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12th International Conference on Internet of Everything, Microwave, Embedded, Communication and Networks, IEMECON 2024

作者： Mukherjee, Dipendu Chauhan, Shivam Ghosh, Sagarika Uma Devi, G. Department of Computer Science Engineering University of Engineering and Management Jaipur India Department of Civil Engineering University of Engineering and Management Jaipur India

ISBN: (纸本)9798350387315

Recent adverse climatological conditions paves the way for studying, understanding and predicting capabilities of it. Being a pivotal parameter of climate, Land Surface Temperature (LST) is the ideal and substantial choice for this study. By utilizing the analytical capabilities of remotely sensed LST data and Geographic Information System (GIS), this study utilizes the XGBoost regressor to predict temperatures in Western Rajasthan city, i.e., Jodhpur from 2003 – 2022 on the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor based data. This LST data is retrieved from Aqua and Terra satellite mounted MODIS sensor. Through meticulous preprocessing and fine-tuning, the model's accuracy is significantly enhanced. The findings not only provide valuable insights for climate prediction applications but also contribute to the field of temporal data analysis in machine learning. This unique and novel temporal predictive analysis is founding work for the development of heat mitigation strategies to be developed by urban planners, dwellers, policy makers and stakeholders. © 2024 IEEE.

关键词： Atmospheric temperature

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Proposal and Implementation of Neural Network-Based Approach for Secure Speaker Recognition Using Spectrograms

Proposal and Implementation of Neural Network-Based Approach...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bindlish, Arindam Gujral, Vaibhav Madaan, Rosy Kumar, Praveen Kunskapsskolan Gurgaon India Mriirs Department of Computer Science and Engineering Faridabad India Astana It University Department of Computer Engineering Astana Kazakhstan

ISBN: (纸本)9798331508685

In this era of Artificial Intelligence, research on Speaker Recognition systems has taken the forefront. In this paper, an efficient deep learning pipeline has been proposed. A total of 7500 audio samples from Kaggle's speaker recognition dataset have been used to generate Spectrograms, Mel Spectrograms, and Mel-Frequency Cepstrum Coefficients (MFCC), which in turn have been used to train a ResNet 18 Deep Neural Network from scratch. Using different regularization techniques such as dropout, early stopping, and weight decay, high accuracies were obtained. The results generated from the ResNet by using Mel spectrograms, classic spectrograms, and MFCCs were compared alongside each other to check which one among them gave the best testing accuracy. The proposed approach is designed and implemented with additional feature enhanced by providing a secure feature selection system from audio signals. To implement this, the proposed system is equipped with AES security algorithm on splitting of data set into ratio of 60:30:10. The Classic Spectrograms predicted the speakers with 92.96% accuracy. Mel Spectrograms were comparatively more efficient with 93.75% testing accuracy. The results obtained from the model trained with the MFCC features were the best of all, at 95.2%. Further, the usage of the Advanced Encryption Standard (AES) algorithm has also been proposed for a secure speaker recognition task. © 2025 IEEE.

关键词： Spectrographs

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Deep Learning-Based Classification of Conference Paper Reviews: Accept or Reject? 23th

Deep Learning-Based Classification of Conference Paper Revie...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Prama, Tabia Tanzin Biswas, Al Amin Anwar, Md Musfique Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh

ISBN: (纸本)9783031647758

Peer review plays a crucial role in ensuring that papers published in conferences are as true, valid, and accurate as possible. As part of the publication process, the scholarly work of an academic author undergoes review by domain experts, reflecting the commitment to uphold integrity. With the growing volume of scholarly submissions at the conference, the classification of the paper reviews through an automated classification system has become a pressing requirement. The primary aim of this research is to develop an automated classification system for conference paper reviews into two categories: accept and reject. However, the objective of this paper is twofold: developing an automated classification system and explaining the decision-making process through explainable artificial intelligence (XAI). First, to develop an automated classification system, we present a deep learning-based framework. Next, to explain the results produced by deep learning (DL) models, Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Anchor Explainer have been applied. Among the applied DL techniques, the Bi-GRU-LSTM-CNN model attained the highest accuracy of 95.33% which indicates that the proposed methodology is able to replicate the human decision-making process in an effective manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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A Comprehensive Comparison of Machine Learning Classification Techniques for Early Detection of Alzheimer's Disorder

A Comprehensive Comparison of Machine Learning Classificatio...

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

作者： Vairale, Prathmesh Dubey, Vandana IES-IPS Academy Department of Computer Science and Engineering-AIML Indore452012 India IES-IPS Academy Department of Computer Science and Engineering Indore452012 India

ISBN: (纸本)9798331512965

Alzheimer's disorder (AlzD) is a chronic condition in which brain cells degenerate and eventually die. People over the age of 65 are most likely to get this condition. There is no cure for this, but nowadays proper medications and treatments are available to slow down the disease progression temporarily. Detection of AlzD in its early stage may help doctors and physicians provide proper medicines as early as possible to slow its development. Artificial intelligence is a blooming field in every sector nowadays that can help in AlzD detection to save us from time-consuming and expensive conventional methods. Various machine learning algorithms are researched to prevent AlzD. This paper uses different linear, distance, and tree-based supervised classifier algorithms. The used dataset contains different psychological parameters like a person's age, number of visits, Mini-Mental State Examination (MMSE), and years of education, on which machines can be trained to predict whether the person is demented or non-demented. The main goal of this research paper is to compare these classification algorithms which give the best result in the early detection of AlzD. RFC, KNN, and MLP algorithms outperform all other algorithms with an accuracy of 96.77%, followed by NB and LR algorithms with 95.70%. © 2025 IEEE.

关键词： Random forests

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BAHBench: A Unified Benchmark for Evaluating Bio-Acoustic Health with Acoustic Foundation Models

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4046-4058页

作者： Xu, Weixiang Dong, Zhongren Peng, Jing Wang, Runming Zhang, Zixing Hunan Normal University School of Information Science and Engineering Changsha410081 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University Shenzhen Research Institute Shenzhen518000 China

Acoustic foundation models, through self-supervised learning on large amounts of unlabeled speech data, can acquire rich acoustic representations. In recent years, these models have demonstrated substantial potential in audio-based health-related tasks, remarkably enhancing the efficiency and quality of healthcare services and contributing to the advancement of smart healthcare. However, there is currently a lack of systematic research and exploration on the performance of acoustic foundation models in health-related tasks. Furthermore, inconsistencies in evaluation methods and experimental setups hinder fair comparisons between different methods, severely impeding progress in this field. To address these challenges, we establish a unified Benchmark for evaluating Bio-Acoustic health via acoustic foundation models, namely BAHBench. BAHBench encompasses 6 distinct health-related tasks and evaluates 12 acoustic foundation models within a unified evaluation framework and parameter settings, enabling fair comparisons across different models. Our objective is to explore the effectiveness of current acoustic foundation models in health-related tasks. Thus, we discuss the impact of model size and data diversity on performance, and investigate feature selection and efficient fine-tuning strategy. Experimental results show that different health-related tasks benefit from features from different layers of the foundation model, while LoRA fine-tuning further enhances the model's performance on downstream tasks. Our goal is to provide clear and comprehensive guidance for future researchers. The code related to this study will be available to the research community to promote transparency and reproducibility. © 2013 IEEE.

关键词： Electronic health record

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Performance Analysis of Liver Disease Prediction Using Supervised Machine Learning Classification Models 1

Performance Analysis of Liver Disease Prediction Using Super...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Singh, Ramveer Sharma, Rahul Chaudhary, Shalbha Yadav, Deepika Jain, Jaishree Kumar, Pawan Raj Kumar Goel Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India Raj Kumar Garg Institute of Technology Department of Computer Science & Engineering UP Ghaziabad India R.D. Engineering College Department of Management Studies UP Ghaziabad India Bharati Vidyapeeth's College of Engineering Department of Computer Science & Engineering New Delhi India Teerthanker Mahaveer University Department of Computer Science & Engineering UP Moradabad India Satyug Darshan College of Engineering Department of Computer Science Faridabad India

ISBN: (纸本)9798350367720

Liver is most fundamental part of human body. It is highly dependable to performing several body functions such as helping in food digestion, waste filtering, enzyme activation, detoxification, nutrition and glycogen storage, carbohydrate metabolism, vitamin and mineral storage etc. It has been found from various survey reports that across the world, 58 million people are suffering from liver disorders and nearly 01 million liver disorder patients are present in India. The growth rate of liver disorder patients is increasing rapidly worldwide due to a sharp rise in obesity, eating more junk foods, more alcohol consumption and unhealthy lifestyles. People who have liver disorders problem need medical treatment at the correct time and early disease prediction helps to reduce the number of deaths. The research focuses on designing an intelligent system that helps for prediction liver disorder by using various classification models of supervised machine learning and it also helps to prevent becoming India a global hub of liver diseases by 2030. The research emphasis on comparative performance analysis of different classification algorithms and ensemble methods. It also helps to select best accurate model for prediction of the liver diseases. © 2024 IEEE.

关键词： Self-supervised learning

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Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes 5

Development of Cancer Drug Targets and Potential Anti-Cancer...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Balamurugan, K.S. Ramesh, T. Sathish Kumar, P.J. Surendran, R. Tamilvizhi, T. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets. © 2024 IEEE.

关键词： Mammals

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Face Classification & Recognition Using Stacking Ensemble Learning with Principal Component Analysis Approach 3

Face Classification & Recognition Using Stacking Ensemble Le...

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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Devi, Shivali Saini, Jyoti Kaur, Harjinder Kaur, Harmanpreet Chandigarh University Computer Science & Engineering Punjab India Ssiet Dinanagar Computer Science & Engineering Punjab India Rayat Bahra University Computer Application Department Punjab India

ISBN: (纸本)9798350369083

Developing a high-level automated security system for identification/authentication has long been an active research subject in practically all fields. Traditional security systems utilize a key or a password to secure a process/product, whereas biometric security systems use a person's physical or behavioral attributes. Face patterns play a significant part in several potential recognition/authentication applications because of their uniqueness, universality, dependability, and stability. This study provides a face identification system that starts with eye detection and ends with face image recognition. AN ensemble stacking approach for face classification is presented in this study that combines various Machine learning (ML) algorithms with and without Principal Component Analysis (PCA). The proposed system is trained and tested on a well-known publically available Olivetti Face Dataset. The dataset is split into 75% train and 25% test dataset which is also transformed using PCA. The results show that the accuracy of each base model having PCA data is on par or better than the one without PCA. The paper also proves that the performance of the new stacking ensemble system results in an average or better performance than the other base models. For Olivetti face dataset, the proposed Stacked Ensemble Learning model's accuracy with PCA is 94%, which is on average than the accuracy of the other base models, including Neural Network (NN) (100%), Random Forest (92%), Support Vector Machine (SVM)(96%), Gaussian Naives Bayes(81%), K-Nearest Neighbour (KNN) (89%), and Logistic Regression (LR) (97%) respectively. © 2024 IEEE.

关键词： Authentication

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Mobility-Aware Power Control and User Scheduling for Downlink V2I Networks 100

Mobility-Aware Power Control and User Scheduling for Downlin...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Wang, Yujie Zhou, Momiao Sun, Yanshi Wang, Kan Hefei University of Technology School of Computer Science and Information Engineering Hefei China Xi'an University of Technology Faculty of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798331517786

Vehicle-to-infrastructure (V2I) network is a new paradigm of wireless system with special topology where roadside units (RSUs) are linearly deployed along the roadside and vehicles linearly move on the road. For such system, some classical problems would have new formulations and solutions. We in this paper investigate the joint power control and user scheduling problem for a multi-cell downlink V2I network, the objective of which is to maximize the sum-rate of the network under the signal-to-interference-plus-noise ratio (SINR) constraint of each V2I link. Considering the high mobility of vehicles, the objective function is set as the mean of the sum-rate over a sequence of time slots. For ease of handling, we first decouple the problem into multiple separate subproblems based on the linear distribution of RSUs. Then we employ the quadratic transform technique for fractional programming (FP) to transform the mixed integer nonlinear programming (MINLP) subproblems into convex problems, and obtain the solutions with Branch and Bound method. Finally the validity of our proposed algorithm is verified by numerical simulations. © 2024 IEEE.

关键词： Quadratic programming

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Multi-Modal Emotion Recognition Network with Balanced Audio-Visual Feature Extraction 5

Multi-Modal Emotion Recognition Network with Balanced Audio-...

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5th International Conference on Artificial Intelligence and computer engineering, ICAICE 2024

作者： Chen, Zeyu Wu, Yiming Cao, Ronghui Hunan University College of Computer Science and Electronic Engineering Changsha China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China

ISBN: (纸本)9798331528911

Video and audio are ways for humans to perceive the world beyond language. It is meant to enable robots to imitate and recognize human emotional expressions. However, most current audio-visual emotion analysis models tend to extract deep features from only one modality. In contrast, the other modality plays a supporting role and cannot fully extract deep features from both modalities. This article proposes a balanced audio-visual emotion analysis model. Specifically, EfficientNet and Wav2vec 2.0 are used for visual and auditory modality feature extraction, respectively, ensuring that indepth features can be extracted from both modalities. Secondly, we use Transformer as the decision-level fusion operator, exchanging information between the two modalities. We verified our model on the RAVDESS dataset and achieved a Top1 accuracy of 88.54%, surpassing audio-visual emotion analysis models of the auxiliary type. © 2024 IEEE.

关键词： Emotion Recognition

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