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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Srinivasan, Jayanth Niranjanee, M. Nandhana, C. Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600 089 India

ISBN: (纸本)9789819773701

In the realm of smart tourism, understanding tourist feedback through sentiment analysis is pivotal for enhancing service quality and experience. This study introduces a novel Hybrid Transformer-Attention Model (HTAM), designed to advance sentiment classification accuracy, model interpretability, and linguistic adaptability in tourist reviews. By integrating the contextual insights of transformer models with a strategic attention mechanism, HTAM aims to provide superior performance over existing hybrid models like Convolutional Neural Networks—Bidirectional Long Short-Term Memory (CNN-BiLSTM) (Meng, The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things, 2024). With empirical evidence showcasing its capability to outperform conventional models in metrics with an accuracy of 96.74%, HTAM’s application promises to revolutionize smart tourism by enabling more responsive and tailored service improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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AI-Driven Assessment and Evaluation: Enhancing Sustainability in Educational Practices 3

AI-Driven Assessment and Evaluation: Enhancing Sustainabilit...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Chinnasamy, P. Pushpalatha, M. Nirmala, V. Vidhya, V. Ayyasamy, Ramesh Kumar Priya, Divya Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Computer Science and Technology Karpagam College of Engineering Coimbatore India Department of MCA Annamacharya PG College of Computer Studies Rajampet Kadapa India India Kampar Perak Malaysia Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

ISBN: (纸本)9798331532420

AI-powered educational technologies are emerging as transformative forces in the quickly changing field of education, where innovation is essential to keeping ahead of the competition. Assessments are one area that is undergoing revolutionary transformation. Artificial Intelligence (AI) is redefining the way we evaluate learning outcomes as traditional evaluation approaches struggle to keep up with the demands of the digital age. The field of artificial intelligence-powered learning assessments has a lot of promising future developments ahead of it. This research proposes novel technique in assessment and evaluation based on online education system using Artificial intelligence in machine learning techniques. Here, the input has been analysed as student performance data based on online education and processed for removing of missing values with noise removal. Then this data has been analysed using spatio LSTM convolutional fuzzy neural network for classification of evaluation and assessment model. the experimental analysis has been carried out for various student performance analysis dataset in terms of training accuracy, average precision, specificity, F-measure and recall. Proposed method training accuracy 97%, Average precision 94%, F-measure 90%, RECALL 93%, SPECIFICITY 96%. © 2024 IEEE.

关键词： Adversarial machine learning

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Inspired Detection Model For Clustering Based Surveillance System: A Non-Conventional Review

Inspired Detection Model For Clustering Based Surveillance S...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Gupta, Anil Kumar Sharma, Rupak Ojha, Rudra Pratap Srm Institute of Science and Technology Department of Computer Science and Engineering NCR Campus Ghaziabad Uttar Pradesh Modinagar India Srm Institute of Science and Technology Department of Computer Applications NCR Campus Ghaziabad Uttar Pradesh Modinagar India Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350382723

in current years, there has been a growing hobby in growing superior surveillance systems for detecting and monitoring anomalous activities. Traditional surveillance systems depend upon handmade policies and features, which are frequently limited of their capacity to accurately become aware of complex anomalies. To conquer this problem, we suggest a better visible anomaly detection version for clustering-based surveillance structures the use of long brief-time period reminiscence (LSTM) Diffusion manner. The proposed version incorporates the temporal context of the CCTV photos with the aid of making use of the LSTM network, which has been proven to deal with sequential data effectively. The LSTM community is used to extract movement features from the video frames, which are then fed into the clustering set of rules. This allows for the identification of comparable styles of sports and grouping them into clusters. Furthermore, we combine the concept of the Diffusion technique into the LSTM version to decorate the clustering method. The Diffusion procedure entails the gradual propagation of information among nodes in a network, which is particularly beneficial in figuring out abrupt changes in activities. This permits the version to locate anomalies with better accuracy and decrease fake alarms. To assess the performance of our proposed version, we behavior experiments on a real-global surveillance dataset. The consequences display that our model outperforms current techniques in terms of accuracy and detection price. © 2024 IEEE.

关键词： Diffusion

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Comparative Analysis of Different Approaches for Cyber Forensics 4

Comparative Analysis of Different Approaches for Cyber Foren...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Raj, Dharm Gupta, Ajay Kumar Krishna, Kunchanapaalli Rama Sharda School of Engineering & Technology Sharda University Department of Computer Science & Engineering India Iimt College of Engineering Department of Computer Science & Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (纸本)9798350387490

The Cyberspace has been enhanced rapidly during the recent years, where people are facing major crisis in that situation cybercrimes all around world are at peak. Cybercriminals always try to exploit system and gain potential information. Cyber Forensics plays major preventive role from cybercrimes, many researchers have proposed their different method to block such type of malwares links with help of Machine Learning, they created advance frameworks. Law enforcement agencies use different types of advance tools for the detection and prevention. In this paper authors propose the comparative review about the work done by other people in this field and analyzed them how they are working what are the limitation have currently also we studied about the different hardware and software tools that is currently being used by cyber experts in forensics. Also we tried to examine current scenario of cybercrimes in India. © 2024 IEEE.

关键词： Adversarial machine learning

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Web Based Attendance Management System Using Geo-Location 8

Web Based Attendance Management System Using Geo-Location

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Vishanthanshree, R. Sai Sucharith M, Venkata Naga Vijiamutha Mary, A. Mercy Paul Selvan, M. Jancy, S. Sathyabama Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350347791

In Attendance Management System by using Geo-Location, the aim is to take attendance by using Geo-Location to track the information about the user or particular user whether the user is present or not in that particular location which was given by admin and this gives the total information about the user. the recent trend in the attendance management system by using finger print and the face detection but in some this will not work and it is time taking process, by using Geo-Location we can mark the attendance it makes easier for authentication. The paper focuses on using of the Geo-Location for the attendance system. © 2023 IEEE.

关键词： Face recognition

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Detection and Classification of Novel Attacks and Anomaly in IoT Network using Rule based Deep Learning Model

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SN computer science 2024年第8期5卷 1056页

作者： Chakraborty, Sanjay Pandey, Saroj Kumar Maity, Saikat Dey, Lopamudra Department of Computer and Information Science (IDA) REAL AIICS at Linköping University Linköping Sweden Department of Computer Science and Engineering Techno International New Town Kolkata India Department of Computer Engineering and Applications GLA University Mathura India Department of Computer Science and Engineering Sister Nivedita University Kolkata India Department of Computer Science and Engineering Meghnad Saha Institute of Technology Kolkata India Department of Biomedical and Clinical Sciences Linköping University Linköping Sweden

Attackers are now using sophisticated techniques, like polymorphism, to change the attack pattern for each new attack. Thus, the detection of novel attacks has become the biggest challenge for cyber experts and researchers. Recently, anomaly and hybrid approaches are used for the detection of network attacks. Detecting novel attacks, on the other hand, is a key enabler for a wide range of IoT applications. Novel attacks can easily evade existing signature-based detection methods and are extremely difficult to detect, even going undetected for years. Existing machine learning models have also failed to detect the attack and have a high rate of false positives. In this paper, a rule-based deep neural network technique has been proposed as a framework for addressing the problem of detecting novel attacks. The designed framework significantly improves respective benchmark results, including the CICIDS 2017 dataset. The experimental results show that the proposed model keeps a good balance between attack detection, untruthful positive rates, and untruthful negative rates. For novel attacks, the model has an accuracy of more than 99%. During the automatic interaction between network devices (IoT), security and privacy are the primary obstacles. Our proposed method can handle these obstacles efficiently and finally identify, and classify the different levels of threats. A brief set of comparisons on detection accuracy and time measurements with some state-of-the-art methods are discussed in this paper. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Anomaly detection Classification Deep Learning Machine Learning Network-attack

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Logistics Shipping Based Blockchain Using Smart Contracts 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

作者： Prashanth, Mallellu Sai Karnati, Ramesh Velpuru, Muni Sekhar Venkateshwara Reddy, H. Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Information Technology Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

The logistics industry is a vital component of the global economy, but it still faces several challenges, including inefficient processes, lack of transparency, and high costs due to intermediaries. Blockchain technology has the potential to address these challenges by providing a decentralized and transparent platform that can automate and streamline logistics processes through smart contracts. This research paper presents a study on Logistics shipping based on blockchain using smart contracts in Solidity language. The paper discusses the challenges in the logistics industry and the potential of blockchain technology to solve them. It then presents a Smart Contract for Logistics shipping, including the required variables, a struct for shipments, a mapping to hold the shipments, events for shipment creation and delivery, and functions to create a new shipment, mark a shipment as delivered, and get the shipment details. The paper also discusses the benefits and limitations of the proposed solution and compares it with existing logistics systems. It concludes that the proposed solution can provide a secure, transparent, and efficient platform for logistics shipping that can eliminate intermediaries, reduce costs, and improve the overall logistics ecosystem. Overall, this research paper provides insights into the potential of blockchain technology for logistics shipping and presents a practical solution that can be implemented in real-world scenarios. It also highlights the need for further research and development to optimize the solution and address any potential challenges in its implementation. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Smart contract

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Equity-Enhancing AI: Integrating Counseling Marks for Fair Student Selection 10

Equity-Enhancing AI: Integrating Counseling Marks for Fair S...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Bhuvaneswaran, B. Vijay, Priya Varshini, J. Yokeswaran, S. Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798350353068

Students and parents face major problem in deciding college based on their interest and academic acheiments. There is always a trade off between counselling marks and college selection. The proposed system, which would drastically change the current scenario, students and their families would receive personalized college recommendations based on their academic interests, career aspirations, and 12th grade cutoff scores. The system examines historical cutoff scores, admission trends, and other pertinent data to provide you with accurate, data-driven insights into your chances of being admitted to various colleges. Comprehensive information about the participating universities is provided, along with streamlined techniques for data retrieval and analysis and easily navigable interfaces. This system's main goals are to reduce the amount of lessthan-ideal college choices made by participants in the TNEA counseling program, improve admissions process transparency, and promote data-driven decision-making. To assist students and their families in making informed decisions that support their academic and career goals, the system makes use of sophisticated algorithms and a vast amount of data. The system aims to enable students to make wellinformed decisions about their future educational pathways through the integration of sophisticated analytics and user-friendly interfaces. The system's goal is to improve the college selection process and outcomes for students enrolled in the TNEA counseling program by providing information on admission probabilities and helping students identify colleges that best suit their preferences and aspirations. In summary, the suggested system leverages data-driven insights to assist students in making informed decisions, which is a significant advancement in the higher education admissions process. © 2024 IEEE.

关键词： Students

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MedEx: Enhancing Medical Question-Answering with First-Order Logic based Reasoning and Knowledge Injection 31

MedEx: Enhancing Medical Question-Answering with First-Order...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zafar, Aizan Mishra, Kshitij Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India School of AI and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798891761964

In medical question-answering, traditional knowledge triples often fail due to superfluous data and their inability to capture complex relationships between symptoms and treatments across diseases. This limits models' ability to provide accurate, contextually relevant responses. To overcome this, we introduce MedEx, which employs First-Order Logic (FOL)-based reasoning to model intricate relationships between diseases and treatments. We construct FOL-based triplets that encode the interplay of symptoms, diseases, and treatments, capturing not only surface-level data but also the logical constraints of the medical domain. MedEx encodes the discourse (questions and context) using a transformer-based unit, enhancing context comprehension. These encodings are processed by a Knowledge Injection Cell that integrates knowledge graph triples via a Graph Attention Network. The Logic Fusion Cell then combines medical-specific logical rule triples (e.g., co-occurrence, causation, diagnosis) with knowledge triples and extracts answers through a feed-forward layer. Our analysis demonstrates MedEx's effectiveness and generalization across medical question-answering tasks. By merging logical reasoning with knowledge, MedEx provides precise medical answers and adapts its logical rules based on training data nuances. © 2025 Association for Computational Linguistics.

关键词： Question answering

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Image Denoising Using AI with Entropy as Metric Analysis 1

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2nd EAI International Conference on Pervasive knowledge and collective intelligence on Web and Social Media, PerSOM 2023

ISBN: (数字)9783031660443

ISBN: (纸本)9783031660436

This paper introduces a novel denoising approach Image denoising is one of the fundamental challenges in the field of image processing and computer vision. Our main aim of the project is to get a complete noiseless image with high accuracy and less time. So, in our project we are proposing an effective denoising technique using RNN (Recurrent neural network) for fixed pattern noisy images which may reduce the usage of number of auto encoders. Here, we are passing images into the recurrent neural networks as pixel information in the form of a 3D coordinate system. RNN doesn’t migrate the information from one node to another node until it gets its basic requirements. As we are using a single auto encoder, it will reduce noise as well as time complexity. The statistical analysis is going to be observed by using the following metric considerations, namely SNR (Signal to noise ratio), PSNR (Peak signal to noise ratio), MSE (Mean square error) and Entropy. From this research work we are going to get a complete noiseless image. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Recurrent neural networks

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