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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Jafar, Sana Pranveer Singh Institute of Technology Dept. of Computer Science and Engineering Kanpur India

ISBN: (纸本)9798350385199

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why;parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar. © 2024 IEEE.

关键词： Concurrency control

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Comprehensive Women's Health Risk Assessment Platform 2

Comprehensive Women's Health Risk Assessment Platform

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Navya, S. Saha, Sharmistha Nagarajan, Deepa Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350349221

The goal of this research is to develop a machine learning-based method for predicting several diseases in women, with an emphasis on polycystic ovarian syndrome (PCOs), diabetes, heart disease, and breast cancer. Timely and accurate prediction of these disorders is essential for early intervention and prevention due to their impact on women's health and prevalence. Predictive models are trained using a wide variety of medical data, including genetic markers, lifestyle factors, demographic data, and clinical test findings. To find patterns and connections between these predictors and the incidence of diseases, machine learning methods are used, such as decision trees, random forests, and support vector machines. The models are able to forecast with accuracy the probability that a woman would develop one or more of these diseases by efficiently obtaining and evaluating data from these datasets. Metrics including accuracy, precision, recall, and F1-score are used to assess the models' performance;the outcomes demonstrate the models' promising levels of predictive power. The creation of a system like this can facilitate targeted screening and preventive measures, assist medical practitioners in identifying high-risk individuals, and eventually lead to better patient outcomes. © 2024 IEEE.

关键词： Diseases

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Quality Assessment for 3D Coloured Point Clouds in Autonomous Vehicles 1

Quality Assessment for 3D Coloured Point Clouds in Autonomou...

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1st International Conference on Pioneering Developments in computer Science and Digital Technologies, IC2SDT 2024

作者： Bhatia, Sagar Brindha, M. National Institute of Technology Dept. of Computer Science and Engg. Tiruchirappalli India

ISBN: (纸本)9798350365016

This paper proposes a novel no-reference (NR) technique for evaluating the quality of 3D colored point clouds, notably for autonomous vehicle applications. Our technique intends to provide an automated and dependable framework for precisely measuring point cloud data quality by using color information, hence improving the assessment process's capacity to detect small deviations and abnormalities. Unlike existing approaches that rely heavily on geometric data, our methodology incorporates color information, which considerably enhances discrepancy detection and gives a thorough quality rating. Our proposed technique uses sophisticated deep learning models, specifically Convolutional Neural Networks (CNNs), to enhance the feature extraction and regression stages. By employing CNNs, we improve the model's capacity to detect detailed patterns in point cloud data, hence enhancing the accuracy and dependability of quality predictions. The technique is intended to be applicable across several domains, supporting high-fidelity applications such as entertainment, industrial quality control, and autonomous cars. It guarantees the consistency and dependability of input data, allowing for more accurate environmental perception and decision-making. The study also benchmarks against full-reference (FR) 3D-QA measures and compares the proposed NR methodology to existing methodologies. © 2024 IEEE.

关键词： Convolutional neural networks

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Developing a Website to Analyze and Validate Projects Using LangChain and Streamlit 2

Developing a Website to Analyze and Validate Projects Using ...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Pillai, Mukesh Thakur, Pallavi Sardar Patel Institute of Technology Dept. of Master of Computer Applications Mumbai India

ISBN: (纸本)9798350327533

This research study provides a new approach to project analysis and validation through the creation of a website using LangChain, a framework for building applications powered by Language Models, and Streamlit which is used to create LLM powered and Generative AI web applications quickly. In an era of rapid technological change, the necessity for effective project assessment tools has become increasingly crucial. The goal of this research is to overcome this obstacle by utilizing LangChain's natural language processing and understanding capabilities. The proposed website serves as a basic platform for project validation, making it easier to assess project feasibility, technical requirements, and work allocation. It serves as a starting point for developing a comprehensive system. The features of LangChain allow for the extraction of valuable insights from project data, technology, and team experience, resulting in a more automated and intelligent decision-making process. The developed website encompasses two main functionalities: individual and team project validation and task allocation. For individual projects, users can input project details and technologies, and their resume after which LangChain processes the information to determine feasibility and generate technical specifications based on the assessment of their resume. On the other hand, the team task allocation feature, utilizes LangChain to analyze frontend and backend specifications, and project requirements to intelligently allocate tasks based on team's experience level. © 2024 IEEE.

关键词： Feasibility Analysis Generative Artificial Intelligence (AI) LangChain Large Language Mode (LLM)l Project Analysis Streamlit Task Allocation Validation

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Lightweight Attention Based Deep CNN Framework for Human Facial Emotion Detection from Video Sequences

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SN computer Science 2025年第1期6卷 1-12页

作者： Kant, Krishna Shah, Dipti B. Computer Science Dept. P.G Department of Computer Science and Technology Sardar Patel University V.V. Nagar Gujrat Anand 388120 India

Emotions with their intensities are associated with the action of humans which decides the behaviour of an *** recent research has gained enormous attention in the domain of emotion detection due to automatic facial emotion detection. The prime goal of facial emotion recognition (FER) is intended to map different facial expressions from video sequences to specific emotional states. Human facial emotion recognition (HFER) has captivated the attention of researchers for its excellent real time application which serves the society. This research work has proposed a novel lightweight attention based deep convolution neural framework for human facial emotion detection from video sequences on CK + dataset. A dense convolutional network (DenseNet-201) has been applied to strengthen feature propagation to find a solution to the vanishing-gradient problem, and significantly lower the parameter *** provide the model for better visual perceptibility, an attention block is applied between the convolution and dense layer. The proposed model achieves good accuracy of 98.8% while using less memory and processing in comparison to state-of-the-art approaches to reach good performance on CK + data set. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention network Deep convolution neural network DensNet-201 Emotion detection Human face detection Intensity Video surveillance

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A Futuristic Optical and Wireless Convergence Network Towards 6G and Beyond 4

A Futuristic Optical and Wireless Convergence Network Toward...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Molefe, Mlungisi Sibiya, Khulekani Nleya, Bakhe Durban University of Technology Dept. of Electronic and Computer Engineering Durban South Africa

ISBN: (纸本)9798350395914

Emerging use cases and stringent requirements for 6G and beyond networks require new quantum leaps in mobile communication due to the demands of higher throughput, extensive bandwidth requirements, ultra-low latency and massive number of connected devices. The advancement in wireless communications and the advancement in optical networks will be essential in addressing the demands for future mobile networks by providing an innovative infrastructure for 6G and beyond networks. To support stringent requirements for 6G networks and beyond, new innovative technologies and systems are required that will combine optical and wireless networks. An innovative approach of converging optical and wireless networks promises to improve the network into new heights by creating a smart world with beyond-human cognitive communication capacity and speed, improved network scalability and enhanced energy efficiency. Research has shown that a promising architecture to achieve this goal, and cater for extremely high bandwidth and extremely low latency requirements is based on an All-Photonics Network. Therefore, in this paper, we propose an All-Photonics Network for the future to address bandwidth limitations, latency issues and massive connectivity. Furthermore, we propose an evolved infrastructure or architecture converging optical and wireless networks. Potential benefits for the evolved infrastructure are also discussed. © 2024 IEEE.

关键词： Bandwidth

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Skin Lesion Classification using Deep Learning Architectures 2

Skin Lesion Classification using Deep Learning Architectures

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Gothivrekar, Siddhi Khanvilkar, Anuj Nandi, Utsab Singh, Kamal Maktum, Tabassum Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350372892

Skin cancer, originating from diverse dermatological conditions, can be classified based on texture, structure, and color. Identifying lesions from images aids in pre-diagnosis;expanding beyond melanoma, early detection of lesions like Actinic Keratosis and basal cell carcinoma is crucial for timely intervention. Recently, deep convolutional neural networks (DCNNs) have shown promising results in dermatological analysis. This paper presents a comparative study of various DCNNs for the categorization of skin lesions. The proposed method involves fine-tuning the popular architectures such as the VGG16, Inception V3, Inception ResNet V2, and DenseNet 201 trained using the HAM10000 dataset. The paper investigates fine-tuning just the top layers and all the layers of the models, aiming to identify the most effective approach. According to the work's experimental findings, fine-tuning the complete trained model performs significantly better. Moreover, the study finds that fewer epochs and less computational time are required for fine-tuning the entire model while achieving higher accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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Detection of Cardiovascular Disease from ECG Image Using Optimized Weighted Average Ensemble Machine Learning Technique 26

Detection of Cardiovascular Disease from ECG Image Using Opt...

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Hasan, Md. Nahid Hossain, Md. Ali Akash, Ahsan Sadek Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Bangladesh Pabna University of Science & Technology Dept. of Computer Science & Engineering Bangladesh

ISBN: (纸本)9798350359015

cardiovascular diseases (CVD) stand as a prominent global cause of death in worldwide. Timely identification and intervention are paramount in dealing with heart disease, as its failure to detect and treated promptly can lead to life-threatening outcomes. The Electrocardiogram (ECG) Signals are mainly used as diagnostic tools that routinely assess the electrical and muscular function of the heart. Until recently, ECG records were predominantly maintained in paper form;thus, manually examining and re-examining the ECG paper records are often proved time-consuming and laborious tasks. Therefore, this research work utilizes, the power of machine learning and deep learning in predicting four different cardiac abnormalities named Abnormal Heartbeats (AHB), History of Myocardial Infarction (HMI), Myocardial Infarction (MI), and Normal Heartbeats (NHB) from paper-based ECG images. Different image pre-processing methods have been employed to improve the quality of individual ECG images and a novel lightweight deep neural model is proposed for extracting prominent features from each ECG images. Finally, we used an optimized weighted average ensemble technique for the final prediction. The grid search technique is used to find the optimal parameters and weights of each machine-learning model. Our proposed model shows state-of-the-art results in accuracy, precision, recall, and f1-score of 99.29%, outperforming the existing work. The model has attained an Area Under Curve (AUC) score of 99.96%. © 2023 IEEE.

关键词： Cardiovascular disease convolution neural network (CNN) deep learning ECG image feature extraction image classification machine learning weighted average ensemble

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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

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Hybrid Music Recommendation System combining Neo4j GraphDB based and ML-based Approach 4

Hybrid Music Recommendation System combining Neo4j GraphDB b...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Sathyajit, Prithviraj Thakur, Pallavi Sardar Patel Institute of Technology Dept. of Master of Computer Applications Mumbai India

ISBN: (纸本)9798350349856

Due to the significant expansion of the internet, particularly the World Wide Web, there is now an extensive collection of music accessible online. Recognizing the vastness of this musical landscape, it has become imperative to go beyond traditional search functionalities and introduce a service that not only facilitates user searches but also provides tailored music recommendations. This research paper presents an innovative approach to music recommendation by combining machine learning (ML) techniques with graph database technology. The proposed system utilizes ML to generate recommendations based on audio features extracted from a vast music dataset. The recommendation engine employs a clustering pipeline and cosine distance metrics to identify songs with similar characteristics. Additionally, the system leverages the capabilities of Neo4j, a graph database, to store and query relationships between songs deemed similar by their audio features. The integration of Neo4j enhances the recommendation process by allowing the system to consider not only audio feature similarities but also graph-based relationships. This hybrid approach aims to provide users with more diverse and contextually relevant music recommendations. The paper outlines the architecture of the system, including data preprocessing, ML model training, and the incorporation of Neo4j for storing and querying music relationships. Evaluation results demonstrate the effectiveness of the combined ML and graph-based recommendation system, offering a promising direction for advancing personalized music recommendation systems. This research contributes to the intersection of ML and graph databases in the field of music recommendation, offering a novel perspective on enhancing recommendation quality through diverse data representations. © 2024 IEEE.

关键词： Music

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