside Convolutional Neural Networks (CNNs), a profound understanding of their collective impact within various sectors emerges, allowing for a detailed exploration of their effectiveness and applications. Cancer prediction being the pivotal event of this research draw on the applications of convolutional neural network. By delving into a curated selection of studies showcasing deep neural network applications, a nuanced exploration of their efficacy within this sector is attainable, presenting an opportunity to glean insights into their practical implications and advancements. This insightful exploration acts as a beacon for future research endeavors, bringing attention to the progression of deep architectures in addressing pressing healthcare challenges and beyond. These findings underscore the paramount importance of these advancements in refining the accuracy of cancer prediction systems. In summation, this research illuminates the profound influence of deep learning methodologies, particularly convolutional neural networks, on the forefront of skin cancer prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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Gene Array Based Cervical cancer categorization with Machine Learning Techniques

Gene Array Based Cervical cancer categorization with Machine...

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2024 International Conference on Integration of Emerging Technologies for the Digital World, ICIETDW 2024

作者： Nisha, K. Vijayalakshmi, A. Surya, N. Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331541170

Cervical squamous cell carcinoma is anticipated to become the fourth most prevalent cancer globally and is expected to cause the death of one in three affected women. The World Health Organization (WHO) reports that 86% of all documented cases of cervical cancer occur in developing countries.. Gene expression profiling is still used extensively in the investigation of cervical cancer. Gene expression patterns have been investigated in biological networks, in particular a gene co-expression network, which emerges as an unconventional comprehensive method for microarray research. A component of the physiognomy in gene co-expression networks continues to be disproportionate even at the point in time when a particular biological issue has been ***, the Enhanced Bacterial colony Optimization Algorithm with Hilbert-Schmidt Independence Criterion (EBCO-HSIC) and the Support Vector Machine (SVM) method are bestowed in this investigation by selecting symbolic genes that encompass phases including normalisation, module detection, gene selection, and classification. The gene dataset is first percolated using the fuzzy C Means (FCM) approach, which is then used for both the purpose of normalisation and the removal of extraneous features. The EBO method is used in order to choose the suitable genes using the degree of accuracy in categorization as the fitness function. The approach that was devised focuses on gene importance as well as calibrating the dependence between differentially expressed genes (DEGs) in order to arbitrate a selection of the most informative genes by using an ultrafast beyond ascendable bat algorithm. In order to remedy this situation, HSIC, which is supported in part by EBO, is put into action. The SVM classifier is an effective tool for categorising high-dimensional data, such as that obtained from microarray gene expression. © 2024 IEEE.

关键词： Diseases

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