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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Anitha, S. Gnanasekaran, P. B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Chennai India

ISBN: (纸本)9798350300093

Internet has become a rich source of user-generated his/her general opinion for products, services, social events, company strategies etc. This has created a need for emotion mining using sentiment analysis. There is a wealth of information and data available today because technology is the foundation of every business. As a result, a recommendation system is useful for managing this massive quantity of data and rapidly and in accordance with the user' s tastes filtering out the relevant data. This paper describes a system for making movie recommendations. The major goal of this study has been to examine several machine learning techniques to classify a text into positive, neutral and negative emotion polarities. An algorithm used to train a Movie review data by support vector machine (SVM), Logistic regression (LR), XGB classifier models. © 2023 IEEE.

关键词： Sentiment analysis

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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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Hybrid Classifier for Software Defect Prediction by Using Filter-Based Feature Selection Techniques 3rd

Hybrid Classifier for Software Defect Prediction by Using Fi...

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3rd International Conference on Smart Computing and Cyber Security—Strategic Foresight, Security Challenges and Innovation, SMARTCYBER 2023

作者： Sreedevi, E. Sivakumar, S. Nayak, Soumya Ranjan Kumar, Raghvendra Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology SRM Nagar Tamil Nadu Kattankulathur603203 India School of Computer Engineering KIIT Deemed to be University Odisha Bhubaneswar751024 India Department of Computer Science and Engineering GIET University Gunupur India

ISBN: (纸本)9789819705726

Software’s capacity for error-free operation determines how reliable it is. Unfortunately, mistakes can happen at any stage of the software development process. Predicting software flaws early on in the development process is now of utmost importance in the field of software engineering. Using classification techniques, which are a popular strategy for predicting software faults, modules that are illustrated by a group of metrics or code properties are divided into defective or non-defective categories. However, there are low-quality, unreliable, redundant, and noisy data sources that have an adverse impact on the process of observing information and helpful patterns. Researchers must therefore use feature selection techniques to extract pertinent info from massive databases. In feature selection, the redundant and unnecessary features are eliminated in order to focus on the most significant ones. In this paper, we looked in to the impact of filter-based feature selection on classification techniques in software defect prediction. NASA and the Metric Data Program software repository both include publicly available datasets that were used in this study. Principal component analysis (PCA) and CFS was used to assess the dataset’s top discriminator features. The datasets were classified by using hybrid ensemble learning, for hybrid ensemble learning classifiers like DT, SVM, K-NN, NB, and LR were used. The experimental findings showed that it is preferable and should be encouraged to apply feature selection to datasets prior to classification in order to anticipate software defects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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Harnessing the Potential of the Internet of Things: Navigating Choices Between Fog and Cloud Infrastructure in Agriculture 1

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Singh, Malikhan Arora, Kavita Pathak, Sonal Quraishi, Suhail Javed Singh, Anupam Ather, Danish Computer Science and Engineering Modern Institute of Technology and Research Centre Rajasthan Alwar India School of Computer Applications Manav Rachna International Institute of Research and Studies Haryana Faridabad India Department of Computer Science and Engineering Graphic era Hill University Dehradun India Amity University in Tashkent Tashkent Uzbekistan

ISBN: (数字)9789819776030

ISBN: (纸本)9789819776023

In recent years, the Internet of Things (IoT) has become a pivotal force in transforming various sectors, with agriculture being a prominent beneficiary. The integration of smart devices, sensors, and data-driven decision-making tools has led to the emergence of a more efficient and sustainable farming landscape. Central to this transformation is the decision of where and how to process and store the vast amounts of data generated by these devices. This paper delves into the practical implications of fog and cloud computing systems in the agricultural domain taking Delhi NCR and region as a sample area by considering factors like efficiency, scalability, and real-world challenges faced by farmers. The aim here is to provide a comprehensive guide to stakeholders in making an informed choice between fog and cloud infrastructure for their IoT-enabled agricultural endeavors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Smart agriculture

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Revolutionizing Education: Exploring the Potential of Blockchain based Incentive Systems 7

Revolutionizing Education: Exploring the Potential of Blockc...

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

作者： Karat, Gautam Kannimoola, Jinesh M. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Karunagappally Kerala Amritapuri India

ISBN: (纸本)9798350397253

The current education system emphasizes memorization and high test scores, leading to disinterest and a lack of motivation among students. A more comprehensive educational approach that integrates hands-on learning, real-world applications and the development of critical thinking skills is proposed. Decentralization and transparency are two critical issues that impact the education system, and addressing them through initiatives such as increased openness in decision-making processes and accountability can improve the system. This research work proposes using Blockchain technology in education to provide recognition for students' skills, secure and transparent verification of their academic history, and a more engaging and rewarding learning experience. A proof of concept is proposed, an incentive-based skill development platform that uses Ethereum smart contracts and cryptographic tokens to reward students' efforts. © 2023 IEEE.

关键词： Blockchain

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Gesture-Based Virtual Keyboard: Enhancing Human-computer Interaction Through Hand Tracking

Gesture-Based Virtual Keyboard: Enhancing Human-Computer Int...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Singh Yadav, Ajay Pratap Yadav, Ganesh Kumar Nagar, Sheetal Sharma, Brijesh Kumar Rawat, Daksh Bhakuni, Vihan Singh Uttar Pradesh Greater Noida201310 India JSS Academy of Technical Education Department of Computer Science and Engineering UP Noida201301 India IIMT College of Engineering Department of ECE Greater Noida India SRM Institute of Science and Technology Department of Computer Applications Modinagar India Graphic Era Hill University Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542344

In this study, an algorithm is developed that utilizes various OpenCV, cvzone and python libraries to provide a virtual keyboard. The program detects hand gestures, specifically the tips of fingers captured through a webcam, to simulate keystrokes corresponding to buttons on the virtual keyboard displayed on the screen, using Hand tracking module provided by Mediapipe. The virtual keyboard consists of keys of 'QWERTY' format and are represented by a graphical element. When the index finger approaches the corresponding key, it is highlighted and when distance between the tips of middle finger and index finger matches the required distance the corresponding keystroke is generated. The program captures video frames continuously from the webcam, identifies hand gestures, and accordingly updates the virtual keyboard's display. Additionally, the program outputs the text generated in a designated area of the screen using pynput library. The implementation uses object-oriented programming principles, with classes defined for buttons and hand detection functionality. © 2024 IEEE.

关键词： Object oriented programming

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Bacterial Foraging Optimization based Recurrent Neural Network Approach ForIdentification and Classification of Maize Plant Disease 3

Bacterial Foraging Optimization based Recurrent Neural Netwo...

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3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Jayapriya, P. Hemalatha, S. NallamuthuGoundarMahalingam College Department of Computer Science Pollachi India NallamuthuGoundarMahalingam College Department of Computer Applications Pollachi India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Maize is one of the largest food crops after rice and wheat in all over the world. However it has become a vital part in the agriculture to automatically monitor and diagnosis the plant disease in new era. In this paper, a unique maize plant disease identification system has been modelled using the optimization of recurrent neural network through bacterial foraging technique. Further it can be employed to moderate the search space of the classifier and provides better initial information for recognition network. Initially pre-processing and region growing based segmentation has been approved to identify and determine the neighbour pixel of seed points toward combining it in particular region. It is to increase the efficiency of the network by searching and grouping of seed points having common attributes to generate the optimal features for classification. Obtained segmented region has been processed using Recurrent Neural Network in order to capture sequential patterns based on longitudinal data to produce recurrent structures. Finally different classes of the plant disease have been classified and identified with new classic techniques. Experimental analysis using plant village dataset on the proposed model explains the performance measure in rudiments of dice coefficient, similarity and specificity towards disease identification. © 2022 IEEE.

关键词： Recurrent neural networks

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Formative-reflective scheme for the assessment of tourism destination competitiveness: an analysis of Italian municipalities

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Quality and Quantity 2023年第4期57卷 3523-3548页

作者： Grassini, Laura Magrini, Alessandro Conti, Enrico Department of Statistics Computer Science Applications University of Florence Florence Italy Regional Institute for Economic Planning of Tuscany (IRPET) Florence Italy

In this article, we propose a formative-reflective scheme for the assessment of Tourism Destination Competitiveness (TDC) based on a combined use of Partial Least Squares-Path Modelling (PLS-PM) and the method recently proposed by Fattore, Pelagatti, and Vittadini (FPV). TDC is conceived as a construct reflecting the tourism performance of a destination, and several determinants are considered, including endowed resources, created resources, and supporting factors. The proposed scheme is applied to a case study on 1575 Italian municipalities for which the Italian National Institute of Statistics released data on tourist flows. Our contribution is innovative for three aspects: (i) the consistency of the formative-reflective scheme for TDC assessment is discussed on a theoretical basis;(ii) an empirical comparison between PLS-PM and the FPV method is performed;(iii) data with higher granularity than most studies on TDC assessment are employed. Our findings highlight that endowed resources are the primary driver of TDC, followed by created resources and supporting factors, and emphasize that the best ranked destinations are big cities with a multifaceted tourism alongside sea and mountain destinations with cultural attractions. © 2022, The Author(s).

关键词： Composite indicators Model assessment Partial least squares Path modelling Tourism competitiveness

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A Study on Swarm-Based Approaches for Intrusion Detection System in Cloud Environment

A Study on Swarm-Based Approaches for Intrusion Detection Sy...

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International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Nishika Solanki, Kamna Dalal, Sandeep University Institute of Engineering and Technology MDU Rohtak India Department of Computer Science and Applications MDU Rohtak India

ISBN: (纸本)9789819909803

Cloud is an advanced Internet of Things enabled network. The layered and distributed nature of this network opens this network to the public environment and varied kind of users. Bulk of data is stored on centralized system, and large amount of information is communicated in the real environment. This public and large amount of shared data and communication opens the network for intruders. Various intrusion detection systems were designed and implemented on different layers of the cloud network to detect and prevent different kinds of internal and external attacks. In the recent years, various swarm-based methods were integrated within intrusion detection system to optimize the performance. In this paper, a detailed study and review of cloud- and Internet of Things-based intrusion detection systems is presented. The analytical and descriptive study of swarm-based intrusion detection system is provided in this paper. This study identifies that the swarm integration within intrusion detection and classification methods improved the performance and reliability of conventional systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Intrusion detection

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Evolutionary Algorithm Based Task Scheduling in IoT Enabled Cloud Environment

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computers, Materials & Continua 2022年第4期71卷 1095-1109页

作者： R.Joshua Samuel Raj M.Varalatchoumy V.L.Helen Josephine A.Jegatheesan Seifedine Kadry Maytham N.Meqdad Yunyoung Nam Department of Information Science&Engineering CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Cambridge Institute of TechnologyBengaluru560036India Department of Computer Applications CMR Institute of TechnologyBengaluru560037India Department of Computer Science&Engineering Swarnandhra College of Engineering and TechnologyNarasapur534280India Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of Computer Techniques Engineering Al-Mustaqbal University CollegeHillah51001Iraq Department of Computer Science and Engineering Soonchunhyang University31538Korea

Internet of Things (IoT) is transforming the technical setting ofconventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to theIoT enabled models are resource-limited and necessitate crisp responses, lowlatencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentionedchallenges. But the intrinsic high latency of CC makes it nonviable. The longerlatency degrades the outcome of IoT based smart systems. CC is an emergentdispersed, inexpensive computing pattern with massive assembly of heterogeneous autonomous systems. The effective use of task scheduling minimizes theenergy utilization of the cloud infrastructure and rises the income of serviceproviders by the minimization of the processing time of the user job. Withthis motivation, this paper presents an intelligent Chaotic Artificial ImmuneOptimization Algorithm for Task Scheduling (CAIOA-RS) in IoT enabledcloud environment. The proposed CAIOA-RS algorithm solves the issue ofresource allocation in the IoT enabled cloud environment. It also satisfiesthe makespan by carrying out the optimum task scheduling process with thedistinct strategies of incoming tasks. The design of CAIOA-RS techniqueincorporates the concept of chaotic maps into the conventional AIOA toenhance its performance. A series of experiments were carried out on theCloudSim platform. The simulation results demonstrate that the CAIOA-RStechnique indicates that the proposed model outperforms the original version,as well as other heuristics and metaheuristics.

关键词： Internet of things cloud computing task scheduling metaheuristics resource allocation

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