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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Pawar, Rahul Singh Panse, Prashant Medi-caps University Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331512965

Early disease identification is essential for prevention in tomato leaf crops and to reduce crop loss. Unfortunately, traditional manual methods are very timeconsuming and error-prone making automated solutions necessary. This study aims to use deep learning, leading the way using Convolutional Neural Networks (CNNs) for disease detection in tomato leaves. Experiments on image classification were conducted for three different models (such as ResNet50, VGG16 and an upgraded version of VGG19) Proposed VGG19 model is giving superior performance, with an accuracy of 99.5%, precision: 99.23 % and recall :99.47% F1-Score: 99.28%. At the other end of the scale, ResNet101 and ResNet50 also performed quite well but with a little less accuracy percentage while VGG16 came at last. The results reveals that the Proposed VGG19 model is highly efficient for early disease detection in tomato crops and can be a powerful tool over precision agriculture. © 2025 IEEE.

关键词： Fruits

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An Approach for Adaptive Traffic Light Control System in ITS using VANET 2

An Approach for Adaptive Traffic Light Control System in ITS...

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

作者： Islam, Md. Mazaherul Rehena, Zeenat Mukherjee, Nandini Jadavpur University Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350328202

Traffic congestion is a common problem in everyday's life as number of vehicles is increasing day by day on the road. So, traditional road traffic control system needs to be updated accordingly. An adaptive traffic light control system can solve the traffic congestion problem efficiently. Vehicular Adhoc Network (VANET) is a common and useful application for designing an adaptive traffic light control system as a part of Intelligent Transportation System (ITS). This paper presents a literature review work on different types of traffic control systems and proposes a method for designing an adaptive traffic light control system using VANET technology. The proposed system ensures that all the vehicles pass through a road intersection point in an urban area smartly and efficiently. It also reduces traffic congestion and solves some other problems related to congestion by improving traffic flow through an intersection point in a city area. © 2024 IEEE.

关键词： Traffic congestion

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SwiftTap: RFID-Driven Automation for Instant Password Entry 8

SwiftTap: RFID-Driven Automation for Instant Password Entry

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Manhas, Prabal Anandu, U. Hariharan, U. Chandigarh University Department of Computer Science & Engineering Mohali India

ISBN: (纸本)9798331505462

SwiftTap is an innovative approach to user authentication by using RFID technology to replace conventional password-based methods. By employing everyday objects such as keychains and cards as secure substitutes, SwiftTap streamlines the authentication process. By merging progress in hardware and software, SwiftTap improves security and simplifies user access. This research delves into the intricacies of utilizing SwiftTap in different scenarios, such as in business settings and on personal devices. It also examines factors concerning user experience and their potential impacts. SwiftTap signifies a fresh era of effortless authentication, prompting users to embrace a smoother method of entry without the usual login difficulties. In addition to its seamless authentication capabilities, SwiftTap also addresses privacy concerns by employing encryption protocols that safeguard user data during transmission. Its versatility extends to integration with existing systems, promising compatibility across diverse platforms. With SwiftTap, the paradigm shifts towards effortless yet secure authentication is within reach, promising enhanced user. © 2024 IEEE.

关键词： Firmware

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AI-Powered Medical Record Summarization for Diagnosis and Physician Training 3

AI-Powered Medical Record Summarization for Diagnosis and Ph...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Singh, Abhishek Rawat, Abhas Khurana, Vrinda Chandel, Jyoti Chandigarh University Department of Computer Science Engineering Mohali India

ISBN: (纸本)9798350375190

In this article, researchers discuss the ways in which computers can assist doctors in diagnosing diseases by summarizing medical records. They also explore the impact of using computers to analyze health records on healthcare and on patients' ability to comprehend their health. The article discusses utilizing internet search data for early disease detection and addresses how searching for health information online may lead to excessive worrying. It also discusses various computer software that assist in diagnosing illnesses, including newer ones such as Isabel. The paper discusses the utilization of sophisticated computer methods such as neural networks to improve comprehension of medical data. It evaluates the ability of these methods to categorize medical text and assign codes for diagnoses. The article also discusses a computer program that accurately diagnoses knee pain and examines the use of deep learning in medical research. The paper also addresses methods to prevent computer programs from becoming too fixated on particular cases and techniques for visualizing data to enhance comprehension. In general, the goal of the paper is to enhance healthcare using computers to aid in more accurate disease diagnosis by doctors and support in medical research. Researchers in this paper explore how artificial intelligence (AI) could transform healthcare practices. They explore how AI can simplify the task of summarizing medical records, helping doctors make faster and more precise diagnoses. Furthermore, the article explores how using electronic health records (EHRs) can transform healthcare delivery and empower patients. © 2024 IEEE.

关键词： Diagnosis

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From Sequence to Structure: Transformer based Secondary Structure and GNN for Tertiary Structure Prediction 6

From Sequence to Structure: Transformer based Secondary Stru...

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6th International Conference on Futuristic Trends in Networks and Computing Technologies, FTNCT 2024

作者： Suryamol, K.S. Mathew, Linda Sara Subhash, Neeraja Computer Science and Engineering Department Mar Athanasius College of Engineering Kothamangalam686666 India

In computational biology, it is still quite difficult to predict the tertiary structure of proteins based on their amino acid sequence. In order to accurately predict the tertiary level, this paper proposes a new method that takes advantage of transformer models and graph neural networks (GNN). The directed approach extracts features from the protein sequence and captures long-range dependencies using a transformer-based model. After that, these features are fed into a GNN, which simulates the local interactions among amino acid residues by exploiting the natural graph structure of proteins. Detailed understanding of the structural and sequence information contained in protein sequences is possible with this combined approach. The CASP11 dataset, a standard for evaluating protein structure prediction techniques, is used to evaluate the correctness of the method. The proposed method's results predicts tertiary protein structures with encouraging accuracy, which could make it a useful tool to improve protein structure prediction and deepen our knowledge of protein function. © 2025 The Author(s). Published by Elsevier B.V.

关键词： Graph Neural Network(GNN) Protein Secondary Structure Tertiary Structure Transformer Model

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Traffic Density Prediction System using Deep Learning 2

Traffic Density Prediction System using Deep Learning

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2nd International Conference on data science and Information System, ICDSIS 2024

作者： Verma, Hritik Sarkar, Sayan Sadhukhan, Souranil Mishra, Aniket Mondal, Md Ashifuddin Narula Institute Of Technology Department Of Computer Science And Engineering India

ISBN: (纸本)9798350381665

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy. © 2024 IEEE.

关键词： Traffic congestion

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MEL Frequency Features Driven Deep Learning for Speech Emotion Detection: A Novel Model

MEL Frequency Features Driven Deep Learning for Speech Emoti...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Rani, Rashmi Tiwari, Kalyani Ramaiya, Manoj Kumar Nagar, Rajesh Kumar Shrivastava, Neeraj Hardia, Garima Indore India IPS Academy Institute of Engineering and Science Department of Computer Science & Engineering India Indore India Indore India

ISBN: (纸本)9798331512965

Speech Emotion Recognition (SER) is a critical area of research in Human-computer Interaction (HCI), aiming to develop systems capable of identifying and responding to human emotions conveyed through speech. This paper focuses on training a Deep Neural Network (DNN) model using Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction, a widely used and effective technique in this domain. To address the complexity of speech data, we utilized a deep learning algorithm, specifically employing the Long Short-Term Memory (LSTM) network, in conjunction with MFCC features. © 2025 IEEE.

关键词： Emotion Recognition

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Advancements in Speech Recognition: Exploring Feature Extraction Techniques for Enhanced Accuracy and Human-Machine Communication 4

Advancements in Speech Recognition: Exploring Feature Extrac...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Bhavani, R. Kathirvelu, Kalaivani Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343670

Speech Recognition (SR), for all diverse languages, is still a big challenge. SR is predominantly used in telecommunication, medical documentation, car control, aviation, etc. SR involves breaking down an audio signal of spoken words into discrete units and then matching those units to words in a language model. The main target of SR is to attain healthier verbal communication amid humans and machines. Therefore, it is a good technology of the human-machine crossing point. The SR uses the Feature Extraction (FE) techniques for better accuracy. In this paper, various techniques for FE are discussed, and an overview is provided to assist in selecting the most suitable technique for achieving higher accuracy. The review highlights the importance of developing more accurate and robust SR systems to improve the user experience in various domains. Overall, it provides the manuscript deals with five different techniques, and among them, the Mel-Frequency Cepstral Coefficients (MFCC) technique was found to exhibit a high level of accuracy. © 2024 IEEE.

关键词： feature extraction Signal processing Signals Sound Speech recognition

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Review of Steganography and Cryptography: Tools for Covert Communications

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International Journal of Network Security 2025年第2期27卷 431-438页

作者： Fatima, Kanza Wu, Nan-I Chan, Chi-Shiang Hwang, Min-Shiang Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan Department of Information Management Lunghwa University of Science and Technology Taiwan Department of Artificial intelligence Asia University Taiwan

In today’s rapidly evolving digital media landscape, safeguarding content privacy and preventing unauthorized access to copyrighted material are major challenges. Cryptography plays a crucial role in modern digital media security, providing the essential tools to protect the confidentiality, reliability, and genuineness of digital media information. This paper examines cryptographic and steganographic techniques employed for securing digital media content. It provides an overview of key cryptographic principles, including symmetric and asymmetric encryption. It also provides different branches of steganography. This paper also discusses different evaluation matrices for cover object quality. Overall, this research offers a comprehensive exploration of steganography, highlighting its importance as a flexible communication method in the digital age and its future scope. © (2025), (International Journal of Network Security). All rights reserved.

关键词： Information hiding

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Deep Learning-Based Diagnosis System for Liver Cancer Through Multimodal Determination for Early Treatment

Deep Learning-Based Diagnosis System for Liver Cancer Throug...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Fatima, N. Sabiyath Singh, Ramendra Swathi, Akuthota Choudhary, Amar Ahmed, Azhar Sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-based diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results based on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms based on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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