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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Mohamed Fasith, R. Ruby, D. Sathya, V. Umamaheswari, D. Periyar Maniammai Institute of Science & Technology Department of Computer Applications Tamilnadu India Periyar Maniammai Institute of Science & Technology Department of Computer Science Tamilnadu India Periyar Maniammai Institute of Science & Technology Department of Commerce Tamilnadu India

ISBN: (纸本)9798331512088

Freshwater scarcity is a grave concern, particularly along coasts. The majority of coastal areas are plagued by over extracted groundwater, contaminated water, and erratic rainfall, making access to pure water a regular problem. Suppose the cities and towns grow rapidly only the problem becomes worse. However, here lies a ray of hope desalination: the process of converting seawater into potable *** desalination technology can help provide a sustainable water supply for coastal regions in India. The existing desalination projects in cities such as Chennai and Mumbai, how they work, the benefits they bring, and the challenges they face. The aspects that also go beyond technology are economic, social and environmental impacts, such as the energy input for running the plants, the cost of water production, and the impact on marine ecosystems. In this research, urban cities run out natural water plants. Desalination methods are primarily used in many countries that treat saltwater but in India there are no sustainable solutions for renewable desalination that decrease water *** study also introduces a forward-thinking design for a renewable source desalination plant using solar power, wind power and wave energy to reduce its carbon footprint. This design is inexpensive and scalable so that the product can meet the expanding needs of coastal communities without contributing to environmental pollution. The two aspects, a technological and a human approach for desalination, together provide a possible roadmap for these sustainable water solutions benefiting coastal populations in India. © 2025 IEEE.

关键词： Climate change and water stress Coastal areas Desalination Environmental impact Freshwater supply Renewable energy Sustainable water solutions Water observations Water purification Water scarcity

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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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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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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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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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Analyzing Risk Elements in Cardiovascular Diseases Prediction using Neural Networks Algorithm 10

Analyzing Risk Elements in Cardiovascular Diseases Predictio...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Dhyan Chandra Pal, Saurabh Yadav, Rakesh Kumar Pant, Hemlata J S University Department of Computer Science Shikohabad India Vbsp University Department of Computer Applications Jaunpur India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Ambalika Institute of Management and Technology Department of Information Technology Lucknow India

ISBN: (纸本)9789380544472

Globally, cardiovascular disease is the leading cause of death. In CVDs, the heart is a vital organ for supplying blood to other body organs. Complex datasets with heart problems are predicted by machine learning methods. Analysts and experts in diagnosis can easily locate reports of data on CVDs at the primary level. In the study, we investigate the performance of neural networks when used on datasets for CVD prediction. The performance of the method is assessed based on the characteristics of the dataset using Random Forest and Extra Tree feature selection strategies with the following basic analyses: accuracy score, precision, recall, f1-score, and roc. Our study highlights the critical properties of CVD datasets in order to determine which accuracy measures work best for predicting and diagnosing CVD. Our main findings were predictive Neural Network performance with an accuracy metric based on Random Forest and Extra Tree, highlighting key features for CVD diagnosis and prediction. Finally, a neural network using the Extra Tree features selection strategy achieved 97% accuracy, while a neural network using Random Forest achieved 98% accuracy. These can be effectively used to improve current CVD diagnosis efforts and help patients all around the world. Especially in places where there is a scarcity of medical professionals. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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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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Integrating IoT in Transportation for Smart City Development for Secured Data Processing 4

Integrating IoT in Transportation for Smart City Development...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Priya, R. Lakshmi, R. Anandha Sudha, C. Pal, Souvik Ali, Ghassan Ahmed Alajmani, Samah H. Vels Institute of Science Technology and Advanced Studies Dept. of Computer Applications Chennai India Anna Adarsh College for Women Dept. of Computer Applications Chennai India SSKV College of Arts and Science for Women Dept. of Computer Applications Kanchipuram India Saveetha College of Liberal Arts & Sciences Saveetha Institute of Medical & Technical Sciences Chennai India Universiti Islam Sultan Sharif Ali Faculty of Islamic Technology Brunei College of Computer & IT Taif University Department of Information Technology Saudi Arabia

ISBN: (纸本)9798350353266

In this study, we propose the architecture and implementation of an IoT and machine learning powered Integrated Transport Management (ITM-SCD)for Smart City Development to mitigate the issues described above. The design takes all potential transportation system difficulties into account by utilizing many scenarios. The model integrates the various city systems into a single, cohesive whole, with an emphasis on secured data. People's movements can be monitored that smart transportation, healthcare, and entertainment can be provided. We discuss the challenges of transit security and smart city applications, as well as possible solutions based on AI and ML. Then, for smart health, transportation, and energy, some solutions to these privacy and security issues are offered. © 2024 IEEE.

关键词： Traffic congestion

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A Mobile App for Seed Grading by Incorporating Deep Learning Approach with Bio Inspired Optimization Technique 7

A Mobile App for Seed Grading by Incorporating Deep Learning...

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Mundada, Monica R. Sowmya, J.B. Anurag, G. Mani, G.M. Narayana, Satya Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India Ramaiah Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Ramaiah Institute of Technology Department of Computer Science Bengaluru India

ISBN: (纸本)9798350343144

One of the main production stumbling blocks that hinders the growth of healthy crops is seed quality. The ability to cultivate more nutrient-dense, higher-yielding crops that can help feed a growing population is made possible by high-quality seeds, which are crucial for ensuring food security. As a result, it is crucial to quickly and accurately identify high-quality seeds. Manually identifying the type of wheat requires skill and time. It gets difficult to manually differentiate seeds from an array when they all seem so identical. This prompted us to develop an app that would automate the process of determining seed quality. With the corn seed data set, a convolutional neural network is trained. By eliminating the thick layers, the characteristics of this neural network are recovered. The moth flame optimization technique, a bio-inspired algorithm, is used to reduce noise and extraneous data. The KNN classifier is used to assess the fitness of the features that have been chosen. Lastly, the KNN classifier is used to categorize the seeds based on the fewest attributes © 2023 IEEE.

关键词： Convolutional neural networks

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