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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Amal Krishna, P. Balendu, M.S. Rahul Varma, U. Department of Computer Science and Applications Amrita Vishwa Vidyapeetham Kollam India

ISBN: (纸本)9798331300579

Opinions are statements that reflects people's perception or *** This paper we have done sentimental analysis using two major NLP models - VADER and RoBERTA using sentiment *** study begins with an overview of RoBERTa regarding its pre-training and optimization enabling dynamic data collection. Similarly, the legal status of VADER is explained in terms of being based on dozens of opinions and predetermined rules. We are testing these models using two different dataset customers for evaluating the accuracy and precision of each model in predicting the sentiment of text. The results are analyzed and compared to know the problems and weakness of the *** tests include training and refinement of RoBERTa's emotional data list model which uses transformer model to process input sequences, while VADER relies on pre-generated lexicon *** methods are used to compare the performance of two models and identify their strengths and weaknesses in different situations. © Grenze Scientific Society, 2024.

关键词： Economic and social effects

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AI-Powered Business Revolution: Elevating Efficiency and Boosting Sales through Cutting-Edge Process Re-engineering with Machine Learning 2

AI-Powered Business Revolution: Elevating Efficiency and Boo...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nair, Sooraj S. Nathiya, R. Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India

ISBN: (纸本)9798350381665

The integration of Artificial Intelligence (AI) with Business Process Re-engineering (BPR) has revolutionized modern businesses. This research delves into the background, methods used, results achieved, and concluding remarks of this trans-formative amalgamation. Background: Traditional BPR principles have evolved with AI, offering unprecedented efficiency and innovation potentials. Methods Used: Automation, data analysis, natural language processing, and machine learning algorithms are employed to enhance processes and customer experiences. Result Finding: The synergy between AI and BPR leads to increased efficiency, innovation, and competitiveness, improving cost-effectiveness, customer experiences, and response times. Concluding Remarks: Meticulous planning and adept management are essential for leveraging AI in BPR effectively. Adopting chat bots, virtual assistants, and intellectual computing enhances customer support and problem-solving capabilities. This research highlights the establishment of intelligent, customer centric businesses capable of adapting to various challenges. © 2024 IEEE.

关键词： Efficiency

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Exploring the Impact of Different Learning Layers within Recurrent Neural Networks on Crop Yield Prediction 2

Exploring the Impact of Different Learning Layers within Rec...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Bala Krishnan, V. Gokila, S. Hindustan Institute Of Technology & Science Department Of Computer Applications Chennai India

ISBN: (纸本)9798350381665

Objective: Modern farming practices have found that agriculture data analytic is a strong instrument that is altering the way farmers analyze and manage crop production. With the aid of this initiative, farmers will be able to determine the yield of their crops prior to cultivation, assisting them in making the appropriate selections. The study aims to optimize the efficiency and accuracy of agricultural yield prediction by including RNNs in the pipeline for analysis. Methodology: The application of Recurrent Neural Network (RNN) algorithms for agricultural data analytic and crop yield estimation is examined in this article. RNNs use the skills of sequential data processing to examine and model the complex temporal patterns found in agricultural information. The experiment on analysis all the three levels of RNN memory to find fix the optimal model to handle the multiple crops of various season.. By employing thirteen identical crop yield data from the past and present. Findings: Through extensive experimentation on agricultural datasets encompassing diverse crop types, geographic regions, and environmental conditions. The 8-layer model produces the best and optimal prediction of the crops with 98% accuracy. © 2024 IEEE.

关键词： Recurrent neural networks

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Unveiling Emotions: Exploring Sentiment Analysis on Flipkart Data with the Power of Support Vector Machines and the Intricacies of Random Forest Algorithm 2

Unveiling Emotions: Exploring Sentiment Analysis on Flipkart...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Aishwarya Bharathy, B. Princy Suganthi Bai, S. Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381665

E-commerce platforms have reshaped consumer shopping, emphasizing customer experience and satisfaction. This study compares sentiment analysis techniques employing Support Vector Machine (SVM) and Random Forest Algorithms on a Flipkart dataset. User-authored reviews and ratings are analyzed to categorize sentiments as positive, negative, or neutral. The research evaluates data pre-processing methods and measures their efficacy through accuracy, precision, recall, and F-1 score. Findings reveal Random Forest showing a slight advantage over SVM with a marginal deviation of 0.43%, where their individual accuracy rates provision are 94.03% and 93.60% respectively. These results highlight the strengths and limitations of each approach, providing valuable insights for businesses to enhance customer satisfaction and drive growth in the fiercely competitive e-commerce landscape. © 2024 IEEE.

关键词： Support vector machines

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Decongesting Terminal Stations and Sections over Indian Railways by Circular Rail Operations 3

Decongesting Terminal Stations and Sections over Indian Rail...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Goyal, Rajnish Kumar Ramachandran, P. Rakhee Hoda, M.N. Indian Railways Mumbai India Bharati Vidyapeeth's Institute of Computer Applications and Management Department of Computer Science and Applications New Delhi India

ISBN: (纸本)9798331518592

Train operations all over the world started on a point-to-point basis with short duration stoppages in between for passengers to board and deboard and for attending to commercial, operating or passenger requirements. That is still the case except for some light metro type urban rail networks operating in some cities. Efforts have been made worldwide to increase the number of lines, remodel yards, and introduce technology such as Communication based train control (CBTC) and electronic interlocking to increase the number of trains that can operate, despite the fact that the number of trains has multiplied and those sectional capacities utilization is above 100%. But the pattern of operation has been the same. A typical train travels between stations A and B with both A and B acting as starting and destination stations in forward and return journeys. Every train takes hours to get ready for the journey in either direction. Also, many times these source/destination stations are located in heavily congested rail networks. In this study, we have developed an alternative philosophy for running trains that utilizes computerization, linked networks, other routes, and technological developments to run trains on circular tracks with a single stop serving as both the source and destination station. Passengers are allowed to book their seats from any station to any station in between. The time table proposed has been taken from the special on demand trains that ran in 2023. Thus, effectively, the congestion on the destination station and section in forward journey (and starting station/ section in return journey) has been reduced to almost zero. The case study in this paper is for Mumbai Division, Central Railway. © 2024 IEEE.

关键词： Railroad transportation

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Comparative Study of Ensemble Stacking Method for Neurodegenarative Disease Classification 11

Comparative Study of Ensemble Stacking Method for Neurodegen...

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11th International Conference on Soft Computing and Machine Intelligence, ISCMI 2024

作者： Raseena, P.A. Deepika, M.P. Sabu, M.K. Cochin University of Science And Technology Department of Computer Applications Cochin India

ISBN: (纸本)9798331518127

Neurodegenerative diseases present a current challenge in the accuracy of the diagnosis. Alzheimer's and Parkinson's disease take a number of years to accurately define the growth of the disease. Neural networks are very efficient algorithm to handle the image data. This paper proposes an ensemble stacking method for the classification of Alzheimer's, Parkinson's, and Normal using MRI images. And also compare the performance with other neural networks such as deep neural networks, convolutional neural networks, and VGG16. The proposed model got a better accuracy of 94% than other neural network models. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-Time Ragging Notification Using Machine Learning Algorithms and MediaPipe 4

Real-Time Ragging Notification Using Machine Learning Algori...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Dhanesh, R. Shanthi, H.J. Hindustan Institute of Technology & Science Chennai Department of Computer Applications India

ISBN: (纸本)9798350349900

Ragging is disorderly practice of senior students harassing their juniors that violates rules of the institutions. Addressing critical issues such as ragging, suicidal ideation, and medical emergencies within college campuses necessitates the integration of innovative technological solutions. The utilization of innovative technologies such as MediaPipe and machine learning algorithms plays a pivotal role in addressing critical issues like ragging, suicidal ideation, and medical emergencies within college environments. This research paper integrates OpenCV, machine learning algorithms, MediaPipe, including support vector machine, random forest, k-nearest neighbour, and support vector machine, to develop a model for hand tracking and key point recognition. Through the deployment of a camera-enabled device, real-time footage is captured and compared against the trained model. Predictions and classification is detected from hand gestures, and when the sign matches the designated sign then it triggers an alarm and sends a screenshot of the person who needs help. This ensures prompt alerts authorities to take necessary actions in response to identified situations, thereby enhancing campus safety and welfare. © 2024 IEEE.

关键词： Decision trees

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MAIDS: malicious agent identification-based data security model for cloud environments

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Cluster Computing 2024年第5期27卷 6167-6184页

作者： Gupta, Kishu Saxena, Deepika Gupta, Rishabh Singh, Ashutosh Kumar Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung80424 Taiwan School of Computer Science and Engineering The University of Aizu Aizuwakamatsu965-0006 Japan Department of Computer Science The University of Economics and Human Sciences Warsaw01-043 Poland Department of Computer Applications SRM Institute of Science & ampTechnology Delhi-NCR Campus Modinagar Uttar Pradesh Ghaziabad201204 India Department of Computer Science and Engineering Indian Institute of Information Technology Bhopal Bhopal462003 India

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agent’s behavior and predicting the malicious agent before granting data. The performance of the proposed model is thoroughly evaluated by accomplishing extensive experiments, and the results signify that the MAIDS model predicts the malicious agents with high accuracy, precision, recall, and F1-scores up to 95.55%, 95.30%, 95.50%, and 95.20%, respectively. This enormously enhances the system’s sec

关键词： Forecasting

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Advancing Fake Profile Detection: A Machine Learning Perspective 4

Advancing Fake Profile Detection: A Machine Learning Perspec...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Goyal, Bharti Gill, Nasib Singh Gulia, Preeti Maharshi Dayanand University Department of Computer Science & Applications Rohtak India

ISBN: (纸本)9798350349900

The development of Internet has made individuals' lives simpler and more convenient in many ways. People from different parts of the world communicate with one another through a variety of social media platforms that are accessible online. Users of these platforms contribute data in order to share significant moments and recollections from their lives. The quantity of data stored on these sites continues to increase, which brings with it a rise in the potential threats to users' privacy. There have been several attempts by cybercriminal actors to hack into these websites and steal sensitive information. In order to accomplish this mission, attackers create duplicate user accounts, known as fake profiles. These fake profiles harm society by spreading fake news, rumors, stealing user personal information, defaming celebrities, asking for money for funds etc. So, identification of fake profiles is the most pressing need of society, in terms of the protection of private data. This paper discusses various machine learning algorithms used in fake profile detection by different researchers, and a comparison of these techniques with their results has been made for aiming at identifying fake profiles. In order to accomplish this task, two Instagram dataset, Dataset1 Instagram fake and real accounts dataset ***, and Datase2 Instagram fake spammer genuine accounts dataset *** are used. The results obtained by both the datasets are compared using fifteen algorithms. The findings demonstrate that the result vary for same algorithm on different datasets. Voted Perceptron leads on dataset1, while SMO leads on dataset 2 in terms of Precision, Recall, F1-Score and Accuracy. These findings encourage us to conduct additional research in the related field. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multimodel Accurate Classification of Brain Tumor Based on Deep Learning Techniques 2

A Multimodel Accurate Classification of Brain Tumor Based on...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Dino, P. Ashwin Lakshmanan, S. Hindustan Institute of Technology and Science Department of Computer Applications Chennai India

ISBN: (纸本)9798350365269

A brain tumor is a highly persistent disease, with over 84,000 people expected to receive a primary brain tumor diagnosis in 2023, resulting in an estimated 24,000 deaths from meningioma tumors in the same year. Magnetic resonance imaging (MRI) is the most effective technique for detecting brain tumors. Neural networks have demonstrated remarkable accuracy in solving image classification and segmentation problems. In this paper, we present a multimodal accurate classification based on neural networks to identify and classify brain tumors into glioma, meningioma, pituitary, or no tumors. Using different optimizer models has been integral to achieving higher accuracy, resulting in MobileNetV2 and MobileNetV3 consequently producing improved results for the multi-class tumor classification model. The proposed model exhibits significant performance improvements and excellent validation accuracy. © 2024 IEEE.

关键词： Tumors

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