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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Dhondiyal, Shiv Ashish Luthra, Pooja Sharma, Bhupender Rajni, Rajni Uniyal, Ishita Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Chandigarh University Department of Computer Science & Engineering Punjab Mohali India Coer University Department of Computer Science and Application Uttarakhand Roorkee India Guru Jambheshwar University of Science and Technology Department Of Computer Science & Engineering Haryana Hisar India Graphic Era Hill University Department of Electrical Engineering India

ISBN: (纸本)9798331522100

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer. © 2025 IEEE.

关键词： Image segmentation

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Neural Network Strategies for Optimizing Near Field Sensor Array Processing

Neural Network Strategies for Optimizing Near Field Sensor A...

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2024 International Conference on Advances in Computing Research on science engineering and Technology, ACROSET 2024

作者： Yadav, Surendra Sinha, Deepak Kumar Kumari, Sweta Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science Engineering Banglore India Atlas SkillTech University Department of Isme Mumbai India

ISBN: (纸本)9798350388800

In this research, we look at how well using neural network techniques may improve the processing of near field sensor arrays. As the introduction explains, near field sensor array technology is becoming more important across many fields, therefore there is a pressing need for cutting-edge techniques that improve spatial resolution, signal-to-noise ratio, and computational efficiency. The study presents a unique neural network technique for processing near field sensor arrays, capitalizing on the flexibility and learning characteristics of the network to boost performance. The suggested neural network technique is shown to be better in all measures by the findings. It outperforms more conventional approaches, which only managed a score of 0.78, because of its 0.92 spatial resolution. Furthermore, the suggested technique achieves a signal-to-noise ratio of 18.5, which is much higher than the greatest signal-to-noise ratio produced by conventional methods (15.2). The suggested method is more accurate, with significantly reduced mean absolute error (0.027) and mean squared error (0.001) values. In addition, the suggested method's processing speed is competitive, coming in at 3.8 ms whereas more conventional approaches take between 4.2 and 5.0ms. With an efficiency of 95% in computation, it outperforms the efficiency of conventional approaches, which ranges from 82% to 88%. Medical imaging, environmental monitoring, and wireless communication are just a few of the many potential applications highlighted by the results, validating the efficacy of the suggested technology. The findings call for further investigation and cooperation at the crossroads of sensor technology and deep learning, which should lead to novel processing techniques for near field sensor arrays that may be used in a wide variety of contexts. © 2024 IEEE.

关键词： Deep Learning Efficiency Neural Network Optimization Processing Sensor Spatial Resolution Strategy

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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

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computer Modeling in engineering & sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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Blockchain and Machine Learning for Data-Driven Insights in Consumer Behavior Analytics 13

Blockchain and Machine Learning for Data-Driven Insights in ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Rawat, Bhupender Singh Thapliyal, Nitin Pal, Sunil Kumar Mishra, Nikhil Verma, Rohan College of Smart Computing Coer University Roorkee India Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Axis Colleges Department of Computer Science & Application Kanpur India Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350305463

Comparing with neural network method to more known methods, this article assesses their simplicity and effectiveness. Our approach uses new network pruning, interpretability-based regularization, and gradient-based sensitivity analysis. Numerous dataset trials show that the suggested method is more accurate, faster, and more durable than others. Also easier to understand. It turns out that small neural networks may simplify models without compromising accuracy. This comparative study shows how neural networks can handle complex data and make good predictions. These networks will be increasingly accessible in daily life. © 2024 IEEE.

关键词： Sensitivity analysis

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An Efficient AdaBoost and CNN Hybrid Model for Weed Detection and Removal

An Efficient AdaBoost and CNN Hybrid Model for Weed Detectio...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Tiwari, Shivam Srivastava, Anurag Kumar Rathore, Yogesh Singh Vats, Satvik Rastogi, Ashish Kumar Department of Computer Science & Engineering Sharda University Greater Noida India Computer Science and Application Sharda University Greater Noida India

ISBN: (纸本)9798350305708

With the aid of AI-based methods like convolution neural networks, this article seeks to enhance weed detection and removal. A plant that is not wanted and is growing wild, especially one that does so on cultivated land and produces a low crop yield, is referred to as weed. In order to increase production in agriculture, it is essential to have a strong and positive weed identification and removal. This paper suggested a weed control programme for wheat fields indicated on the RGB combination of image elements with essential attributes due to the low weed control rate and availability of inefficient weed removal approaches in the wheat sector. AdaBoost estimation and the CNN model are combined to enhance the performance of the weed distinguishing proof. The approach tests the bounds of the 2-D topographical from RGB images while seeing wheat-like grass weeds. The accuracy of the test results displayed by the algorithm during the process was 94.7%. In comparison to previously employed methods, the combination of both algorithms used provides a high degree of accuracy in the estimation of various types of weeds. © 2023 IEEE.

关键词： Adaboost algorithm Convolutional Neural Network (CNN) Depth features Feature Extraction weed

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MemoChat App: An Advancement in Existing WhatsApp 1

MemoChat App: An Advancement in Existing WhatsApp

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Takkar, Hardik Sharma, Krishna Karun Kumar, Nagadamudi Vijay Jha, Manish Kumar Saraswat, Bal Krishna Tyagi, Himani Sharda University Computer Science and Engineering Department Uttar Pradesh Greater Noida India Sharda University Computer Science and Application Department Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350356816

With over 1 billion users in 180 countries, it's no surprise that WhatsApp is one of the most popular instant messaging apps available today. Whether you're looking to make voice or video calls, or simply share messages, photos, and videos with friends and family, this app is an excellent choice. Not only is it free of charge, but it's also incredibly reliable and easy to use. With its user-friendly interface and convenient features, WhatsApp is the perfect way to stay connected with loved ones, no matter where they are in the world. With regular updates, WhatsApp is becoming even more user-friendly and interactive. Though there may be some issues, solutions are always available. By addressing these problems head-on, WhatsApp can provide a unique and unparalleled messaging experience, setting itself apart from the competition. The future of WhatsApp is bright, and no one can't wait to see what's in store! Have you ever encountered the problem of not being able to send messages to specific members of a group? Unfortunately, with the current system, messages sent to a group are received by everyone in the group. However, there are ways to work around this issue. To access the Include and Exclude options while sending a message, simply click on the Arrow symbol located next to the attachment icon. This will open up a new section where you can choose which members to include or exclude from the message. It's a simple and effective way to ensure that your messages are only sent to the intended recipients. © 2024 IEEE.

关键词： Instant messaging applications

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Recommender System: Bibliometric Analysis of a Decade 3

Recommender System: Bibliometric Analysis of a Decade

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bansal, Saumya Baliyan, Niyati Bharati Vidyapeeth's Institute of Computer Application and Management New Delhi India National Institute of Technology Department of Computer Science and Engineering Kurukshetra India

ISBN: (纸本)9798331518592

With the rise of Internet, plethora of content has been uploaded on various platforms such as social media, e-commerce, e-business websites, etc. As a result, research on recommender system has gained significant attention since the year 2000. Therefore, with an aim to provide a coherent overview of research in the field of recommender systems with respect to sustainability, Web of science database is mined from the year 2014 to 2024. The analysis is done on 7776 documents retrieved based on country, bibliographic coupling, publication year, citations, and publisher. From the analysis, it was found that China contributed highest number of documents. Further, there is linear increase in number of publications over years in the field of sustainable recommender system. This study aims to gather statistical information thereby assisting researchers in analyzing trends and parameters such as publication venue, collaboration, top researchers, etc delving into this research field. © 2024 IEEE.

关键词： Data reliability

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Alzheimer's Disease Prediction from MRI image using Convolutional Deep Neural Network

Alzheimer's Disease Prediction from MRI image using Convolut...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Panda, Sanghamitra Sahu, Rekha Dash, Satya Ranjan Centurion University of Technology and Management Department of Computer Science and Engineering Odisha Bhubaneswar India Silicon University Department of Computer Science and Engineering Odisha Bhubaneswar India KIIT Deemed to Be University School of Computer Application Odisha Bhubaneswar India

ISBN: (纸本)9798350369175

Alzheimer's disease is one sort of neurological disorder which affects the daily activities and it is incurable. But, if it can be detected early, then it may be prevented or made to progress slowly. For detection, deep learning has played a great role now a days. In this paper, we have taken 6400 MRI images of both dementia and non-dementia subjects. The dataset is spilt into ${8 0 \%}$ for training a model and ${2 0 \%}$ for validating and accessing the achievement of the trained model. We have recommended a two-dimensional convolutional neural network architecture in order to apply on the MRI dataset to generate a model for classifying and predicting Alzheimer's disease patients. The model generated using the proposed two-dimensional architecture has given approximately $98 \%$ classification accuracy. Besides, we have presented roc-auc curve, f1-score, recall, support, precision and confusion matrix to show the execution of the model. Again, at training procedure, model loss and accuracy also depicted according to epoch times. The researches already performed on prediction of Alzheimer's disease has given poor performance of ${9 1 \%}$ classification accuracy in comparison to our model. © 2024 IEEE.

关键词： Convolutional neural networks

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Multimodal Attention CNN for Human Emotion Recognition 1

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International Conference on. Cryptology and Network Security with Machine Learning, ICCNSML 2023

作者： Tiwary, Gyanendra Chauhan, Shivani Goyal, Krishan Kumar Department of Computer Science and Engineering Bhagwant University Ajmer India Department of Computer Application Raja Balwant Singh Management Technical Campus Agra India

ISBN: (数字)9789819706419

ISBN: (纸本)9789819706402

The human face is the mirror of the mind. The face generally tells all that is going on in one’s heart and mind. Just by looking at the faces of our known ones, we may easily guess their mood. But many times, when we meet some unfamiliar person, it’s hard to get his or her mood just by looking at their faces. This is just because the person may have a certain facial structure that makes them by default look angry, happy, or sad. So, we need to spend some time with that person to analyse other parameters before concluding their state of mood. The current work proposed a novel approach that integrates facial images with electroencephalography (EEG) signals for facial expression recognition tasks. When attention-based deep CNN analyses the facial traits of the subject, a parallel Long Short-Term Memory (LSTM) network analyses the EEG signals. A late fusion network combines the features extracted from both networks, and finally, a classification network tells about what is the current mood of the subject. Combining multiple modalities for emotion recognition has shown promising results when compared with other state-of-the-art models. There are multiple real-life applications of emotion recognition models, such as Advertisement Industry, Human–Robot Interaction, Automatic Depression Detection, Mood Audio/Video Players, etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Emotion Recognition

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Secure Sentinel Leveraging Machine Learning for Fraud Detection in Blockchain Transactions 5

Secure Sentinel Leveraging Machine Learning for Fraud Detect...

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

作者： Shirley, C.P. Thanga Helina, S. Berin Jeba Jingle, I. Saran, P. Absin, S.J. Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India Kpr College of Arts Science and Research Department of Commerce with Computer Application Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Blockchain technology's decentralized and transparent transaction platforms have changed a number of sectors. However, harmful activities like fraud and money laundering are also drawn to blockchain due to its distributed and unchangeable nature. The distinctive features of blockchain transactions frequently prove to be too much for conventional fraud detection techniques to handle. In order to identify fraud in blockchain transactions, this work suggests "Secure Sentinel,"a machine learning-based method based on the Isolation Forest concept. By separating anomalies in a binary tree structure, the Isolation Forest algorithm is highly effective in detecting abnormalities inside datasets. By utilizing this approach, our system is able to identify transactions on the blockchain that are suspicious or stray from the norm. The Isolation Forest model will be trained using attributes such transaction amount, frequency, source/destination addresses, and transaction time. The project's goal is to improve blockchain networks' security and integrity by offering a real-time fraud detection system. Users, miners, and regulators are among the blockchain stakeholders who can take proactive steps to reduce risks and preserve system confidence by automatically identifying potentially fraudulent transactions. We will use past blockchain data to test our model's performance and determine how well it detects different kinds of fraudulent activity in terms of accuracy, precision, recall, and efficiency. By using machine learning to protect blockchain ecosystems from new dangers, Secure Sentinel is a step in the right direction toward advancing the acceptance and sustainability of blockchain technology. © 2024 IEEE.

关键词： Adversarial machine learning

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