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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Amanatulla, M.D. Swathi, G. Pallavi, M. Bindu, K. Pavana Computer Science & Engineering Department Lakkireddy Balireddy College of Engineering Mylavaram India

ISBN: (纸本)9798350361155

Prolonged Nephropathy Given that detection usually shows no signs in its early phases and has a high morbidity rate, it presents a serious danger to world health. This research paper proposes a machine learning architecture that uses models based on deep learning (CNN, MobileNet, VGG16, ResNet-50) to identify illness using MRI data. 12, 446 images in the dataset obtained through Kaggle are categorized as normal, cyst, stone, and malignancy. The study evaluates the models' performances, and ResNet-50 exhibits the highest accuracy. The VGG16 and ResNet-50 designs for the proposed system are presented, with a focus on their characteristics. Pre-processing, instruction, classification, and collection of data are all included in the implementation. Users may upload their photos to be classified by different algorithms. The results demonstrate the effectiveness of the ResNet-50 model, which had an overall 98% classification accuracy for renal diseases. In conclusion, the application of deep learning specifically, the ResNet-50 model shows promise as a quick and dependable technique for detection, therefore alleviating a major healthcare issue. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

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2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through Automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 IEEE.

关键词： Sales

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Crop Yield Prediction Using Multimodal Meta-Transformer and Temporal Graph Neural Networks

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 545-553页

作者： Sarkar, Somrita Dey, Anamika Pradhan, Ritam Sarkar, Upendra Mohan Chatterjee, Chandranath Mondal, Arijit Mitra, Pabitra Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Industrial and Systems Engineering Kharagpur721302 India Indian Institute of Technology Kharagpur Department of Agricultural and Food Engineering Kharagpur721302 India Indian Institute of Technology Patna Department of Computer Science and Engineering Patna801106 India Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur721302 India

Crop yield prediction is a crucial task in agricultural science, involving the classification of potential yield into various levels. This is vital for both farmers and policymakers. The features considered for this task are diverse, including weather, soil, and historical yield data. Recently, plant images captured in different modalities, such as red-green-blue, infrared, and multispectral bands, have also been utilized. Most of these data are inherently temporal. Integrating such multimodal and temporal data is advantageous for yield classification. In this work, a deep learning framework based on meta-transformers and temporal graph neural networks has been proposed to achieve this goal. Meta-Transformers allow the modeling of multimodal interactions, while temporayel graph neural networks enable the utilization of time sequences. Experimental results on the publicly available EPFL multimodal dataset demonstrate that the proposed framework achieves a high classification accuracy of nearly 97%, surpassing other state-of-the-art models, such as long short-term memory networks, 1-D convolutional neural networks, and Transformers. In addition, the proposed model excels in accuracy metrics, with a precision of approximately 98%, an F1-Score of 91%, and a recall of 94% in crop yield prediction. © 2024 IEEE.

关键词： Distribution transformers

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Virtual reality graded exposure therapy for phobia and PTSD 6

Virtual reality graded exposure therapy for phobia and PTSD

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6th Parul International Conference on engineering and Technology, PICET 2024

作者： Menon, Adith Adwaidh, C.R. Joju, Anoysius Rithik, V.R. Krishnadas, J. Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur India

ISBN: (纸本)9798350369748

By combining virtual reality technology with graded exposure therapy, Virtual Reality Graded Exposure Therapy (VRGET) transforms the treatment of anxiety disorders. VRGET uses controlled exposure to anxiety-inducing stimuli using immersive virtual environments, addressing the global problem of anxiety disorders. With a method that mimics real-world circumstances, people can methodically face their fears in a supervised, safe environment. Situations that are adaptable to meet certain needs. Safety comes first since VRGET makes exposure therapy risk-free in the real world. With its potential across multiple mental health domains, it presents a promising treatment option for PTSD and phobias. This abstract emphasizes VRGET’s transformational significance in changing anxiety treatment and lays the groundwork for a thorough investigation of the technology. © 2024 IEEE.

关键词： Virtual environments

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Impact of Variable Sample Size on the Efficiency of Support Vector Machines in Cardiovascular Disease Detection 2

Impact of Variable Sample Size on the Efficiency of Support ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sarraju, Vijayalakshmi Pal, Jaya Kamilya, Supreeti Computer Science and Engineering Department Birla Institute of Technology Lalpur Ranchi Mesra India Computer Science and Engineering Department Birla Institute of Technology Ranchi Mesra India

ISBN: (纸本)9798350383867

Exploring the relationship between sample size and the performance of machine learning models in diagnosing cardiovascular diseases (CVD) aids in understanding the optimal data volume required to achieve reliable and accurate diagnostic predictions. This research focuses on examining how the size of the sample influences the efficacy of a particular machine learning algorithm, Support Vector Machine (SVM) in detecting cardiovascular diseases. The efficacy of the SVM models was assessed by employing datasets with different sample sizes. These include one original dataset and two statistically representative samples with sample sizes of 170 and 208 respectively. We evaluate multiple metrics, including F1-score, accuracy, specificity, sensitivity, precision, AUC and ROC curve. Based on the findings, it is evident that a larger sample size generally leads to improved precision and specificity. However, it is important to note that a decrease in sensitivity often accompanies this improvement. In addition, the study highlights the importance of using multiple performance metrics to comprehensively assess the efficacy of SVM models. ©2024 IEEE.

关键词： Support vector machines

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Enhancing Fire Safety: Encoder-Decoder Based Detection and IoT-Driven Response System 2

Enhancing Fire Safety: Encoder-Decoder Based Detection and I...

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2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Roy, George Tonmmoy Atik, Atiqul Islam Rabby, Masudur Rahman Islam, Md. Repon Department of Computer Science and Engineering Khulna University of Engineering and Technology Khulna Bangladesh

ISBN: (纸本)9798331508227

Innovative technologies are crucial for improving fire detection and response systems. An embedded system has been developed to improve fire detection and response by integrating a user mobile application, an admin website, and an IoT-based robot. The mobile application uses the Encoder-Decoder architecture to identify fires from user-captured images, enabling real-time transmission of critical data via the Firebase Realtime database. The encoder-decoder model performs better with an accuracy of 97.50%, precision of 97.20%, recall of 97.10%, and F1-score of 97.05%. The admin website serves as a centralized management platform, utilizing Google Maps API for real-time robot tracking and video streaming from the robot’s camera. The system also employs fire and person detection(YOLOv4) algorithms for enhanced monitoring. The robot’s construction includes fireproof materials for resilience in fire-prone environments. This comprehensive system offers a robust solution for effective fire detection and response, improving safety and efficiency in emergencies. ©2024 IEEE.

关键词： Video streaming

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Design and Development of Block Chain Technology Based Small Scale Institutional Voting System 5

Design and Development of Block Chain Technology Based Small...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Nirmala, M. Giraddi, Chandralekha V New Horizon College of Engineering Department of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331530013

Today most of our daily activities are led by the Internet like banking transactions, shopping, communication, transportation, and so on. Without the internet and its applications, we could not envision our lives. However, many areas need the introduction of the Internet. E-voting has been one such subject of research for many decades to provide a secure, transparent system. Developing a secured voting system is a challenging task as it includes many variables like vote tampering, transparency, security, scalability, etc. The introduction of blockchain technology has made us think about its incorporation into the voting system. Blockchain technology enables transparency, an immutable and secure system for conducting elections. Many implementations of voting systems exist some may fail in usability and others may in scalability, transparency, or security. Unlike existing blockchain voting systems, this implementation prioritizes real-time results, scalability, privacy, and layered access control. The system is being implemented using the Ethereum smart contract framework and meta mask tool. Research shows the Ethereum framework is the most popular choice with 34.91 % portion of the utilized framework. The proposed system focuses on automated winner declaration, decentralized voter registration, pending registered voter approval, and a user-friendly interface. This system has distinguished functionalities based on roles like Admin and User. © 2024 IEEE.

关键词： Ethereum

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mL-mB LBP Fused Net: Multi-layer-Multi-block LBP Image Fusion Facial Expression Recognition 5th

mL-mB LBP Fused Net: Multi-layer-Multi-block LBP Image Fusio...

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5th International Conference on Electrical and Electronics engineering, ICEEE 2024

作者： Mukhopadhyay, Moutan Ghosh, Ankush Shaw, Rabindra Nath Dey, Aniruddha Department of Computer Science & Engineering MAKAUT West Bengal Kolkata India Chandigarh University Punjab Mohali India Department of Computer Science & Engineering MSIT West Bengal Kolkata India

ISBN: (纸本)9789819791118

Facial emotion recognition (FER) systems have gained popularity due to their applications in various fields, including healthcare, cognitive science, video conferencing, and driver safety. However, recognizing facial expressions automatically from images presents challenges due to inter-person variability and the difficulty in accurately distinguishing between different emotions. The research investigates the application of a modified Local Binary Pattern (LBP) in facial emotion detection, focusing on the multilayer multi-block local binary pattern (mL-mB LBP). The process involves converting facial expression images into 3×3 identical blocks, which are then used to generate center pixels using the LBP descriptor block-wise. The final layer is partitioned into 3×3 blocks to generate central pixels. The study introduces a customized convolutional neural network (CNN)-based framework for emotion recognition, which uses the mL-mB LBP image data to accurately predict emotions. The CNN model is trained using multi-level multi-block images. The experimental study findings show that the accuracy of the CNN model is significantly improved when trained using multi-block multi-level images with all layers fused. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image fusion

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Leveraging Support Vector Machines for Detection of Network Traffic Attacks 4

Leveraging Support Vector Machines for Detection of Network ...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Tiwari, Anurag Indian Institute of Technology School of Artificial Intelligence & Data Science Jodhpur India Babu Banarasi das Institute of Technology& Management Department of Computer Science & Engineering Lucknow India

ISBN: (纸本)9798350364729

Network security is crucial to ensure the protection of information systems in an increasingly connected world, and network traffic attack detection is an important aspect of maintaining system integrity. This study addresses this by investigating the use of a powerful and flexible machine learning algorithm, namely Support Vector Machines (SVM), to detect network traffic attacks. As a result, SVM has been successfully applied in different domains like network security, etc. In this study, SVM is applied for normal and malicious network traffic classification, including several attack types like Denial-of-Service (DoS), User-to-Root (U2R), and Remote-to-Local (R2L) attacks. To this end, the KDD Cup 99 dataset is employed for training and testing the model. These metrics are then used to evaluate the performance of the SVM-based intrusion detection system. The results demonstrate that SVM is able to recognize a high level of accurateness on network data traffic attacks as well as it has performed well in a difficult attack trends. In this case, the F1-scores for the Linear and RBF did result in 96.65/99.24, respectively, indicating that SVM also proved to be an effective algorithm choice in this example. Finally, the study either propose new algorithms or improve existing algorithms for SVM and demonstrate their effectiveness in the area of network security. In conclusion, this study reinforces the importance of SVM as an effective instrument in the detection of attacks on network traffic and strengthening cyber defense capabilities. © 2024 IEEE.

关键词： Network intrusion

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TechHarvest: Advancing Agriculture through Digital Innovation for Sustainability 15

TechHarvest: Advancing Agriculture through Digital Innovatio...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Singh, Pankaj Pratap Sharma, Utkarsh Rathore, Utkarsh Shukla, Vibhav Patel, Yash Kapoor, Shashwat Department of Computer Science and Engineering IMS Engineering College UP Ghaziabad India

ISBN: (纸本)9798331300579

The revolutionary potential of a technology project intended to alter Indian agriculture. Using *** and MongoDB for backend operations and Kotlin Java for frontend development in Android Studio, the suggested application is a ground-breaking platform for the peer-to-peer trade of surplus farming equipment. In keeping with the forward-thinking projects of the National Bank for Agriculture and Rural Development (NABARD), the application is designed to tackle important issues that farmers, especially small and marginal ones, confront. The search carefully examines the novel aspects and capabilities of the program, highlighting its strong authentication mechanism, intuitive user interface, and safe payment gateway. The study looks into how the application might help with global agricultural difficulties in addition to its local effects. By encouraging effective resource usage, the application appears as a key instrument in addressing the increasing need for food, given that the world's population is predicted to reach 9.7 billion by 2050. As the study develops, it seeks to offer a thorough examination of the evolution, significance, difficulties, and prospects brought about by the convergence of P2P lending and blockchain technology. By carefully examining each topic area, the research aims to add to the body of knowledge in academia as well as offer insightful information for the real-world implementation of these game-changing technologies in important fields influencing our socioeconomic structure. © Grenze Scientific Society, 2024.

关键词： Agricultural Productivity Exchange Platform Farming Equipment Immutability Intermediaries Security Transparency

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