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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Sirigineedi, Manikanta Mohan, R N V Jagan Sahu, Bandita Giet University Department of Computer Science and Engineering Odisha Gunupur India SRKR Engineering College Department of Computer Science and Engineering India

ISBN: (纸本)9798350335095

The process of quantifying fish populations holds significant value in the realm of fisheries management, as it enables a precise evaluation of population sizes and facilitates comprehension of the current state of the fish stock. Nonetheless, the process of manually counting fish is demanding in terms of labor, time, and susceptible to inaccuracies. In order to tackle this issue, there have been advancements in automated fish counting techniques utilising computer vision and deep learning algorithms. The present study introduces an automated fish counting system based on deep learning, which employs a Convolutional neural network (CNN) to identify and enumerate the fish present in an image. The system under consideration has been assessed on a dataset comprising of underwater images that encompass diverse fish species. The findings of the evaluation indicate that the system attains a mean absolute error of 0.5 fish per image. The system under consideration exhibits a high degree of precision in quantifying fish populations across diverse settings, thereby presenting a viable avenue for enhancing fisheries governance. The system exhibits the ability to identify distinct fish species, rendering it appropriate for employment in fisheries management contexts, including stock evaluation and species categorization. In summary, the present study presents empirical support for the efficacy of the deep learning-based automated fish counting system, which has the capability to accurately quantify the quantity of fish in an image. This technology holds significant potential for enhancing fisheries management practices. © 2023 IEEE.

关键词： Fish

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Prediction of Heart Disease using Data Mining and Machine Learning

Prediction of Heart Disease using Data Mining and Machine Le...

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Waseemullah Manzoor, Saba Baig, Mirza Adnan Ned University of Engineering and Technology Department of Computer Science and Information Technology Karachi Pakistan Iqra University Department of Computer Science Karachi Pakistan

ISBN: (纸本)9798350354447

In this new generation, cardiac disease is expanding abruptly. The main reason for spreading this disease is the way of life of human beings, such as poor nutrition, absence of activities, medication, smoking, and so on. Today's heart disease is normally happening in individuals, and 85% of people die due to heart disease. There is an enormous amount of data created every single day in the discipline of medication, which is put away in a clinical data bank. This dataset consists of rough data that consists of conflicting and excess information. There is no question about the well-being care framework;they are extremely wealthy in keeping information and yet exceptionally poor in bringing information. Data mining techniques may help in separating significant information by using data mining procedures such as classification, regression method, clustering, and so forth. Later, when the information gathered in the dataset increases and becomes more complicated, data mining algorithms are utilized. The target of this paper is to investigate the different data mining procedures, specifically Random Forest, logistic regression, and SVM, by using an approved dataset for the prediction of heart disease, which includes various characteristics such as age, sexual orientation, chest pain, blood pressure, hyperglycemia, and so on. © 2024 IEEE.

关键词： ANN Classification Dataset Logistic Regression Random Forest SVM

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Unmasking offensive content: a multimodal approach with emotional understanding

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Multimedia Tools and Applications 2025年 1-24页

作者： Singh, Gopendra Vikram Ghosh, Soumitra Firdaus, Mauajama Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta Bihar Patna801103 India Fondazione Bruno Kessler Povo Trento38123 Italy University of Alberta Edmonton Alberta Canada Department of Computer Science and Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India

The widespread use of social media has brought people from all over the world closer, but it has also become a platform for spreading hate, offensive content, and misinformation. Various forms of media, such as text, images, audio, and video, are used to disseminate information. In this paper, we tackle the task of identifying offensive and non-offensive text in the context of women’s harassment, considering the intensity of the offense. We propose a novel approach that leverages sentiment and emotion, including their intensity, to enhance the performance of this task. To facilitate our research, we constructed a large-scale dataset called the Women and Child-related Sentiment and Emotion-Aware offensive dataset (WCSEoff) to aid in identifying offensive content. Furthermore, we develop a multi-task model that not only detects whether images contain offensive or non-offensive content and quantifies the intensity of offensiveness as the primary task but also classifies the sentiment and emotions present, along with their respective intensities, as the auxiliary task. By adopting this multitasking framework, we achieve a performance improvement of over 7% compared to approaches that do not incorporate sentiment and emotion analysis. Code and data will be available at https://***/~ai-nlp-ml/***#MOSAIC. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Classification (of information)

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On the use of the Total Constraint Violation as an Additional Objective in Evolutionary Multi-Objective Optimization

On the use of the Total Constraint Violation as an Additiona...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Nan, Yang Ishibuchi, Hisao Shu, Tianye Gong, Cheng City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9781665410205

In real-world applications, multi-objective optimization problems (MOPs) usually have multiple constraints. To solve constrained MOPs (CMOPs), various constraint handling techniques (CHTs) were proposed in the field of evolutionary multi-objective optimization (EMO). A simple CHT with high applicability is to use the total constraint violation as an additional objective. The total constraint violation-based CHT transforms a constrained (m-1)-objective MOP to an unconstrained m-objective MOP. This CHT was also used to create a real-world unconstrained multi-objective test suite called RE from real-world constrained problems. Recently, the RE test suite has been frequently used for evaluating EMO algorithms. Only when the additional objective value is zero (i.e., only when the total constraint violation is zero), solutions are feasible in the original constrained MOP. This means that feasible solutions of the original constrained MOP are located on the boundary of the Pareto front of the formulated unconstrained MOP. As a result, the final population of an EMO algorithm on the formulated unconstrained MOP includes many infeasible solutions of the original constrained MOP. This means that good solution sets for the unconstrained MOP are not always good solution sets for the original constrained MOP. In this paper, we propose an improved total constraint violation-based CHT. The core idea is to use not only positive constraint violations but also negative constraint violations. We apply the proposed CHT to real-world constrained MOPs. Experimental results show that the proposed modification improves the quality of feasible solutions obtained by the total constraint violation-based CHT. © 2024 IEEE.

关键词： Multiobjective optimization

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Domain-independent adaptive histogram-based features for pomegranate fruit and leaf diseases classification

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CAAI Transactions on Intelligence Technology 2025年第2期10卷 317-336页

作者： Mohanmuralidhar Prajwala Prabhuswamy Prajwal Kumar Shanubhog Maheshwarappa Gopinath Shivakumara Palaiahnakote Mahadevappa Basavanna Daniel P.Lopresti Department of Studies in Bio-Technology Davanagere UniversityDavangereKarnatakaIndia Department of Studies in Computer Science Davangere UniversityDavangereKarnatakaIndia School of Science Engineering and EnvironmentUniversity of SalfordSalfordUK Lehigh University BethlehemPennsylvaniaUSA

Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for *** the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the *** study presents an adaptive histogram-based method for solving this *** method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture *** approach explores colour spaces,namely,Red,Green,and Blue along with *** histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also *** proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of *** the grey image is the average of colour spaces(R,G,and B),it can be considered a reference *** estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance ***,the method uses an Artificial Neural Network for *** effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different *** results show that the method outperforms existing techniques in terms of average classification rate.

关键词： color spaces distance measure fruit classification leaf classification plant disease classification

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Revolutionizing Diagnostic Precision: The Role of Neural Networks in Advancing Medical Imaging 1

Revolutionizing Diagnostic Precision: The Role of Neural Net...

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1st International Conference on Healthcare Innovations, Software and engineering Technologies, HISET 2024

作者： Raymond, David Goureshettiwar, Palash Kumar, Praveen Faculty of Engineering and Technology Depertment of Computer Science and Medical Engineering Nagpur India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Name of Organization Nagpur India

ISBN: (纸本)9798350360707

The incorporation of neural networks into medical imaging has recently resulted in significant modifications to diagnosis. This article looks at the job of brain networks in clinical picture handling, featuring their enhancements in exactness, speed and consistency across different clinical spaces. The utilization of profound learning models, for example, CNN and RNN has become significant in picture examination, illness recognizable proof, and treatment arranging. The exhaustive review investigates the elements of brain network processes and their applications in different imaging modalities like radiology, pathology and neuroimaging. Also, it tends to the difficulties, development possibilities, and moral contemplations related with conveying brain networks in true medical services conditions. As brain networks keep on changing clinical pictures, this examination fills in as a complete aide, showing their significant effect on further developing diagnostics and reconsidering clinical imaging © 2024 IEEE.

关键词： Convolutional neural networks

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Automated Detection of Heart Arrhythmia Signals by Using a Convolutional Takagi–Sugeno–Kang-type Fuzzy Neural Network

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Sensors and Materials 2024年第2-3期36卷 639-653页

作者： Lin, Cheng-Jian Cheng, Han Chang, Chun-Lung Department of Computer Science & Information Engineering National Chin-Yi University of Technology Taichung411 Taiwan Department of Artificial Intelligence & Computer Engineering National Chin-Yi University of Technology Taichung411 Taiwan

In clinical practice, electrocardiography is used to diagnose cardiac abnormalities. Because of the extended time required to monitor electrocardiographic signals, the necessity of interpretation by physicians, and the vulnerability of electrocardiographic signals to noise interference, electrocardiography is laborious and places a heavy burden on healthcare providers. Therefore, in this paper, a convolutional Takagi–Sugeno–Kang (TSK)-type fuzzy neural network (CTFNN) is proposed to address the challenges of arrhythmia signal classification. The proposed CTFNN is divided into three parts, namely, a convolutional layer, a feature fusion layer, and a TSK fuzzy neural network. The TSK fuzzy neural network is used to replace the fully connected neural network, thereby reducing the number of parameters and enabling the model to mimic the human brain when classifying signals. In addition, because the parameters of the CTFNN are difficult to determine, the uniform experimental design method, which requires only a small number of experiments, is used to determine the optimal parameter combination. The proposed model was tested using the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database, which contains 1000 records belonging to 17 categories. Each record has a duration of 10 s and contains 3600 sampling points. According to our experimental results, the accuracy, recall, precision, and F1-score of the CTFNN for long-term signals were 97.33, 97.96, 96.00, and 96.97%, respectively. In addition, the number of parameters for the proposed model was only 558,728, which was less than that for LeNet (i.e., 1734501). © 2024 M Y U Scientific Publishing Division. All rights reserved.

关键词： Electrocardiograms

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Predicting Disaster Tweets using Enhanced BERT Model 7

Predicting Disaster Tweets using Enhanced BERT Model

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Duraisamy, Premkumar Duraisamy, M. Periyanayaki, M. Natarajan, Yuvaraj KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Government Arts and Science College Department of Computer Science Tirupattur India

ISBN: (纸本)9798350397253

Twitter has become an essential means of communication in emergency situations. The widespread use of smart phones, anyone can immediately report any incident they witness. As a result, more agencies are interested in programmatically monitoring Twitter. Social media sites such as Twitter provide a common forum for sharing and exchanging personal experiences with others. Most of the people share personal experiences, neighborhood problems also the activities on social websites to let unwanted peoples to know. Many rescue organizations constantly monitor this kind of data to detect disasters and reduce the likelihood of casualties. However, humans cannot manually comb through vast amounts of data to uncover threats in real-time. To achieve this, many studies have proposed representing words in a form that computers can understand and applying machine learning techniques to the word representations to determine the sentiment of the text. Previous search techniques provide single embeddings or representations of words from a given document. However, the new Advanced Contextual Embedding is Bidirectional Encoder Representations from Transformers (BERT) method creates different vectors for the same expression with different settings. Although there are no specific studies on how BERT embeddings can help analyze catastrophe-type tweets, these representations have been successfully used in various Natural Language Processing (NLP) tasks. © 2023 IEEE.

关键词： Embeddings

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Analysis of Eye Disease classification by Fundus Images using Different Machine/Deep/Transfer Learning Techniques 4

Analysis of Eye Disease classification by Fundus Images usin...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sharma, Himanshu Wasim, Javed Sharma, Pankaj Mangalayatan University Computer Science & Engineering Uttar Pradesh India Mangalayatan University Department of Computer Engineering and Applications Uttar Pradesh India Eshan College of Engineering Computer Science & Engineering Mathura India

ISBN: (纸本)9798350307757

The fundus image is a crucial diagnostic tool for diseases like diabetic retinopathy (DR) because it catches a retina, a back of an eye. This issue is most prevalent among diabetics and a leading contributor to blindness, particularly in young adults and those engaged in the workforce. Detecting DR in its early stages is crucial in reducing the likelihood of blindness in patients. An ophthalmologist performed DR screening and looked for lesions on the fundus imaging. Intelligent systems have the ability to make early identification of DR more efficient, especially with the recent advancements in machine learning in healthcare. This study compares and contrasts many methods for detecting DR in fundus images. This study lays out methods for improving upon pre-trained architectures such that they can correctly classify fundus images, allowing for early detection and prediction of diabetes. A concatenate model is presented in this comparative study as a method for classifying fundus images using ML and deep/transfer learning. Various model architectures are used, including SVM, Gradient Boosting, Efficient Net, Efficient-Net B4 and B7, MLP, CNN, ResNet, VGG-19, and DNN, among others. The models' accuracy, F1-score, recall, and precision are measured in experimental findings that compare them. Our strategies outperform a single model with results ranging from 84% to 96% accuracy, respectively. They found that our ML- DL-TL methods work well for automated DR classification employing fundus image data in this experiment. © 2024 IEEE.

关键词： Intelligent systems

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An enhanced three factor based authentication protocol using wireless medical sensor networks for healthcare monitoring

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 1165-1186页

作者： Ali, Rifaqat Pal, Arup Kumar Kumari, Saru Sangaiah, Arun Kumar Li, Xiong Wu, Fan Dhanbad826004 India Department of Mathematics Ch. Charan Singh University Uttar Pradesh Meerut250004 India School of Computing Science and Engineering VIT University Vellore Tamil Nadu632014 India School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Department of Computer Science and Engineering Xiamen Institute of Technology Xiamen361021 China

With the rapid growth of wireless medical sensor networks (WMSNs) based healthcare applications, protecting both the privacy and security from illegitimate users, are major concern issues since patient’s precise information is vital for the proper diagnosis procedure. So, authentication protocol is one of the efficient mechanisms to deal with trustworthy and authentic users. Several authentication protocols have been proposed in WMSNs environment. However, the most of these protocols are so susceptible to security threats and not suitable for practical use. In this article, recently proposed Amin et al.’s authentication scheme is reviewed and some vulnerabilities like off-line password guessing attack, user impersonation attack, known session-key temporary information attack, the revelation of secret parameters, and identity guessing attack are pointed out. To overcome all the above mentioned vulnerabilities, we have proposed an enhanced three-factor based remote user authentication protocol in WMSNs environment. Further, the proposed protocol is validated using Burrows–Abadi–Needham logic and then simulated using Automated Validation of Internet Security Protocols and Applications tool. Moreover, the security analysis ensures that the proposed protocol is well protected from various types of malicious attacks. In addition, the performance evaluation shows better efficiency and suitability of our protocol over other related protocols. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Authentication

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