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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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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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A Comparative Analysis and Prediction of Diabetes Using Machine Learning Approaches 5th

A Comparative Analysis and Prediction of Diabetes Using Mach...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Ansari, Gufran Ahmad Bhat, Salliah Shafi Bhoite, Sachin Department of Computer Science and Engineering and Software Engineering University of Hail Hail Saudi Arabia Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Pune411 038 India

ISBN: (纸本)9789819763177

Diabetes is a leading cause of death. People with type 1 diabetes (T1D) can face a number of consequences from their illness. Hyperglycemia is a symptom of T1D disease. Hyperglycemia can be symptoms that can be used for predicting diabetes in patients using the machine learning approach (MLA). Learning algorithms play a role in disease detection and prediction of decision-making. The leading cause of diabetes is really a high blood sugar level or a lack of insulin. As a consequence, it is critical to develop a reliable method to predict diabetes. Once it becomes a serious health concern, it will damage different organs such as the lungs, brain, nerves, blood vessels and heart as a result. Several approaches for detecting diabetes have been used in recent decades. Early diabetic diagnosis is currently very important to help the patients’ health, and this can be done by utilizing a variety of procedures. Tenfold cross-validation technique was used to train dataset for classification. In this paper, authors use different classifications of algorithms such as random forest, decision tree, logistic regression and multilayer perceptron for finding T1D patients. The accuracy of MLP is 98.077% which is the highest among other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Contrastive Learning

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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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A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

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SN computer science 2024年第7期5卷 1-14页

作者： Pattanayak, Pradipta Kumar Tripathy, Rudra M. Padhy, Sudarsan Research Scholar BPUT(Rourkela) Department of Computer Science and Engineering Silicon University Patia Odisha Bhubaneswar 751024 India School of Computer Science and Engineering XIM University Harirajpur Odisha Bhubaneswar 752050 India Institute of Mathematics and Applications Andharua Odisha Bhubaneswar 751029 India

Twitter has emerged as a significant source of data to be used for text summarization, Topic Modeling, document clustering, information retrieval, sentiment analysis, etc. Using hashtags, Twitter users may categorize their tweets as hashtags provide the essential meta-information in connecting tweets to the underlying themes. However, the majority of tweets do not have hashtags, which makes it challenging to search for a particular theme. The proposed model is designed to recommend appropriate hashtags for tweets by considering their categorization into sports, politics, health, or technology. In this paper, we proposed a novel heuristic for recommending hashtags of tweets. Taking 20,000 tweets, which includes 5000 tweets from each of the specified four topics along with their respective hashtags. These hashtags were manually assigned by a group of experts, which were subsequently excluded during the topic modeling process. The basic data-cleaning technique is applied to clean and tokenize the tweets. Then Word2Vec technique is used to vectorize the tokens which captured the semantic meaning of the words in the tweets and overcomes the data sparsity issues. The dimension of the data is reduced using Singular Value Decomposition (SVD) followed by t-SNE (t-distributed Stochastic Neighbor Embedding). The reduced data is divided into four clusters and a semi-supervised method is introduced to link these clusters to the aforementioned topics, which eventually helped to produce the hashtag for a list of tweets. On comparison of our results with existing techniques, it is observed that our model performance is better with respect to the metrics: precision, recall, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Gibbs sampling Dirichlet mixture model Linear Dirchlet allocation Singular value decomposition Topic modeling Word2Vec

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Deep Learning Techniques for Object Detection in Underwater Environments

Deep Learning Techniques for Object Detection in Underwater ...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Praveen, R.V.S. Maindola, Meenakshi Anandhi, R.J. Thandra, Nagarjuna Bansal, Saloni Sharma, Vishal LTIMindtree Limited Telangana Hyderabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India New Horizon College of Engineering Department of Information Science Engineering Bangalore India MLR Institute of Technology Dept. Computer Science and Engineering Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India Lovely Professional University Phagwara India

ISBN: (纸本)9798331512965

The degradation of image quality caused by things like light absorption, scattering, and distortion makes object detection in underwater environments a unique challenge. applications like marine research, environmental monitoring, and autonomous underwater vehicles (AUVs) are restricted in their effectiveness by traditional image processing techniques, which often struggle with these complexities. Recent years have seen the rise of deep learning techniques as a potent weapon in the fight against these obstacles. In order to accurately detect objects underwater, this paper investigates the use of deep learning models, specifically convolutional neural networks (CNNs). We explore cutting-edge models and methods like attention mechanisms, transfer learning, and hybrid deep learning architectures to improve detection performance in challenging underwater environments. Furthermore, we utilize data augmentation techniques and synthetic dataset generation to tackle the critical issue of limited labeled underwater datasets. From a detection accuracy and robustness perspective, the experimental results show that our suggested deep learning method is light years ahead of the competition. Marine exploration, autonomous underwater vehicle navigation, and underwater surveillance systems can all benefit from this study's new insights into how to better detect objects in real time while submerged. © 2025 IEEE.

关键词： Convolutional neural networks

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Object Detection in Real-Time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 IEEE.

关键词： Deep learning

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