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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Pandey, Trilok Nath Navya Kala, S.V. Amrutha, S. Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350308167

The intent of customer segmentation is primarily to aid institutions to custom-make their marketing techniques and methods to suit the specific requirements of every consumer group. As this helps to solve the segregating of the groups of customers for banks efficiently and helps provide more custom-made services for customers of each group better, applying the customer segmentation for banking data can provide many insights that can be beneficial to banks as well as their customers. This increases the effectiveness of customer interaction and can maximize selling opportunities. It makes marketers more efficient in terms of time, money, and other resources. © 2023 IEEE.

关键词： Customer segmentation Gaussian mixture model clustering hierarchical agglomerative clustering hyper parameter tuning k-modes

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Blockchain Technology: Reshaping Telemedicine and Telehealth Services for Emergency Management

Blockchain Technology: Reshaping Telemedicine and Telehealth...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Singh, Neelam Mittal, Varsha Rawat, Vandana Kumar, Priyansh Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Uttrakhand Dehradun India Graphic Era Deemed to Be University Dept. of Computer Applications Uttrakhand Dehradun India

ISBN: (纸本)9798331508685

Both consumers and healthcare providers have benefited greatly from the quick development of software-based technology. The dominance of blockchains is impending as the medical care sector moves quickly towards digitization and customization. The development of blockchain provided answers to several problems that had long plagued the healthcare sector. However, not only in healthcare due to its capabilities, blockchain technology has found great success in a variety of industrial fields. The important factors propelling the growth of the healthcare blockchain market are the rise in data breaches, privacy concerns, fake pharmaceuticals, and provided answers to numerous problems that had long plagued the healthcare sector. Although telemedicine had been developing for a while, the covid-19 epidemic gave it the necessary kick. In order to facilitate social isolation, the pandemic made it necessary to expand virtual healthcare and distant therapy. Over the past year, the telemedicine market has experienced rapid expansion. In the current study, the adaptation issues and prospective prospects for blockchain technology in the telehealth and telemedicine sectors are *** on a systematic review, we have determined the existing state of the use of blockchain technology in a number of healthcare-related areas, the ideal state that would be achieved, the research gap regarding the usage of blockchain technology in a number of healthcare-related application areas, as well as a number of potential research agendas for future studies. © 2025 IEEE.

关键词： Telemedicine

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A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

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computer Modeling in engineering & Sciences 2024年第5期139卷 1357-1379页

作者： Zhiwei Lin Hui Wang Tianding Chen Yingtao Jiang Jianmei Jiang Yingpin Chen Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province School of Physics and Information EngineeringMinnan Normal UniversityZhangzhou363000China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA College of Material Engineering Fujian Agriculture and Forestry UniversityFuzhou350000China

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and *** Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion *** satisfactory results in practical scenarios remains *** response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial *** approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free *** enhance convergence speed,the Q-learning algorithm in RL is augmented with ***,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection ***,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power *** efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target *** results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target ***,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.

关键词： Reverse path planning Dyna-Q bidirectional search posture angle joint motion

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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

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Big Data Mining and Analytics 2023年第1期6卷 32-43页

作者： Kalyan Kumar Jena Sourav Kumar Bhoi Soumya Ranjan Nayak Ranjit Panigrahi Akash Kumar Bhoi Department of Computer Science and Engineering Parala Maharaja Engineering CollegeBerhampur 761003India Amity School of Engineering and Technology Amity UniversityUttar Pradesh 201303India Department of Computer Applications Sikkim Manipal Institute of TechnologySikkim Manipal UniversitySikkim 737102India Directorate of Research Sikkim Manipal UniversityGangtokSikkim 737102India

As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time ***,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical *** this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud *** is trained marvelously on cloud images with an impressive amount of prediction *** time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset *** satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its *** proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

关键词： satellite images satellite image classification cyclone prediction Deep Convolutional Neural Network(DCNN) features layers down-sampling process

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Analysis of Important Facts between Heart Disease Features using Machine Learning Classifiers 14

Analysis of Important Facts between Heart Disease Features u...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Pal, Gyanendra Kumar Gangwar, Sanjeev Department of Computer Science & Engineering VBS Purvanchal University Jaunpur India Department of Computer Applications VBS Purvanchal University Jaunpur India

The heart disease is very dangerous disease so diagnosis expert cannot easily manage the effects of risk factors. Machine learning algorithms provide way to predict complex problem in dataset. In this paper, we have simplified dataset by two different features selection methods as Lasso Regression and Random Forest. The Lasso algorithm easily reduces the weak features and prepared strong important collection of features. The Random Forest randomly selects the important features and reduces dataset complexity. In this experiment, we have used Random Forest as predictor algorithm and test dataset model on different K-Fold cross validation as K=2, 4, 6, &. 10., and analyzed simplify dataset by Lasso and Random Forest. Finally, we find Random Forest perform better on dataset which is simplified by Random Forest features selection method. In our experimental results, the proposed Random Forest model performs excellent works in terms of accuracy and ROC. The Random Forest approach with Random Forest features selection calculated classification accuracy as 99% and ROC values calculated 93% on 10-fold cross validation. © Grenze Scientific Society, 2023.

关键词： Classification (of information)

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Effective Bandwidth based Distributed DoS Attack Detection and Source Identification using Maximum Flow Techniques 3

Effective Bandwidth based Distributed DoS Attack Detection a...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Subburaj, T. Suthendran, K. Darshan, P. Deeraj, C. Jai Shankar, M. Pushpalatha, G. Department of Master of Computer Applications Rajarajeswari College of Engineering Bangalore India Department of Information Technology Kalasalingam Academy of Research and Education TamilNadu India

ISBN: (纸本)9798331527549

The DDoS attack is one of the most threatening factors affecting the internet services. Now days, most of the common attacks are targeting the gaming industry, online shopping websites followed by the software and media sectors. Many of the service providers are available to detect and prevent the DDoS attacks. But their software’s are not updated enough to detect the recent time attacks. Cyber security challenges include rising ransomware attacks, AI-powered threats, supply chain risks, and frequent data breaches. In the current IoT trend, available DDoS defenses aren’t sufficient. This work is aimed to propose a novel BW- DDoS attack detection approach based on maximum data flow identification in networks. The proposed method is faster and taking less number of iterations to determine the maximum data flow and also trace out the source of the attacks with minimum time compared with existing approaches. © 2025 IEEE.

关键词： Attacks BW-DDoS Entropy Maximum flow Trace out

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A Review of Moving Object Detection Techniques for Night Time 5

A Review of Moving Object Detection Techniques for Night Tim...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Dua, Disha Bhat, Aruna Girdhar, Ashish Delhi Technological University Department Of Computer Science & Engineering Delhi India Kurukshetra University Department Of Computer Science & Applications Kurukshetra India

ISBN: (纸本)9798350330861

Moving Object Detection in low light conditions is a challenging task due to limited visibility and the presence of noise and objects in the captured images, low contrast, low brightness, low appearance information, and low Signal to Noise Ratio. The majority of night surveillance models suffer from lack of adaptability for different scenes and separation between detection and tracking. This review paper provides a comprehensive survey of recent advancements in Moving Object Detection Techniques for night-time situations. It presents an overview of various approaches proposed in the literature, including traditional and deep learning-based methods, and discusses their strengths and limitations. Moreover, the paper highlights evaluation metrics commonly used in the literature, the datasets they have been evaluated upon, along with a comparative analysis of the performance metrics & various limitations of different methods. Finally, the paper identifies the key research challenges and future directions for Moving Object Detection in Night Time viz. dim-lit environments, such as the need for more robust and efficient algorithms that can handle diverse environmental conditions and real-time applications. © 2023 IEEE.

关键词： Object detection

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Mental Health Predictive Analysis using Multiple Machine Learning Techniques 2

Mental Health Predictive Analysis using Multiple Machine Lea...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Tressa, Neethu Asha, V. Prasad, Arpana Prathith Pramodh Kumar, D. Saju, Binju New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India Adi Shankara Institute of Engineering & Technology Department of Computer Science - Ai Engineering Kerala Kalady India

ISBN: (纸本)9798350369960

The growth in mental health issues and the demand for high-quality medical care have prompted the requirement of machine learning techniques in the psychological research area. For the better prediction of mental health complications, this work presents an up-to-date thorough evaluation of machinelearning algorithms. This article reviews the difficulties, limitations and future directions of using machine learning approaches in psychological health. By searching reliable databases, relevant studies and publications on machine learning methods were collected for predicting mental health related issues. For the completion of this work, 40 research outcomes were analyzed using screening and classification procedures. The analyzed works were categorized according to mental health conditions such as schizophrenia, bipolar disorder, depression or anxiety, PTSD, and mental health conditions affecting children. While discussing the results, the difficulties and limitations that many researchers have encountered when studying mental health related problems were also considered. The paper also encompasses specific directions for future research work that are feasible and the need for utilizing varying approaches in machine learning for psychological health disorder predictions. © 2023 IEEE.

关键词： Decision Tree Classifier Flask Deployment K Nearest Neighbor Classifier Logistic Regression Algorithm Naïve Bayes Random Forest Stacking

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Hybrid Image Captioning Model

Hybrid Image Captioning Model

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2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Panigrahi, Lipismita Panigrahi, Raghab Ranjan Chandra, Saroj Kumar Kiit University Department of School of Computer Applications Bhubaneswar India Soa University Department of Computer Science and Engineering Bhubaneswar India Opju University Department of Computer Science and Engineering Raigarh India

ISBN: (纸本)9781665492942

Image captioning is implemented using Deep learning and NLP (Natural Language Processing) resulting in producing a description of an image. The proposed model generates a caption for an image using a Convolutional Neural Network (CNN) together with a Recurrent Neural Network (RNN) and area of attention. Previously, the image names were used as keys to map the images with descriptions. In order to achieve high performance, in the proposed model the image caption is based on the relationship between the areas of a picture (attention model), the words used in the caption, and the state of an RNN language model. The approach of progressive loading is employed for the loading of the image dataset. Further, for encoding the image dataset into a feature vector, VGG16 a pre-trained CNN is used. The extracted feature vector is given as input to the RNN model. These image encodings are output to a specific type of RNN model known as Long Short-Term Memory (LSTM) networks. Subsequently, the LSTM works on decoding the feature vector and predicts the sequence of words, resulting in the generation of descriptions or captions. The training performance is measured using one of the model's quantitative analysis metrics known as BLEU. © 2023 IEEE.

关键词： Long short-term memory

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An Imperial Analysis of Large Language Models for Automated Tweet Sentiment Prediction

An Imperial Analysis of Large Language Models for Automated ...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Akhouri, Shivam Ajbani, Vidhi Lakshminarayanan, Ritika Pandey, Trilok Nath Nigam, Meher Shrishti Patra, Sudhansu Shekhar Vellore Institute of Technology School of Computer Science and Engineering Chennai India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350313987

Company' s brand perception majorly depends on customer experience and the reviews which follow that. A customer is capable of influencing many more people just on the basis of reviews given by him/her. Google released Pathways Language Model (PaLM) which is a major advancement in Artificial Intelligence (AI). It has been trained with the Pathways System, which allows it to generalize tasks in various domains. In this work, a trustworthy platform is provided for the examination of millions of people's continually moving and changing perspectives. Twitter data is captured, and effective sentiment and data analysis is used to generate trustworthy and helpful info graphics reflecting public opinion. Product sales, stock returns, election outcomes, and other commercial and social events may all be predicted and explained using the information found in tweets. Brands, product manufacturers, and other companies may utilise the information derived from data analysis to better understand their brand image, expand their market share by targeting the relevant demographics at the right moments, and enhance their offerings in terms of both quality and customer service. © 2023 IEEE.

关键词： Attention Units Data visualization Masks Sentiment Analysis Tokenization

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