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2nd International Conference on Computer, Communication and Control, IC4 2024

作者： Tayubi, Iftikhar Aslam Pawar, Dilip N. Kiran, Ajmeera Reddy, Pundru Chandra Shaker Sharma, Nipun Chitra, D. King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Bharati Vidyapeeth College of Engineering Instrumentation Engineering Maharashrra Navi Mumbai India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders. © 2024 IEEE.

关键词： Local binary pattern

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An Accurate Prediction of Coronary Heart Disease Using Ensemble Algorithms 1

An Accurate Prediction of Coronary Heart Disease Using Ensem...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Joshi, Kambham Pratap Prasad, M Lakshmi Natchadalingam, R. Reddy, Pundru Chandra Shaker Mukherjee, Saptarshi Babu, G Charles Hyderabad Dundigal India Institute of Aeronautical Engineering Computer Science and Engineering Telangana Dundigal India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India Maulana Abul Kalam Azad University of Technology M. Tech-Bioinformatics West Bengal India Gokaraju Rangaraju Institute of Engineering and Technology Computing Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350316469

Coronary Heart-disease (CHD) is one the foremost causes of death globally, making the accurate recognition of it vital. Machine-learning (ML) and deep-learning (DL) are two of the newest technologies being put to use in the healthcare industry. This paper uses a risk factor method to examine how CHD can be predicted. K-Nearest-Neighbors, Binary-Logistic Classification, and Naïve-Bayes are only few of the predictive methods that are measured by accuracy, recall, and receiver operating characteristic (ROC) curves for evaluation. Ensemble modeling strategies including bagging, boosting, and stacking are contrasted to these foundational classifiers. The efficacy of using ensemble approaches to enhance coronary heart disease prediction was investigated using a comparative analytical methodology. The 'Cardiovascular-Disease Dataset,' which includes 80,000 records of patient data for CHD, is used to evaluate the modeling techniques. It is demonstrated that bagged models, on average, are more accurate than their non-bagged counterparts by the margin of 1.99 percentage points. The highest accuracy of all models was 76.5%, but the AUC for the boosted models was 0.78. Accuracy of 75.1% was achieved by the stacked model consisting of KNN, random-forest classifier, and SVM. In addition, data-analytic methods and K-Folds cross-validation were used to verify the tested models' performance. © 2023 IEEE.

关键词： Artificial intelligence CHD Ensemble techniques Heart-disease Hybrid modeling Machine-learning

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Enhanced Route Optimization: Incorporating Road Safety Factors for Optimal Path Selection

Enhanced Route Optimization: Incorporating Road Safety Facto...

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International Conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： R Subash Banu Priya Prathaban Ganeshkumar N Gopinathan S Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management GITAM (Deemed to be University) Bengaluru Karnataka India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

Efficient route optimization is a crucial concern in modern transportation systems, influencing travel time, resource consumption, and user satisfaction. By empowering users to select their priority parameters, such as safety or efficiency, this project enables travellers to make eco-conscious choices and select routes that minimize their environmental footprint. Real-time data integration ensures users can bypass traffic jams and accidents, reducing emissions and fuel consumption. Furthermore, the focus on safety factors helps prevent accidents, thereby fostering safer road environments. Existing systems often fall short, providing limited customization and overlooking safety and sustainability priorities. To bridge these gaps, this project introduces an advanced route planning system that empowers users to select their priority parameters, integrates real-time data for up-to- the-minute updates, and prioritizes road safety. It introduces a pioneering approach that enhances the Dijkstra Algorithm by integrating real-time accident rate and travel time data into the route optimization process. By considering these additional factors, the algorithm produces routes that not only minimize distance but also prioritize safety and time efficiency in dynamic real-world conditions. The algorithm's multi-factor optimization considers distance, accident rates, and travel times, enabling the creation of routes that balance these criteria and offer improved decision-making for users through a user- friendly interface. The algorithm's dynamic adaptability, multi- factor optimization, and practical implementation showcase its potential to revolutionize how routes are planned, leading to safer, faster, and more informed travel experiences.

关键词： Analytical models Adaptation models Heuristic algorithms Real-time systems Road safety Safety Planning Optimization Public transportation Accidents

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Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design 4

Application of Explicit Group Sparse Projection in Optimizin...

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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Manukonda, Kodanda Rami Reddy Swamy, Hemanth Sankar, R. Chidambaranathan, C.M. Haribabu, K. Saranya, K. Ibm Department of Consulting India Motorola Solutions India Chennai Institute of Technology Department of Eee Kundrathur Chennai India School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institute of Technology Department of Ece Dundigal Hyderabad India Dhanalakshmi College of Engineering Department of Ai & Ds Tambaram Chennai India

ISBN: (纸本)9798350354218

The growing adoption of 5G networks in diverse domains like IoT, smart cities, healthcare, and industrial automation necessitates advanced optimization techniques. While existing approaches have shown promise, they are often limited by computational intensity, lack of adaptability, and specific focus on geometric constraints. This paper presents EGSP-5G, a novel methodology employing Explicit Group Sparse Projection (EGSP) for 5G network resource allocation and topology design. Its capability to achieve targeted sparsity levels allows for efficient utilization of network resources, thereby making it a versatile and powerful tool for various optimization tasks. Remarkably, EGSP-5G achieved a resource utilization rate of 90.02% and a throughput of 110.32 in networks comprising 1000 nodes. It stands as a robust and versatile solution for optimizing 5G networks across diverse application domains. © 2024 IEEE.

关键词： Network topology

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Enhancing Low-Dose CT Image Quality Through Deep Learning: A DoG-Sharpened U-Net Approach With Attention Mechanism

Enhancing Low-Dose CT Image Quality Through Deep Learning: A...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Zubair, Muhammad Rais, Helmi B Md Ullah, Fasee Yousafzai, Arsalaan Khan Hassan, Farrukh Universiti Teknologi Petronas Department of Computer and Information Sciences Seri Iskandar32610 Malaysia Universiti Teknologi Petronas Department of Civil and Enviromental Engineering Seri Iskandar32610 Malaysia Sunway University Bandar University Sunway Department of Computing and Information Systems School of Engineering and Technology Selangor Darul Ehsan 47500 Malaysia

ISBN: (纸本)9798350372229

Computed Tomography (CT) scans utilize electromagnetic radiation. However, the excessive exposure of a patient's body during CT acquisition poses potential health risks. Subsequently, the integration of low-dose CT scans has resulted in an escalation of noise, artifacts, and a discernible decline in the overall quality of CT imaging, significantly impacting the diagnostic capabilities of Computer-Aided Diagnosis (CAD) system. Removing these noises and artifacts while preserving critical information is a challenging task. Traditional noise reduction algorithms are expensive, produce blurry results, and rely on challenging sinogram data. As a result, deep learning-based image-denoising approaches have emerged. This study introduces a DoG-UNet+ model which incorporates a novel layer called the 'Difference of Gaussians (DoG) Sharpening Layer' into the U-Net architecture. This layer utilizes two distinct convolutional kernels, termed 'fat' and 'skinny,' aimed to capturing diverse scales of features. To increase the clinical detection precision, an attention mechanism has been added to focus on the critical features in CT images. This model has been compared with the most recent algorithms based on the Peak Signal-to-Noise Ratio (PSNR), Root Mean Squared Error (RMSE), and Structural Similarity Index (SSIM). The outcomes showed superior performance of DoG-UNet+ model, showcasing promising and notable results compared to existing methods. © 2024 IEEE.

关键词： Computed Tomography Deep Learning Image Enhancement LDCT Noise Removal

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An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT

An Interpretation of the Challenges and Solutions for Agricu...

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International Conference on computing Methodologies and Communication (ICCMC)

作者： M. Surya S. Manohar Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamilnadu India

Healthcare, the smart grid, and supply chain management are some of the industries where IoI(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services.

关键词： Smart agriculture Supply chain management Costs Supply chains Transforms Blockchains Smart grids

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DropDim: A Regularization Method for Transformer Networks

arXiv

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arXiv 2023年

作者： Zhang, Hao Qu, Dan Shao, Keji Yang, Xukui The School of the Information Engineering University Zhengzhou450000 China Jiangnan Institute of Computing Technology China

We introduce DropDim, a structured dropout method designed for regularizing the self-attention mechanism, which is a key component of the transformer. In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions. In this way, the semantic information can be completely discarded. Thus, the excessive co-adapting between different embedding dimensions can be broken, and the self-attention is forced to encode meaningful features with a certain number of embedding dimensions erased. Experiments on a wide range of tasks executed on the MUST-C English-Germany dataset show that DropDim can effectively improve model performance, reduce over-fitting, and show complementary effects with other regularization methods. When combined with label smoothing, the WER can be reduced from 19.1% to 15.1% on the ASR task, and the BLEU value can be increased from 26.90 to 28.38 on the MT task. On the ST task, the model can reach a BLEU score of 22.99, an increase by 1.86 BLEU points compared to the strong baseline. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Enhancing Earthquake Prediction through Hybrid Deep Learning Approach

Enhancing Earthquake Prediction through Hybrid Deep Learning...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： M. Kandan Swagata Sarkar Meenakshi Sharma V. Rajeswari P. Poonkuzhali Srimathi S Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai India Department of CSE Global Group of Institute Amritsar Information Technology Panimalar Engineering College R.M.D Engineering College Kavaraipettai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

Earthquakes, one of the greatest natural catastrophes, kill people and harm the economy. Heavy earthquakes and tsunamis may kill thousands and cost millions. Globally, predicting major earthquakes is important. Because earthquake prediction is difficult, researchers employ machine learning to improve forecasts. Many countries have early earthquake warning systems to save lives. Temporal rather than geographical characteristics dominated previous research. A similarity between earthquake and non-seismic signals makes real-time earthquake detection with cellphones or IoT devices problematic. This system may change entity interactions using ML and an IoT network. EEWS metrics are tracked using IoT. However, ML can assess these actions and determine smart city risk reduction and catastrophe management strategies. A deep learning approach for continuous earthquake forecasting utilising past seismic events is proposed. This research predicts earthquakes by position and depth using machine learning and deep learning. ConvLSTM, a Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), extracts important waveform elements to make the classifier reliable in earthquake parameters.

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AI Based Model for the Creation of Chatbots to Help in the Instructional Process 2

AI Based Model for the Creation of Chatbots to Help in the I...

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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Anvesh, Kachi Reshmi, Bharati Mahantayya Karthikeyan, R. Ramamoorthi, S. Hussain, S.Faizal Mukthar Vardhaman College of Engineering Department of It Hyderabad India Basaveshwar Engineering College Department of Cs Karnataka Bagalkot India Telangana Hyderabad India School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Mohamed Sathak Engineering College Department of Cse Kilakarai623806 India

ISBN: (纸本)9798331542573

Now-a-days technology advances rapidly. New smartphone apps are popular. From manufacturing to customer assistance, AI is crucial today. Academic Chatbots help children succeed academically, but they're time-consuming to manage. The COVID-19 pandemic and the abrupt transition of education to the internet have made it much harder and more time-consuming for advisers to manage the rising amount of online enquiries. Alexa and Siri are popular chatbots that help people. Most students visit institutions to seek college-related information, which requires manpower to provide visitors with information. So, a chatbot can solve the difficulties. This article allows users to converse with a chatbot anytime, anyplace. This work set up an online chatbot system to help college website users. It uses tools that expose Artificial Intelligence methods like Natural Language Processing. This lets users talk to the college chatbot using natural language input and train the chatbot to give a response using the right Machine Learning methods. The algorithm looks at questions about college and tells the user what it found. © 2024 IEEE.

关键词： Artificial Intelligence Chatbot Dataset Natural Language Processing Pattern Matching

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A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics

arXiv

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arXiv 2024年

作者： Wang, Jiahao Yang, Sikun Koeppl, Heinz Cheng, Xiuzhen Hu, Pengfei Zhang, Guoming School of Computer Science and Technology Shandong University China School of Computing and Information Technology Great Bay University China Department of Electrical Engineering and Information Technology Technische Universität Darmstadt Germany

Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with noisy and incomplete count data. Among these models, Poisson-Gamma Dynamical Systems (PGDSs) are proven to be effective in capturing the evolving dynamics underlying observed count sequences. However, the state-of-the-art PGDS still fails to capture the time-varying transition dynamics that are commonly observed in real-world count time sequences. To mitigate this gap, a non-stationary PGDS is proposed to allow the underlying transition matrices to evolve over time, and the evolving transition matrices are modeled by sophisticatedly-designed Dirichlet Markov chains. Leveraging Dirichlet-Multinomial-Beta data augmentation techniques, a fully-conjugate and efficient Gibbs sampler is developed to perform posterior simulation. Experiments show that, in comparison with related models, the proposed non-stationary PGDS achieves improved predictive performance due to its capacity to learn non-stationary dependency structure captured by the time-evolving transition matrices. © 2024, CC BY.

关键词： Dynamical systems

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