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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Karthik Raghunath, K.M. Kamalraj, R. Faculty of Engineering and Technology Department of Electronics and Communication Engineering Bangalore India Department of Computer Science and IT Bangalore India

ISBN: (纸本)9781665456876

The rapid advancement of wireless sensor networks has prompted the invention of low-cost and compact sensor devices, with the Internet of Things (IoT) enabled as a feasible tool for automation and decision-making in the field of agriculture. This is a significant step forward. The purpose of this study is to investigate the feasibility of developing an expert system for determining the appropriateness of agricultural land by combining sensor networks with artificial intelligence (AI) systems. This proposed system will assist farmers in evaluating agricultural land for cultivation in terms of four decision classes, determining why manure regimes should be adjusted to meet the demands of specific crops and regions, and protect the environment by lowering the amount of pollution caused by unused fertilizer and manure. There are a few methods for the management of soil richness, such as assessing the quality of the soil in terms of its pH level and the amounts of phosphate, nitrogen, and potassium it contains. An ANN model uses climate data to make suggestions and estimates about the amounts of NPK supplements, as well as suggestions about how compost should be treated and when it should be applied.. © 2022 IEEE.

关键词： Internet of things

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A Survey of Artificial Hummingbird Algorithm and Its Variants: Statistical Analysis, Performance Evaluation, and Structural Reviewing

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Archives of Computational Methods in engineering 2025年第1期32卷 269-310页

作者： Hosseinzadeh, Mehdi Rahmani, Amir Masoud Husari, Fatimatelbatoul Mahmoud Alsalami, Omar Mutab Marzougui, Mehrez Nguyen, Gia Nhu Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Taif University Taif21944 Saudi Arabia Department of Computer Engineering College of Computer Science King Khalid University Abha61413 Saudi Arabia School of Computer Science Duy Tan University Da Nang Viet Nam Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam13120 Korea Republic of

In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. © The Author(s) under exclusive licence to International Center for Numerical Methods in engineering (CIMNE) 2024.

关键词： Optimization

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Mobile-based Cloud Computing with Autonomous Assets Supply 4

Mobile-based Cloud Computing with Autonomous Assets Supply

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

作者： Shadaksharappa, B. Maruthu Kannan, B. Yuvaraj, S. Maniraj, S.P. Sri Sairam College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru India Sphoorthy Engineering College Nadergul Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

The overwhelming rise in computing and networking skills makes Mobile platforms as predominant means of Internet access. Specific portable apps are unusable due to the inadequate computing capacities of mobile platforms. The constrained conne ctivity and reaction times involved in offloading data into multiple servers, such as a cloud data center, make permitting applications needing real-time information collection and processing on mobile stages are very challenging. The hybrid statics/mobile computing network is the latest supply system which has the heterogeneous ability for sensing, computing, and networking with static/mobile devices nearby. This robust computing network can allow creative mobile information and calculation-intensive applications, including omnipresent context-aware health and wellbeing checks of older adults, remote precipitation and flood risk evaluation, shared object detection and monitoring, and shared multimedia content searching and sharing. A detailed experimental study is provided to validate and demonstrate the advantages of the autonomous capability of the proposed system. © 2023 IEEE.

关键词： Cloud computing

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Integration of heterogeneous medical and biological data with electronic personal health records 68

Integration of heterogeneous medical and biological data wit...

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68. Jahrestagung der Deutsche Gesellschaft fur Medizinische Informatik, Biometrie und Epidemiologie e.V., GMDS 2023 - 68th Annual Conference of the German Association for Medical Informatics, Biometry and Epidemiology, GMDS 2023

作者： Savoska, Snezana Ristevski, Blagoj Blazheska-Tabakovska, Natasha Jolevski, Ilija Bocevska, Andrijana Trajkovik, Vladimir Faculty of Information and Communication Technologies University "St. Kliment Ohridski" Bitola Macedonia Faculty of Computer Science and Engineering "Ss. Cyril and Methodius" University of Skopje Macedonia

The shortage of data for patients with chronic and other diseases and previous medical treatments shows significant weakness in the diagnosis and treatment of patients. Due to the healthcare system insufficiency, patients with comorbidities might not survive the diseases, especially when the disease is novel. The lack of information on patients’ genetic disorders, especially when they are unaware of them, also contributes to increased patient deaths. This conveys the necessity to integrate medical and health data with various biological omics and other data, especially in pandemic circumstances. Patients’ health data matters are apparent, but they are stored in multiple hospitals and health systems such as electronic health records (EHRs), healthcare institutions, and laboratories. Furthermore, biological data are often not integrated and cannot be used by patients, physicians, and specialists to treat particular diseases. Although the urgent need for healthcare and medical data integration is apparent, personal data protection regulations are severe. They do not allow much progress in the area without implementing security and privacy standards for patient healthcare data. One solution for this issue is setting a personal health record (PHR) as an integrative system for the patient. Many ontological frameworks have been proposed to unify the record formats, but none of them is accepted as a healthcare standard. The efforts toward approving the Health Level Seven (HL7) standards and the common medical coding systems ensure further data integration. Some efforts are made to associate particular diseases with data obtained from external environmental sensors that measure disease-associated data. Using these data, which are called exposome, the increasing symptoms of particular diseases influenced by external factors can be clarified. This paper suggests a cloud-based model for integrating healthcare and medical data from different sources such as EHRs, health informat

关键词： Data integration

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Packet Drop Reduction by Volunteer nodes in Mobile Wireless Sensor Networks 2

Packet Drop Reduction by Volunteer nodes in Mobile Wireless ...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Mishra, Nilamadhab Latha, P. Rekha, K. Sashi MuthuKumaran, B. School of Computer Science and Engineering Vit Bhopal University Madhya Pradesh Bhopal India St.Joseph's College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350347579

In Mobile Wireless Sensor Networks (MWSN), minimizing packet drop is vital. Many strategies are proposed to manage the packet drops. Though, these strategies need to be more capable of handling the packet drops proficiently. This approach proposes a Packet Drop Reduction using Volunteer nodes (PDRV) in MWSN. Volunteer nodes aim to reduce packet drops and delays in the network. In this approach, the volunteer node listens to the discharge data packet and then allocates the transmitter node. These nodes are selected based on node linkage, energy, communication processing time, and bandwidth. In addition, PDRV verifies the hash signature with One Time Key (OTK) for transmitting secure data. Hence, it reduces both the packet drops and the delay. Here, the volunteer nodes are selected by the node ability. The node linkage. energy, communication processing time, and bandwidth evaluate node ability. Experimental outcomes illustrate that the PDRV mechanism has reduced the network delay and packet losses. © 2023 IEEE.

关键词： Bandwidth

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Leveraging AI to Unlock the Potential of Data science 15

Leveraging AI to Unlock the Potential of Data Science

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Rakesh Kumar Sudhamsu, Gadug Goyal, Manish Kumar Yuvaraj, S. Deshpande, Leena A. Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350370249

The application of synthetic Intelligence (AI) to records science has spread out a massive array of new possibilities. Via the combination of advanced evaluation strategies, effective understanding-pushed system gaining knowledge of algorithms, and modern computing architectures, AI is allowing researchers to quick and as it should be discover insights from huge and complicated datasets. Furthermore, AI tools are offering corporations with the ability to make higher and more knowledgeable choices from the evaluation of hundreds of thousands of statistics points. The skills of information technology powered by means of AI are revolutionizing industries which includes healthcare, finance, and manufacturing-producing savings, uncovering efficiencies, and identifying new opportunities. As AI-driven records technology maintains to strengthen, organizations that capitalize on its ability stand to gain precious insights and unlock new degrees of overall performance. © 2024 IEEE.

关键词： Smart manufacturing

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Multi-Access Edge Computing Using Intelligent Mobile User Resource Allocation In 6G 4

Multi-Access Edge Computing Using Intelligent Mobile User Re...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Gaid, AbdulGuddoos S. A. Ahmed, Zeinab E. Elbasheir, Mohammed S. Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University Faculty of Engineering Department of Electrical and Electronic Engineering Altaif Saudi Arabia College of Computers and Information Technology Taif University Department of Computer Engineering Taif21944 Saudi Arabia Taiz University Faculty of Engineering & Information Technology Dept. of Communication & Computer Engineering Taiz Yemen International Islamic University Malaysia Department of Electrical and Computer Malaysia School of Electronic Engineering Sudan University of Science and Technology Khartoum Sudan

ISBN: (纸本)9798350372632

Edge computing plays a crucial role in the advancement of Beyond 5G (B5G) and 6G networks. The substantial growth in network traffic and computational requirements necessitates the implementation of multi-access edge computing (MEC) as a viable solution. MEC aims to bring the capabilities of cloud computing closer to end-users by integrating them into the radio access network (RAN). This research introduces an optimal approach to connect mobile users with MEC hosts and access points, effectively distributing radio and computing resources to individual mobile users. The objective is achieved by lowering the total user transmit power while abiding by latency constraints in both the communication and computation phases, using the Grey Wolf Optimization technique (GWO). A comparison to alternative smart techniques demonstrates the effectiveness of the suggested approach © 2024 IEEE.

关键词： 5G mobile communication systems

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