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

作者： Singh, Pardeep Chaudhary, Vipin Kumar Bhatia, Arvind Kumar Sharma, Gaurav Kumbhawat, Amit Singh, Suchita Lovely Professional University Dept. of Computer Science Engineering Punjab Phagwara India Buddha Institute of Technology Dept. of Computer Science Engineering Uttar Pradesh Gorakhpur India

ISBN: (纸本)9798350385199

Globally, heart disease poses a significant challenge to public health. Early identification is crucial for effectively managing and treating heart conditions. Machine learning has shown promising results in predicting medical problems for the past few years. In this study, A system was developed that is capable of detecting cardiovascular diseases using machine learning methods. The project consists of Data loading, Exploratory data analysis, preprocessing, training, evaluation and storage. A dataset was introduced that contained an array of characteristics related to the cardiovascular health status (such as age, gender, cholesterol level.). However, training measured the accuracy of 3 algorithms: K Nearest Neighbours (KNN), Decision Tree, and Random Forest in identifying heart ailment. The article also returns an ensemble graph that considers the forecasts of both these models together, saving the best GNB model for future use. Results showed that the machine learning methods are proficient in heart problem identification, as in this hybrid model, with a 97.32% accuracy rate. © 2024 IEEE.

关键词： Adversarial machine learning

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Gauging Deep Learning Archetypal Effectiveness in Haematological Reclamation

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SN computer Science 2024年第7期5卷 963页

作者： Pradeep kumar, B.P. Ravikumar, J. Dept. of Computer Science and Design Atria Institute of Technology VTU Karnataka Bangalore India Dept. of Computer Science and Engineering Atria Institute of Technology VTU Karnataka Bangalore India

This paper project an system for reclamation of medical images in an effective way for the extraction and retrieving of data from large archives is critical. This study aims to improve haematological image reclamation by combining visual-based image reclamation (VBIR) approaches with the GLCM algorithm, specifically for characteristics related to the prostate gland. While previous research has focused on computer-aided methods for Gleason grading, our technique focuses on early-stage feature reclamation across 14 paired glands. The experimental findings, including Bicubic Interpolation The resolution Enrichment with a standard deviation of 12.37, kurtosis of 122.83, and skewness of 2.92, demonstrate improved reclamation performance. Our suggested methodology uses a handmade learning approach that includes critical components such as GLCM contrast and correlation. This extensive feature extraction step includes descriptors for morphology, texture, fractals, and contextual information, which are then subjected to in-dept. statistical analysis to identify significant features. Using an RNN, our technique improves performance somewhat but significantly when compared to alternatives, with time required for execution offering insights into the intricate interaction of stages and the effect of sample variability. These findings not only encourage more research, but also help to advance standard reviews in haematological analysis of images. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gland reclamation Gray-level co-occurrence matrix (GLCM) Haematological Haematological image Prostate gland

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Enhancing Traffic Control Strategies through Dynamic Simulation and Reinforcement Learning 3

Enhancing Traffic Control Strategies through Dynamic Simulat...

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

作者： Sebastian, Alona Mary Athulram, K.R. Michael, Colin Sunil, Diya Anna Preetha, K.G. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350375190

In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic control systems cannot adapt to dynamic traffic conditions, causing inefficient traffic flow and inefficient use of road infrastructure. In this paper, we propose a novel method to enhance traffic management strategies through dynamic simulation and reinforcement learning (RL). A simulation framework is presented that models the complex interactions among vehicles, traffic signals, and infrastructure in a realistic urban environment. Within this framework, RL agents are deployed to learn adaptive traffic control policies that minimize congestion and optimize traffic flow. This approach uses the ability of RL to continuously learn and adjust to shifting traffic circumstances, which results in improved efficiency and responsiveness compared to static or rule-based control systems. Finally, evaluate the effectiveness of the approach through extensive simulations and demonstrate its capability to significantly reduce congestion, decrease travel times, decrease average waiting time and enhance overall traffic management. By integrating dynamic simulation and RL-based control strategies, the proposed approach offers a guaranteed solution for addressing the challenges of urban traffic congestion and advancing the field of intelligent transportation systems. © 2024 IEEE.

关键词： Reinforcement learning

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Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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A Comparative Study on Keyword Extraction and Generation of Synonyms in Natural Language Processing

A Comparative Study on Keyword Extraction and Generation of ...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Dhala, Rasmi Rani Pavan Kumar, A.V.S. Panda, Soumya Priyadarsini Giet University Dept. of Computer Science and Engineering Gunupur India Sit Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350339369

Natural Language Processing (NLP) is an emerging field that aims to enable machines to understand and interpret human language. Keyword extraction and synonym generation are essential tasks in natural language processing. They play a significant role in information retrieval, text classification, and sentiment analysis. In this paper, we explore three different approaches to keyword extraction and synonym generation: rule-based model, statistical model, and extreme learning machine (ELM) model. We compare the performance of each method on a corpus of text and analyze the strengths and weaknesses of each approach. Our results show that the ELM model outperforms the other two methods in terms of accuracy and efficiency. © 2023 IEEE.

关键词： Neural network models

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Mental State Report From Social Media Activity

Mental State Report From Social Media Activity

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ajit, Akshay Mathew, Crispin Denny, David Johns Devananth, H. Dhanya, P.M. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798331540685

This paper is about making user experience with social media better, by developing the complete analysis tool that would parse textual data from different platforms like Instagram, Facebook or Reddit. By developing a custom browser extension, the user-generated content is captured in real-time and can also be processed subsequently. The project comprises two key components: Emotion Detection and Hate Speech Detection. The Emotion Detection system uses sophisticated Natural Language Processing NLP techniques, such as BERT models to identify users' emotional state through their social media posts. This information is then used to develop a daily mental state report by which users are able to gain insights into their emotional health over time. Further, the Hate Speech Detection system leverages machine learning algorithms to detect and mark words that may be harmful or offensive in user- generated content. This feature enhances positive and inclusive Internet environment. The project goes beyond the analysis;it includes a pragmatic component suggesting specific tasks for users depending on their mental state reports. These recommendations are aimed at promoting positive interaction and enhancing the user's overall well-being. This project offers a comprehensive insight into users' emotional experiences on social media using state-of-the-art NLP and machine learning approaches. The combination of task recommendations and text summarization increases interaction among users, creating a pleasant atmosphere where people can enjoy using social media. The browser extension is a multipurpose tool that does not merely analyze but provides some improvements to the user's mental health and online activity. © 2024 IEEE.

关键词： Emotion Recognition

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BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Semantic Segmentation of X-Ray Images of Human Hand Bone 3

BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Seman...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Venkatesh Nagaraju, Y. Darshan, D. Sahanashree, K.J. Nagamani, P.N. Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Multiclass segmentation of hand bones in X-ray images is crucial for various medical applications, including diagnostic assistance and surgical planning. This study presents BoneSegNet, a deep learning-based segmentation model designed to classify hand bones into seven distinct anatomical classes: Distal phalanges, Intermediate phalanges, Proximal phalanges, Metacarpals, Carpals, Ulna, and Radius. Our model, BoneSegNet leverages the strengths of both convolutional neural networks (CNNs) and transformer encoders incorporating the UNet architecture. Our model architecture begins with patch and positional embeddings of the input image, followed by a series of transformer encoder layers to capture long-range dependencies and contextual information. The transformer encoder is equipped with multi-head attention mechanisms and multi-layer perceptron's (MLPs) with GELU activation and dropout for regularization. Skip connections at specific layers are stored and later utilized in the decoder. The decoding path consists of a series of deconvolution and convolution blocks designed to progressively upscale the feature maps while incorporating skip connections from the encoder. This hierarchical structure ensures detailed spatial information is preserved and enhanced. The final output layer employs a 1x1 convolution with a sigmoid activation function to produce the multiclass segmentation map. The model is trained and evaluated on a dataset of hand X-ray images. It achieves an average accuracy of 99.25%, an average Dice score of 0.81, an average mIoU score of 0.70, sensitivity of 0.79, and specificity of 0.997. These results demonstrate the model's effectiveness in accurately segmenting and classifying hand bones, making it a valuable tool for medical professionals. © 2024 IEEE.

关键词： Semantic Segmentation

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WebTrek Learner : AI Integrated Cloud Based Learning Platform

WebTrek Learner : AI Integrated Cloud Based Learning Platfor...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent systems and Communications, COSMIC 2024

作者： Bhandari, Arjun Harisha, A. Rishav, Kumar Shifana, Mariyam Sameer, Jazil Sahyadri College of Engineering and Management Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331517892

In an era defined by the rapid proliferation of digital technology and connectivity, the need for virtual education has never been more critical. This article, referred to hereon as 'WebTrek Learner,' is a forward-thinking initiative aimed at catering quality virtual education through a dynamic and engaging approach of gaming. To further enhance the learning experience, WebTrek Learner incorporates cutting-edge LLM based AI technology to curate internal insights and content based on glossary documents. Cloud based a gaming platform enables lightweight experience to all potential learners. © 2024 IEEE.

关键词： Adversarial machine learning

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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Nirnayaak: Revolutionizing Legal Research 4

Nirnayaak: Revolutionizing Legal Research

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Datta, Soumyajit Chakraborty, Arya Bhattacharya, Saptarshi Samui, Amit Chakraborty, Anurag Dasgupta, Kousik Kalyani Government Engineering College Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350389128

Nirnayaak is a novel and advanced legal software application, that is meticulously designed to expedite the retrieval of crucial legal information in an Indian Context, addressing the challenges posed by a growing caseload and inaccuracies in the legal system. Employing a groundbreaking architectural framework, it leverages OCR, Smart Text preprocessing, keyword extraction, a sophisticated LLM-based summarization and information retrieval and a feedback-based keyword ranking technique. This amalgamation of techniques enhances the accuracy and accessibility of legal data, enabling legal professionals, scholars, and the public to access pertinent information seamlessly just using natural language queries. We conducted benchmarks on popular keyword extraction techniques, demonstrating that our preprocessing techniques enhance their performance. Additionally, we evaluated LLMs to ascertain their proficiency in understanding contextual nuances. © 2024 IEEE.

关键词： Application programs

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