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Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Intelligent Automation & Soft Computing 2023年第2期35卷 1873-1887页

作者： S.Siamala Devi Chandrakala Kuruba Yunyoung Nam Mohamed Abouhawwash Department of Computer Science and Engineering Sri Krishna College of TechnologyCoimbatore641042India Department of Computer Science and Engineering Vignan Nirula Institute of Technology and ScienceGuntur522009India Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the *** fundamental problem with WSN is that it has aﬁnite *** a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data *** cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunﬂower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is *** does not provide better competence and out-put in the ***,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the *** hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used toﬁnd the route among origin CH and *** open cache of nodes,trans-mission power,and proximity are used to improve the CH *** grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle *** GWO method was inspired by this hunting *** proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale *** HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

关键词： Energy efﬁciency power consumption lifespan of the network black hole method ant colony optimisation routing and cluster heads(CHs)

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Paillier Cryptography Based Message Authentication Code for IoMT Security

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computer systems science & engineering 2023年第3期44卷 2209-2223页

Health care visualization through Internet of Things(IoT)over wireless sensor network(WSN)becomes a current research attention due to medical sensor evolution of *** digital technology-based communication system is widely used in all *** of medical thing(IoMT)assisted health-care application ensures the continuous health monitoring of a patient and provides the early awareness of the one who is suffered without human *** smart medical devices may consume with limited resources and also the data generated by these devices are large in *** IoMT based applications suffer from the issues such as security,anonymity,privacy,and *** overcome these issues,data aggregation methods are the solution that can concatenate the data generated by the sensors and forward it into the base station through fog node with efficient encryption and *** article proposed a well-organized data aggregation and secured transmission *** data generated by the sensor are collected and *** nodes(AN)received the compressed data and concatenate *** concatenated and encrypted data is forward to fog node using the enhanced Paillier cryptogra-phy-based encryption with Message Authentication code(MAC).Fog node extracts the forwarded data from AN using Fog message extractor method(FME)with *** proposed system ensures data integrity,security and also protects from security *** proposed model is simulated in Net-work Simulator 2.35 and the evaluated simulation results proves that the aggregation with MAC code will ensures the security,privacy and also reduces the communication *** node usages in between Aggregator and base station,will reduce the cloud server/base station computational overhead and storage *** proposed ideology is compared with existing data aggregation schemes in terms of computational cost,storage cost,communication cost and energy *** of communica

关键词： Fog IoMT wireless sensor network cloud aggregation encryption decryption energy

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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Extracting Motion Patterns of Greeting Gestures Using GNG with Spiking Neuron Model 13

Extracting Motion Patterns of Greeting Gestures Using GNG wi...

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Joint 13th International Conference on Soft Computing and Intelligent systems and 25th International Symposium on Advanced Intelligent systems, SCIS and ISIS 2024

作者： Obo, Takenori Wibowo, Angga Wahyu Sato-Shimokawara, Eri Shibata, Hiroki Ho, Yihsin Kobayashi, Ichiro Tokyo Metropolitan University Department of Mechanical Systems Engineering Tokyo Japan Tokyo Metropolitan University Department of Computer Science Tokyo Japan Takushoku University Department of Electronics and Computer Systems Tokyo Japan Takushoku University Faculty of Engineering Tokyo Japan

ISBN: (纸本)9798350373332

This study focuses on behavior patterns, which are characteristic actions common to groups or communities, to promote intercultural understanding. Understanding greeting gestures is crucial for comprehending social relationships, but these vary across cultures and regions. Recent advancements in sensing technology and artificial intelligence, such as RGB-D cameras and markerless motion capture systems, have made it easier to capture motion data in diverse formats. Therefore, it is important to extract relevant motion patterns and apply appropriate structuring for effective long-term archiving. We propose a method for extracting motion patterns using a Growing Neural Gas (GNG) algorithm with a Spiking Neuron Model. Additionally, this paper presents an experimental example of extracting a gesture pattern in Indonesian style. © 2024 IEEE.

关键词： Economic and social effects

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CSV Alliance Model of SMEs Considering Local Clusters in Japan 1

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8th Asia Conference on Environment and Sustainable Development, ACESD 2023

作者： Shirasawa, Naoto Seo, Yuna Department of Industrial and Systems Engineering Faculty of Science and Technology Tokyo University of Science Noda Japan

ISBN: (数字)9789819733200

ISBN: (纸本)9789819733194

In recent years, Creating Shared Value (CSV), a management strategy based on the concept of creating both economic and social value for a company, has been attracting attention, and companies are trying to put it into practice. In Japan, one of the CSV approaches is also developing clusters, which in some cases create both value for companies. However, there are only a few empirical studies about CSV alliances in local industrial clusters in the context of inter-organizational networks. Especially in Japan, where SMEs comprise the majority of the supply chain, it is important for SMEs to seek and expand CSV alliances with other companies and organizations within the cluster. This study aims to propose CSV alliance models of SMEs among local clusters in Japan by using structural equation modeling. Then we target SMEs in local industrial clusters and construct the CSV alliance with regard to intra- and inter- organizational alliance management and local resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Supply chains

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Bitcoin Price Prediction Using Deep Learning Models 4

Bitcoin Price Prediction Using Deep Learning Models

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Hajare, Rasika Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

Forecasting the price of bitcoins is significant in contemporary research, given the fact that the digital currency is relatively unpredictable and highly integrated in global securities markets. This paper compares the use of three deep learning models, namely recurrent neural networks (RNN), gated recurrent unit (GRU), and a convolutional neural network-long short-term memory (CNN-LSTM) for Bitcoin price prediction. Historical Bitcoin price data is used in the dataset, and every one of the models is trained and tested based on its performance to predict future prices based on past data. The proposed models include the RNN and GRU, which incorporate sequential dependency mappings to learn complex sequences, and the CNN-LSTM structures combine convolution layers for feature extraction and LSTM layers for sequencing. Different models' competency is discussed using the averaged Mean Squared Error (MSE) and graphs of the model's prediction against actual prices. The experimental results reveal that the proposed CNN-LSTM model has better prediction accuracy than RNN and GRU in time-series forecasting tasks, indicating that more complex architecture hybrid models might provide better forecasting performance. In analyzing and analyzing cryptocurrency price dynamics, this investigation gives information about the opportunities and challenges of these models to enhance future research. © 2025 IEEE.

关键词： Prediction models

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Textual Emotion Recognition: Advancements and Insights 6

Textual Emotion Recognition: Advancements and Insights

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Esmail, Ahmed A. Abdallah, Sayed M. Elsayed, Mohamed A. Ghaith, Amr E. Elbanhawy, Magy Al-Zoghby, Aya M. Moawad, Ibrahim F. Artificial Intelligence Science New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Mansoura University Faculty of Computers and Information Dakhlia Egypt New Mansoura University Faculty of Computer Science and Engineering Dakhlia Egypt Portsaid University Faculty of Engineering Egypt Damietta University Faculty of Computers and Artificial Intelligence Computer Science Department Damietta Egypt Ain Shams University Information Systems Faculty of Computer and Information Sciences Cairo Egypt College of Computer Science and Engineering Taibah University Department of Artificial Intelligence Medina42353 Saudi Arabia

ISBN: (纸本)9798350378511

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Emotion Recognition

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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AI-Powered Clinical Decision Support systems (CDSS): Challenges, Benefits, Applications, and Future Directions 2

AI-Powered Clinical Decision Support Systems (CDSS): Challen...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Yesankar, Prajyot Puri, Chetan Gote, Pradnyawant M. Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

AI-based Clinical Decision Support systems are a revolutionary innovation in the healthcare sector, using artificial intelligence to improve diagnostic precision, optimize treatment planning, and enhance patient outcomes. This paper discusses the challenges, benefits, applications, and future directions of AI-driven CDSS. Some of the key challenges are data quality, integration with EHRs, ethical concerns, and explainability issues. Despite these challenges, benefits such as efficiency improvements, reduced clinician workload, and the possibility of tailored medicine are substantial. In addition, the paper depicts diverse applications across oncology, cardiology, and primary care. The paper further stresses the directions: validation of robustness, user-friendly interfaces, and equitable deployment to achieve maximal utilization of AI-CDSS in global health care systems. Besides this, the integration of multimodal data and improvement in predictive analytics will help to revolutionize preventive care. Moreover, ethical practices with AI will bring forth trust and adoption. Such an all-inclusive review signifies the pivotal role AI will play in shaping clinical decision-making for the future. © 2025 IEEE.

关键词： Personalized medicine

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Submerged rigid vegetation effects on flow hydrodynamics within the pool morphology

River

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River 2024年第3期3卷 260-271页

作者： Kourosh Nosrati Hossein Afzalimehr Hamidreza Raeisifar Mohammad Nazari-Sharabian Moses Karakouzian Faculty of Civil Engineering Iran University of Science and TechnologyTehranIran Department of Mathematics Engineeringand Computer ScienceWest Virginia State UniversityInstituteWest VirginiaUSA Department of Civil and Environmental Engineering University of Nevada Las VegasLas VegasNevadaUSA

This research is centered on a comprehensive investigation into the impact of turbulence on the movement and dispersion of materials within a three-dimensional(3D)bedform,speciﬁcally when there is a continuous presence of rigid vegetation submerged in theﬂ*** achieve our research objectives,we conducted extensive velocity measurements within a channel featuring this submerged *** measurements were carried out using an Acoustic Doppler Velocimeter(ADV).Additionally,our study delved into the intricate structures and turbulent characteristics of theﬂow,considering the coexistence of submerged vegetation and a 3D gravel *** pool featured entrance and exit slopes measuring 3 and 2.5°,*** experimental setup took place in a straightﬂume,measuring 14 m in length,0.9 m in width,and 0.6 m in ***ﬂume was equipped with transparent side walls to facilitate ***,our investigation extended to the spatial variations in velocity and turbulence *** analyzed various parameters including turbulence kinetic energy,integral turbulence lengths,dispersion coefﬁcients,and advective *** results revealed that integral length scales offer key insights into turbulent eddy *** the presence of vegetation and a 3D bedform,turbulent eddies undergo notable changes,ﬂattening in the longitudinal direction and expanding in the transverse and vertical ***,longitudinal advection is notably higher compared toﬂows without vegetation in a uniformﬂow or bare channel,especially for z/H>*** indicates that the presence of vegetation and a 3D bedform leads to an increase in turbulent kinetic energy(k values)that surpasses the reduction in the time-averaged velocity component(“U”)in the U×k term,thereby enhancing longitudinal advection.

关键词： 3D pool advection dispersion integral turbulent length submerged rigid vegetation

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