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7th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2022

作者： Swain, K.P. Misra, S. Barik, Shekharesh Samal, S.R. Sahoo, D. Department of Electronics and Communication Engineering GITA Autonomous College Bhubaneswar India Department of Computer Science and Engineering DRIEMS University Cuttack India Department of Electronics and Communication Engineering Silicon Institute of Technology Bhubaneswar India

ISBN: (纸本)9789819950140

Nowadays, data visualizations and data analysis are the most significant fact in providing a quick and clear understanding of the information. An exploratory data analysis of shopping mall customers’ data set is presented in this study to achieve the customers’ segmentation which will beneficial for making marketing strategies. To achieve this, deep data analysis is carried out by extracting specific items from the dataset to know the different groups of customers coming into a mall and their spending behaviors. Depending upon their spending behavior, a machine learning model is created using the K-Means clustering algorithm to serve different marketing strategies for different groups of customers. The developed model can make a business impact to solve similar scenario problems for improving the decision-making related to the best marketing strategies for the customers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data visualization

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Efficient Mobility in Next Generation Heterogeneous Wireless Networks Based on Enhanced Vertical Handoff Approach

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 128-139页

作者： Kaliappan, Vishnu Kumar Padmapriya, T. Manikanthan, S.V. Ilavarasi, S. Suresh Kumar, B. Department of Computer Science and Engineering KPR Institute of Engineering and Technology Arasur Tamil Nadu Coimbatore India Melange Publications Puducherry India Melange Academic Research Associates Puducherry India Tamil Nadu Tiruchengode India Department of Electronics and Communication Engineering The Kavery Engineering College Mecheri Tamil Nadu Salem India

The progression of mobile technologies from first generation (1G) to fifth generation (5G) networks has brought about noteworthy advancement. The present network environment is characterized by the coexistence of various mobile networks and technologies, leading to overlapping or non-overlapping coverage areas, creating a next-generation heterogeneous landscape. Handoffs within the same network (i.e., GSM to GSM or vice versa) are known as horizontal or homogeneous handoffs. Conversely, when handoffs entail a transition to a different network type, such as moving from GSM to CDMA or vice versa, they are categorized as vertical or heterogeneous handoffs. Multiple link triggers, such as link down (LD), link going down (LGD), link detected, and link up, are employed to initiate and execute vertical handoff (VHO) when needed. We have determined the thresholds for vehicular speed and link triggers through extensive simulations, explicitly focusing on high-demand traffic types like video. This paper proposed an enhanced VHO approach based on IEEE 802.21, commonly called media independent handover (MIH), to improve Quality of Service (QoS) parameters, specifically mitigating packet loss (PL), and handoff delay and enhancing the user experience. © 2025 by the authors of this article.

关键词： 5G mobile communication systems

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Generative Artificial Intelligence: Principles, Potentials and Challenges 2

Generative Artificial Intelligence: Principles, Potentials a...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Routray, Sudhir K. Jha, Mahesh K. Sharmila, K.P. Javali, Abhishek Pappa, M. Singh, Monika Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350368413

In recent years, we have witnessed the raise of several generative artificial intelligence (AI) products and services which have strong disruptive capabilities. Majority of these AI-based products and services are also very popular since their public availability. These generative AI products and services have a lot of potentials to do the common office works, writing codes, doing accounting tasks, predicting common patterns, generating creative contents, marketing products and services, increasing efficiencies in the systems, translating to different languages, designing products, and many such things. These effects of generative AI are phenomenal and it is going to start a large scale automation in the product design and service sectors. In addition to the benefits, several challenges are associated with generative AI. Starting from the bias to security risks to fake content creation, there are many challenges with these products and services. In this paper, we go through the basic principles of generative AI and then study its present and future prospects. We analyze its perilous effects and the common challenges with a few examples. Overall, generative AI can be beneficial tool if it is used positively under appropriate regulatory systems. © 2024 IEEE.

关键词： Generative adversarial networks

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The Firstever LTE Network on the Moon: Challenges and Prospects 5

The Firstever LTE Network on the Moon: Challenges and Prospe...

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5th IEEE International Conference on Recent Trends in computer Science and Technology, ICRTCST 2024

作者： Routray, Sudhir K. Pappa, M. Singh, Monika Javali, Abhishek Sharmila, K.P. Tengshe, Richa CMR University Department of Computer Science and Engineering Bangalore India CMR Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798350351378

Mobile cellular networks are very popular across the globe, and they are found at all places where human settlements are present. Long term evolution (LTE) is a popular standard for mobile wireless communications. It has been deployed in almost all the countries. These networks are deployed to support various services, such as voice, image, video, text, and data transmission. In 2024, NASA and Nokia together get ready to deploy an LTE network on the moon. This project was planned in 2020, and NASA selected Nokia as the vendor for this lunar LTE network deployment mission. The LTE network on the moon would help in the enhancement of lunar exploration. The common services of LTE are planned to be deployed on the lunar surface. In addition to that, some moon specific valueadded services will also be deployed to support the exploration tasks. The deployed network would send the pictures, videos, and other sensor measured data from the lunar surface back to earth. This lunar network will be supported by the orbiting satellites and other peripheral networks. In this paper, the basic features and characteristics of the first lunar LTE network and its utilities have been studied. © 2024 IEEE.

关键词： NASA

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Enhancing Blurred Image Processing with IoT Integration for Improved Clarity 2

Enhancing Blurred Image Processing with IoT Integration for ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Tummalapalli, Geetamma Summia Parveen, H. Ramesh, S.M. Monica, P. Santhiya, B. Rajasekaran, Arun Sekar GMR Institute of Technology Department of Electronics and Communication Engineering Rajam India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India GITAM Deemed to be University Department of Electronics and Communication Engineering GST Visakhapatnam India Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India SR University Department of Electronics and Communication Engineering Warangal India

ISBN: (纸本)9798331534950

A blurred image is an image that has undergone a blurring or smoothing effect, resulting in a loss of sharpness and clarity. Blurring is a technique used in image processing to reduce noise, remove unwanted details, or create a visual effect. It involves averaging or blending neighboring pixels to create a smoother appearance. The specific processing techniques and algorithms used will depend on the nature and extent of the blur, the intended outcome, and the available software or programming tools. It's important to experiment with different methods and parameters to achieve the desired result. IoT (Internet of Things) can be used in image processing in several ways to enhance functionality, efficiency, and data processing capabilities enhances image processing by enabling real-time analysis, edge computing capabilities, integration with other IoT devices, and remote monitoring and control. These advancements facilitate applications such as object detection, image recognition, and real-time decision-making, contributing to more efficient and intelligent systems in various domains. Here is a proposed model for blurred image processing with IoT. This proposed model demonstrates the integration of IoT devices, image processing techniques, and cloud-based analytics to address the challenges of blurred image processing. It combines edge processing for real-time analysis and cloud-based processing for more complex algorithms, enabling efficient and intelligent image processing applications in various domains. © 2024 IEEE.

关键词： Edge computing

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Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

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SN computer Science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

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Optimizing Cybersecurity: Leveraging Support Vector Machines for Real-Time Threat Detection 1

Optimizing Cybersecurity: Leveraging Support Vector Machines...

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1st International Conference on Innovations in communications, Electrical and computer engineering, ICICEC 2024

作者： Dubey, Ankit Kumar Dubey, Rohit Kumar Shukla, Anil Dubey, Poornanand Baderia Global Institute of Engineering and Management Department of Computer Science and Engineering Jabalpur India Baderia Global Institute of Engineering and Management Department of Engineering Mathematics Jabalpur India Baderia Global Institute of Engineering and Management Department of Electronics and Communication Engineering Jabalpur India

ISBN: (纸本)9798350376517

In the current digital era, that is marked by the enhanced use of information technology, there are also enhanced threats which are in the form of cybercrimes. This research deals with enhancing the cyber security through Support Vector Machines (SVMs) in threat detection in real time. SVMs are a class of machine learning algorithms favored for the accuracy and efficiency they provide when it comes to the classification processes;they would be very useful in fast and accurate finding of potential threats. This forms the basis of the study under which the authors propose a new approach using SVMs in a real time data mining system that can quickly detect anomalous activities across networks. Using the detection rates for different further commonly used machine learning models, we show the advantages of our concept to the development of SVMs in view of detection accuracy, velocity and resource demand. The findings demonstrate that the use of SVM based system can alleviate the false positive incident and improve the security situation of organizations. This research extends the field of cybersecurity by developing a customizable and transferable model that can be deployed in different contexts to strengthen existing defenses against the emerging and evolving cyber threats. © 2024 IEEE.

关键词： Adversarial machine learning

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Leveraging U-Net Variants for High-Precision Flood Mapping: A Performance Evaluation on the ETCI Dataset 2

Leveraging U-Net Variants for High-Precision Flood Mapping: ...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thirugnanasammandamoorthi, Puviyarasi Ghosh, Debabrata Dewangan, Ram Kishan Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Bennett University School of Computer Science Engineering and Technology India

ISBN: (纸本)9798331534950

Flood mapping is a crucial task in mitigating the adverse impacts of flooding by providing accurate spatial information about inundated areas. This study explores flood mapping using the ETCI dataset, leveraging the U-Net model and its enhanced variants. Initially, a basic U-Net architecture is employed to generate flood maps, followed by progressively improved versions incorporating batch normalization, dropout, and attention mechanisms. These modifications aim to enhance model generalization, robustness, and feature extraction capabilities. For the purpose of evaluating the model's performance, metrics like accuracy, F1-score, and Intersection over Union (IoU) are utilised. The generated flood maps demonstrate the potential of advanced U-Net architectures for precise and efficient flood delineation, providing a valuable tool for disaster management and planning. © 2024 IEEE.

关键词： Disaster prevention

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A robust sparse representation algorithm based on adaptive joint dictionary

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 430-439页

作者： Ying Tong Rui Chen Minghu Wu Yang Jiao College of Information and Communication Engineering Nanjing Institute of TechnologyNanjingChina College of Electrical and Electronic Engineering Hubei University of TechnologyWuhanChina Department of Statistics University of TorontoTorontoOntarioCanada

Sparse representation based on dictionary construction and learning methods have aroused interests in the field of face *** at the shortcomings of face feature dictionary not‘clean’and noise interference dictionary not‘representative’in sparse representation classification model,a new method named as robust sparse representation is proposed based on adaptive joint dictionary(RSR-AJD).First,a fast lowrank subspace recovery algorithm based on LogDet function(Fast LRSR-LogDet)is proposed for accurate low-rank facial intrinsic dictionary representing the similar structure of human face and low computational ***,the Iteratively Reweighted Robust Principal Component Analysis(IRRPCA)algorithm is used to get a more precise occlusion dictionary for depicting the possible discontinuous interference information attached to human face such as glasses occlusion or scarf occlusion ***,the above Fast LRSR-LogDet algorithm and IRRPCA algorithm are adopted to construct the adaptive joint dictionary,which includes the low-rank facial intrinsic dictionary,the occlusion dictionary and the remaining intra-class variant dictionary for robust sparse *** conducted on four popular databases(AR,Extended Yale B,LFW,and Pubfig)verify the robustness and effectiveness of the authors’method.

关键词： facial recognition feature extraction noise dictionary robust regression

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