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9th International Conference on Science, Technology, engineering and mathematics, ICONSTEM 2024

作者： Prakash, Kode Jaya Gobimohan, S. Rani, J. Femila Mercy Mageshwari, P. S. Latha Fawad, Iffath Suganya, G. Vnr Vignana Jyothi Institute of Engineering & Technology Department of Mechanical Engineering Telangana Hyderabad500090 India College of Engineering And Technology University of Technology And Applied Sciences Nizwa Oman Dindigul Tamil Nadu 624622 India Department of Mathematics Tamil Nadu Dindigul624622 India Tamil Nadu Kavaraipettai601206 India Dayananda Sagar College of Engineering Department of Electronics And Telecommunication Engineering Karnataka Bengaluru India Dr. Sns Rajalakshmi College of Arts And Science Department of Computer Science Tamil Nadu Coimbatore641049 India

ISBN: (纸本)9798350365092

There are a lot more IoT gadgets and data being made because IoT apps are becoming more common in our daily lives. IoT devices don't have a lot of resources, which makes it harder to handle and store IoT data. Traditional cloud computing helps with some problems caused by the Internet of Things' (IoT) limited resources, but it also causes new problems, like delay in IoT apps that need to work right away. It makes sense that edge-cloud computer technology has changed recently. It is possible to store and handle data at the edge of the network with this technology. Edge computing architectures for the internet of things (ECAsIoT) are grouped in this piece by where the data is stored, the services that help with orchestration, security, and big data. The research looks into and compares each architecture in depth using a number of factors. We look at ECAs-IoT against two other IoT stacked models to see what their pros, cons, chances, and risks are. The research lists the main problems with present ECAs-IoT and suggests ways to fix them. The Internet of Things (IoT) and its effects on edge computing are also talked about in this poll. The research ends with four possible uses for ECAs-IoT in IoT. © 2024 IEEE.

关键词： Internet of things

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Climate-Based AI-Powered Precision Irrigation: Sustainably Smart Agriculture Frameworks for Maximum Crop Yields

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Remote Sensing in Earth Systems Sciences 2024年第1期8卷 161-172页

作者： Dhanke, Jyoti A. Srivastava, Diksha Menaga, D. Raj, Roop Kumar, Kambala Vijaya Jangir, Pradeep Mani, P. Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Biotechnology and Chemical Engineering Manipal University Jaipur Jaipur India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Economics HNBG Central University Uttarakhand Srinagar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan K.Ramakrishnan College of Technology Trichy India

A digital transformation is necessary for agriculture to achieve sustainability and increase productivity. With the use of IoT, high-tech detectors and helicopters can monitor crop health and environmental factors in real time, transmitting crucial data to cloud-based systems. Due to their robust computational and storage capabilities, these systems are capable of handling large datasets and carrying out intricate data analytics. Even before the COVID-19 pandemic, the world’s food supply was precariously balanced and insufficient. The present agricultural system prioritizes short-term gains above the preservation of the earth and its ecosystems. To construct agricultural systems that can meet the needs of ten billion individuals in the next 30 years, significant structural and automated changes are required. However, with the support of intelligent technology and the progress of artificial intelligence in agriculture, these obstacles might be overcome. Integration of data, processing images, and neural networks are examples of artificial intelligence technologies that have allowed producers to make better choices using up-to-date information and prediction models. The study emphasizes the need to address the ethical and practical concerns associated with climate-based AI in agriculture to promote its equitable deployment. The results of this research contribute to the ongoing debate about artificial intelligence’s possible applications in farming. New agricultural technologies provide optimism for a future that is environmentally friendly and will be able to feed the growing global population. The integration of renewable energy sources is one of the areas where artificial intelligence is seen to be able to assist in reaching global sustainability goals. Artificial intelligence (AI) has the potential to revitalize both newly developed and existing agricultural land by modifying, deploying, and implementing automated systems and tools. We take stock of where AI is at

关键词： Artificial intelligence Climatic conditions Cloud computing Deep learning Energy Sustainable precision irrigation

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A Novel Nash-Based Matching Approach for Multirobot Task Allocation in Distributed Robotic Networks 7th

A Novel Nash-Based Matching Approach for Multirobot Task All...

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7th EAI International Conference on Robotics and Networks, ROSENET 2023

作者： Hamidoğlu, Ali Gül, Ömer Melih Gültekin, Gökhan Koray Kadry, Seifedine Nimer Department of Mathematics Bahcesehir University İstanbul Turkey Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Electrical-Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

Efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (MRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular MRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same number of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed systems Energy harvesting Game theory Multirobot systems Task allocation

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems Science & engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Optimizer: A Study 3

Skin Melanoma Segmentation using VGG-UNet with Adam/SGD Opti...

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

作者： Rajinikanth, Venkatesan Kadry, Seifedine Damasevicius, Robertas Sankaran, D. Abed Mohammed, Mazin Chander, Shrinithi Noroff University College Faculty of Applied Computing and Technology Kristiansand94612 Norway Silesian University of Technology Faculty of Applied Mathematics Gliwice44-100 Poland St. Joseph's College of Engineering Omr Department of Electronics and Instrumentation Engineering Chennai600119 India University of Anbar College of Computer Science and Information Technology Ramadi31001 Iraq St. Joseph's College of Engineering Omr Department of Computer Science and Engineering Chennai600119 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Skin is a major sensory organs and the irregularity in the skin causes various issues. Skin cancer is one major medical emergency, and appropriate detection and treatment are essential. Clinical level skin cancer assessment is normally performed using the Dermoscopy Images (DI). This research aims to develop and implement the VGG-UNet scheme to extract and assess the abnormal section in DI with improved accuracy. This plan includes;(i) Image collection and resizing, (ii) Segmentation of skin lesion in DI, (iii) Comparing the segmented section with the Ground-Truth (GT), and (iv) Performance evaluation and validation. The merit of the proposed technique is confirmed by computing the necessary performance metrics. In this work, the performance of the proposed technique is verified with different optimizers (Adam/SGD) and the average pooling. The experimental work is executed on the ISIC2016 challenge dataset, and the merit of this scheme is verified with Jaccard, Dice, and Accuracy. © 2022 IEEE.

关键词： Diseases

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Fake News Detection Regarding COVID-19 Tweets Using Machine Learning Approaches 8

Fake News Detection Regarding COVID-19 Tweets Using Machine ...

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8th IEEE International Conference on Recent Advances and Innovations in engineering, ICRAIE 2023

作者： Hussin, Muhammad Hazimmuddin Mahmud, Yuzi Mohd Hanafiah, Nurul Izzah Azma Nasruddin, Zan Mohd Ariffin, Nor Hapiza Ince, Murat Senel, Fatih Ahmet Universiti Teknologi MARA School of Computing Sciences College of Computing Informatics and Mathematics Shah Alam Malaysia Sohar University Faculty of Business MIS Department Sohar Oman Isparta University of Applied Sciences Department of Computer Technologies Isparta Turkey Suleyman Demiral University Department of Computer Engineering Isparta Turkey

ISBN: (纸本)9798350315516

The pervasiveness of misinformation surrounding the COVID-19 pandemic has garnered heightened attention due to its implications, as a noteworthy proportion of the populace is being exposed to spurious and unsubstantiated narratives concerning the crisis. This research utilizes a dataset sourced from Codalab, comprising 8,560 tweets, with 4,480 labelled as real and 4,080 as fake. The research explores the effectiveness of different machine learning models, including logistic regression (LR), random forest (RF), and deep learning models such as Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). In addition to model comparison, experiments were conducted to analyze the impact of different data splits (70:30, 80:20, and 90:10), batch sizes (16, 32, and 64), and the number of epochs (5, 10, and 15) on model performance. The experiments provided insights into the optimal configurations for the models. The results showcase the model's capabilities, with high accuracy achieved across the different models. Specifically, logistic regression achieved an accuracy of 92%, random forest 91%, Bi-LSTM 93%, and CNN 95%. These findings highlight the potential of deep learning models, particularly CNN, in accurately detecting fake news from COVID-19-related tweets. © 2023 IEEE.

关键词： COVID-19

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Near Duplicate Video Detection Algorithm Based on Gray-scale Pyramid and Temporal Features 7

Near Duplicate Video Detection Algorithm Based on Gray-scale...

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7th International Conference on Big Data and Information Analytics, BigDIA 2021

作者： Xie, Yuxiang Yan, Jie Luan, Xidao Gong, Quanzhi Zhang, Jiahui Feng, Suru College of Systems Engineering National University of Defense Technology Changsha China College of Computer Engineering and Applied Mathematics Changsha University Changsha China

ISBN: (纸本)9781665424660

Near duplicate video detection algorithms based on global feature is sensitive to the brightness and cropping transform of videos. To solve the problem, a near duplicate video detection algorithm based on gray-scale pyramid and temporal feature called GSPOM is proposed. Before extracting the block brightness feature of the video, select the first k frames of the test video and match the gray-scale pyramid feature with the query ones. On the one hand, the dissimilar video will be filtered by matching and the amount of data will be reduced;On the other hand, the editing modes of the test video are estimated by the matching process. The corresponding gray-scale feature of the keyframe in the video library is extracted as the keyframe, and its features are extracted and matched with the feature of the test video. The similarity between them is also calculated. Experiments show that the algorithm has a significant improvement compared with the traditional algorithm based on temporal features. © 2021 IEEE.

关键词： Luminance

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Intelligent recognition system for viewpoint variations on gait and speech using CNN-CapsNet

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 363-382页

作者： G.Merlin Linda N.V.S.Sree Rathna Lakshmi N.Senthil Murugan Rajendra Prasad Mahapatra V.Muthukumaran M.Sivaram Department of Computer Science and Engineering SRM Institute of Science and Technology–Vadapalani CampusChennaiIndia Department of Electronics and Communication Engineering Agni College of TechnologyChennaiIndia School of Computer Science and Engineering VIT-AP UniversityAmaravatiIndia Department of Computer Science and Engineering SRM Institute of Science and TechnologyGhaziabadIndia Department of Mathematics School of Applied SciencesREVA UniversityBangaloreIndia Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Purpose-The paper aims to introduce an intelligent recognition system for viewpoint variations of gait and *** proposes a convolutional neural network-based capsule network(CNN-CapsNet)model and outlining the performance of the system in recognition of gait and speech *** proposed intelligent system mainly focuses on relative spatial hierarchies between gait features in the entities of the image due to translational invariances in sub-sampling and speech ***/methodology/approach-This proposed work CNN-CapsNet is mainly used for automatic learning of feature representations based on CNNand used capsule vectors as neurons to encode all the spatial information of an image by adapting equal variances to change in *** proposed study will resolve the discrepancies caused by cofactors and gait recognition between opinions based on a model of ***-This research work provides recognition of signal,biometric-based gait recognition and sound/speech *** evaluations are conducted on three aspects of scenarios,namely fixed-view,cross-view and multi-view *** main parameters for recognition of gait are speed,change in clothes,subjects walking with carrying object and intensity of *** limitations/implications-The proposed CNN-CapsNet has some limitations when considering for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware *** implications-This research work includes for detecting the walking targets from surveillance videos considering multimodal fusion approaches using hardware sensor *** can also act as a pre-requisite tool to analyze,identify,detect and verify the malware ***/value-This proposed research work proves to be performing better for the recognition of gait and speech when

关键词： Intelligent system Gait recognition Convolutional neural network Deep learning Capsule network Viewpoint variations

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IoT Malware Detection Tool with CNN Classification for Small Devices 6

IoT Malware Detection Tool with CNN Classification for Small...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Bhatnagar, Gunjan Kumar Rajoria, Yogendra Sakeel, Md. Vigenesh, M. Premananthan, G. Dongre, Deepika School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand India School of Basic & Applied Sciences K. R. Mangalam University Department of Mathematics Gurugram India Invertis University Department of Computer Applications Bareilly India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore India Vishwakarma Institute of Information Technology AI & Ds Pune India

ISBN: (纸本)9798350304480

Since the number of linked devices continues to grow quickly, the Web of Everything (IoT) is being employed change today in more industries. More issues in regards to safety, stability, and supportability are brought on by an increase in smart devices, particularly as it relates to 5G technology. We are concentrating on the safety component of IoT systems in just this article because it is a relatively new field. For varied purposes, such as cost savings or a desire to forego the use of generation system, many Internet device manufacturers need not take security into account when designing their products. The opponent may use those potentially dangerous technologies to carry out several destructive assaults. As a result, we created a device capable of recognizing malicious activity from a particular IoT networks node. They are capable to offer detection methods for IoT utilizing a primary key that may be put inside the system using a multilayer brain networks for identification and surveillance. The accomplishment dispels any misconceptions about methods for deep training while demonstrating how that systems can be simply adapted and deployed to every system. © 2023 IEEE.

关键词： Classification CNN IoT Machine Learning Malware Multicategories

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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