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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Mahaveerakannan R Murali Dhar M S Sohan Goswami P. Gopala Krishna K. Jayanthi M. Sathish Kumar Department of Spatial Informatics The Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu Department of Computer Science and Technology Technique Polytechnic Institute West Bengal Department of Information Technology Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana Department of Computer Science and Engineering Pollachi Institute of Engineering and Technology Coimbatore Pollachi Tamil Nadu Department of Cyber Security Karpagam college of Engineering Coimbatore Tamil Nadu

Given the circumstances the healthcare system, the Internet of Things (IoT) is crucial. IoT gadgets offer patient data for the framework of healthcare monitoring. IoT is a key component in every aspect of the health care management system since people can use smart devices to check on their health. Lung cancer is a fatal malignancy, and the likelihood of survival is increased by early identification. Because of the computational difficulty involved in gathering characteristics, it is imperative to design an approach using machine learning techniques for categorising cancer disease because the classification results given by the current methods are inadequate. With the use of the synthetic minority oversampling methods (SMOTE) methodology, the incoming data is pre-processed and balanced. With the help of the binary grey wolf optimisation algorithm (BGWOA), the pertinent features are best chosen. Finally, the suggested model's hyper-parameters are best chosen by the tunicate swarm optimisation (TSA) model, and the classification is carried out by the extreme gradient boosting (XGBoost) model. The experimental analysis demonstrates that the suggested model attained accuracy and recall values of 98% and 95%, respectively, compared to 95% and 95%, respectively, for the identical proposed model without the feature selection (FS) method.

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An Effective Analysis of Cloud Related Platform using Big Data Approaches for Effective Implementation of High-Performance Computing through Structural Equation Modeling (SEM) Approach 2

An Effective Analysis of Cloud Related Platform using Big Da...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Bansal, Gourav Saxena, Monika Johar, Siti Sarawati Othman, Bestoon Abdulmaged Husain, S.Syed Kapila, Dhiraj Information Technology Kurukshetra University India Computer Science Banasthali Vidyapith Rajasthan India Centre for General Studies and Co-curricular Universiti Tu Hussein Onn Malaysia Department of Social Science Malaysia College of Administration & Financial Sciences Knowledge University- Kurdistan Region Iraq. Business Administration Koya Technical Institute Erbil Polytechnic University Erbil Iraq K.Ramakrishnan College of Engineering Department of ECE Trichy India Lovely Professional University Department of Computer Science & Engineering Phagwara India

ISBN: (纸本)9781665437899

The recent technologies are focusing on High- performance computing to fasten the complex calculations and market procedures in industries. Therefore, analysis of the big- data and cloud-computing is necessary to implement the 'High- performance computing' (HPC) in practice. Based on the advantages, the research aimed to identify the advantages and disadvantages of cloud computing as well as HPCs. The research used SEM approach to justify the procedure for implementing HPC effectively. In this research, SPSS software was used to conduct multiple regression analysis. A significance value of p © 2022 IEEE.

关键词： Big data

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The Application of the Internet of Things (IoT) to establish a technologically advanced Industry 4.0 for long-term growth and development 2

The Application of the Internet of Things (IoT) to establish...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Pallavi, B. Othman, Bestoon Trivedi, Guptnath Manan, Nimisha Pawar, Rajendra Sitaram Singh, Devesh Pratap Sreenidhi Institute of Science and Technology Department of Information Technology India Koya Technical Institute Erbil Polytechnic University Business Administration Erbil Iraq College of Administration and Economics Nawroz University Department of Business Administration Duhok Iraq Dr. Rajendra Prasad Central Agricultural University Pusa Bihar Samastipur India Patna Women's College Department of Computer Science Bihar Patna India Padmashri Vikhe Patil College of Arts Science and Commerce MS Ahmednagar India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Nowadays, industries are observing the potential benefits of using the Internet of Things (IoT) and it increased their interest to invest in IoT technologies. The fourth revolution of industries (industry 4.0) influenced many companies, organisations and individuals to implement the IoT in their respective fields. Industry 4.0 not only looks for its implementation;however, it also demands long-term growth and development. This research has found recent technologies which help the industries to gain significant profit through IoT. However, the major barriers that do not allow long-term growth are security issues, lack of governance, lack of skilled staff and a lack of technological advancement. Therefore, this research is going to identify the new technologies and ideas via an online survey that will help industry 4.0 to rely on IoT for long-term growth and development. The online survey has been carried out with 68 experts who had experience in IoT. They provided their responses via a google form and the collected data were analysed. Findings showed that the major concern of IoT in industry 4.0 is security issues and it can be mitigated by implementing blockchain technology. More appropriately, consortium and public blockchains are more secure which will bring future prospects in the context of long-term growth. Problems related to big data can be addressed by advancing the hardware and software system and encouraging staff training. © 2022 IEEE.

关键词： Internet of things

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Can Individuals be Repeatedly Selected for Mutation in Matrix-Based DE? A Comparative Study

Can Individuals be Repeatedly Selected for Mutation in Matri...

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Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Ling-Ling Wei Bing Sun Jian-Yu Li Qiang Yang Jing Xu Zong-Gan Chen Fei-Yi Tang School of Computer Science South China Normal University Guangzhou China College of Artificial Intelligence Nankai University Tianjin China School of Artificial Intelligence Nanjing University of Information Science and Technology Jiangsu China School of Information Engineering Guangzhou Panyu Polytechnic Pazhou Lab Guangdong China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

Evolutionary Computation (EC) is a class of algorithms derived from the simulation of natural phenomena, which faces the challenges of long running times and high computing loads resulting from large-scale problems. For this reason, the matrix-based EC (MEC) has been presented as an extension of classic EC algorithms to address large-scale optimization. The MEC can be implemented directly on high-performance computing resources, thus accelerating the computation of evolutionary operators. However, MEC is still in its infancy and lacks deeper analysis. Hence, this paper analyzes and investigates matrix-based differential evolution (MDE) and advances the development of related research through theoretical analysis and experimental validation, which has two contributions. First, through the comprehensive comparison of MDE and DE in different problems, we compare the solution quality and computational speed of MDE and DE, and show that the solution quality of MDE is not significantly different from that of DE, but the computational speed of MDE is much faster than that of DE. Second, we also further analyze the mutation mechanism of MDE, focusing on the effect of the repeatability of individuals in the mutation process on the performance of the algorithms, which provides a basis for future research on MDE of MEC.

关键词： High performance computing Focusing Evolutionary computation Stability analysis Reproducibility of results Problem-solving Optimization Machine intelligence Faces Convergence

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Advancing agriculture with machine learning:a new frontier in weed management

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农业科学与工程前沿（英文） 2025年第2期12卷 288-307页

作者： Mohammad MEHDIZADEH Duraid K.A.AL-TAEY Anahita OMIDI Aljanabi Hadi Yasir ABBOOD Shavan ASKAR Soxibjon TOPILDIYEV Harikumar PALLATHADKA Renas Rajab ASAAD Department of Agronomy and Plant Breeding Faculty of Agriculture and Natural ResourcesUniversity of Mohaghegh Ardabili***ArdabilIran Ilam Science and Technology Park ***IlamIran Department of Horticulture College of AgricultureUniversity of Al-Qasim Green00964 BabylonIraq Department of GIS and Remote Sensing Faculty of GeographyUniversity of Tehran***TehranIran Medical Laboratories Techniques Department Al-Mustaqbal University College51001 HillahIraq Erbil polytechnic university Technical engineering collegeinformation system engineering department44001 ErbilIraq Department of Fundamental Economics Tashkent State University of Economics100066TashkentUzbekistan Manipur International University Manipur 795140ImphalIndia Department of Computer Science Nawroz UniversityPO BOX 77DuhokIraq

Weed management is a crucial aspect of modern agriculture as invasive plants can negatively impact crop yields and ***-established methods of weed control,such as manual labor and synthetic herbicides,have been widely used but come with their own set of *** methods are often time-consuming,labor-intensive,and pose environmental *** have been the primary method of weed control due to their efficiency and ***,over-reliance on herbicides has led to environmental contamination,weed resistance,and potential health *** address these issues,researchers and industry experts are now exploring the integration of machine learning into chemical weed management *** technology advances,there is a growing interest in exploring innovative and sustainable weed management *** review examines the potential of machine learning in chemical weed *** learning offers innovative and sustainable approaches by analyzing large data sets,recognizing patterns,and making accurate *** learning models can classify weed species and optimize herbicide ***-time monitoring enables timely intervention,preventing invasive species spread,Integrating machine learning into chemical weed management holds promise for enhancing agricultural practices,reducing herbicide usage and minimizing environmental *** and refinement of these algorithms are needed for practical application.

关键词： Weed management herbicides machine learning agricultural practices environmen-tal impact

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Management of business using machine learning for decision making 3

Management of business using machine learning for decision m...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Ilahi, Saud Othman, Bestoon Joshi, Kapil Sundararajan, S. Jethabhai, Patel Yogeshkumar Gour, Veer Sudheer Jazan University College of Business Administration Department of Business Administration Jazan Saudi Arabia Polytechnic University Business Administration Koya Technical Institute Erbil Erbil Iraq Nawroz University College of Administration and Economics Department of Business Administration Duhok Iraq Uttaranchal University Uttaranchal Institute of Technology India Skyline University Business Management Kano City Kano State Nigeria Nigeria Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies India St. Martin's Engineering College Department of Cse Telangana Secunderabad India

ISBN: (纸本)9798350399264

While machine learning and artificial intelligence might lessen expenses and increment the viability of business processes, these methods can likewise obliterate the worth of an organization, some of the time with serious repercussions. The failure to perceive and control that chance might make a few directors put off carrying out innovations, keeping them from arriving at their maximum capacity. Machine learning has colossal potential for decreasing the expense of labor and products, speeding up organization activities, and further developing client care. In the ongoing time of uncommon specialized headway, it is recognized as one of the huge application regions, and reception is advancing across essentially all businesses. In light of this, Researchers first provide a brief review of the different types of machine learning before presenting three different ways that machine learning is used in businesses. The trade-off between machine-learning algorithms' accuracy and interpretability is then covered. This is an important factor to take into account while choosing the best approach for the task at hand. © 2023 IEEE.

关键词： Machine learning

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ARN-LSTM: A Multi-Stream Fusion Model for Skeleton-based Action Recognition

arXiv

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arXiv 2024年

作者： Wang, Chuanchuan Mohmamed, Ahmad Sufril Azlan Noor, Mohd Halim Bin Mohd Yang, Xiao Yi, Feifan Li, Xiang School of Computer Science Universiti Sains Malaysia Penang11800 Malaysia School of Engineering College of Technology and Business Guangzhou510850 China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University 518102 China

This paper presents the ARN-LSTM architecture, a novel multi-stream action recognition model designed to address the challenge of simultaneously capturing spatial motion and temporal dynamics in action sequences. Traditional methods often focus solely on spatial or temporal features, limiting their ability to comprehend complex human activities fully. Our proposed model integrates joint, motion, and temporal information through a multi-stream fusion architecture. Specifically, it comprises a joint stream for extracting skeleton features, a temporal stream for capturing dynamic temporal features, and an ARN-LSTM block that utilizes Time-Distributed Long Short-Term Memory (TD-LSTM) layers followed by an Attention Relation Network (ARN) to model temporal relations. The outputs from these streams are fused in a fully connected layer to provide the final action prediction. Evaluations on the NTU RGB+D 60 and NTU RGB+D 120 datasets outperform the superior performance of our model, particularly in group activity recognition. © 2024, CC BY-NC-SA.

关键词： Musculoskeletal system

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Reversible Data Hiding over Encrypted Images via Intrinsic Correlation in Block-Based Secret Sharing

arXiv

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arXiv 2025年

作者： Zou, Jianhui Cao, Weijia Yi, Shuang Zheng, Yifeng Hua, Zhongyun School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen518055 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Engineering Research Center of Forensic Science Chongqing Education Committee College of Criminal Investigation Southwest University of Political Science and Law Chongqing401120 China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

With the rapid advancements in information technology, reversible data hiding over encrypted images (RDH-EI) has become essential for secure image management in cloud services. However, existing RDH-EI schemes often suffer from high computational complexity, low embedding rates, and excessive data expansion. This paper addresses these challenges by first analyzing the block-based secret sharing in existing schemes, revealing significant data redundancy within image blocks. Based on this observation, we propose two space-preserving methods: the direct space-vacating method and the image-shrinking-based space-vacating method. Using these techniques, we design two novel RDH-EI schemes: a high-capacity RDH-EI scheme and a size-reduced RDH-EI scheme. The high-capacity RDH-EI scheme directly creates embedding space in encrypted images, eliminating the need for complex space-vacating operations and achieving higher and more stable embedding rates. In contrast, the size-reduced RDH-EI scheme minimizes data expansion by discarding unnecessary shares, resulting in smaller encrypted images. Experimental results show that the high-capacity RDH-EI scheme outperforms existing methods in terms of embedding capacity, while the size-reduced RDH-EI scheme excels in minimizing data expansion. Both schemes provide effective solutions to the challenges in RDH-EI, offering promising applications in fields such as medical imaging and cloud storage. Copyright © 2025, The Authors. All rights reserved.

关键词： Information hiding

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DPPAD-IE: Dynamic Polyhedra Permutating and Arnold Diffusing Medical Image Encryption Using 2D Cross Gaussian Hyperchaotic Map

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Quanjun Li, Qian Ling, Bingo Wing-Kuen Pun, Chi-Man Huang, Guoheng Yuan, Xiaochen Zhong, Guo Ayouni, Sarra Chen, Jianwu Guangdong University of Technology School of Advanced Manufacturing Jieyang522000 China Wuyi University School of Electronics and Information Engineering Jiangmen510006 China Guangdong University of Technology School of Information Engineering Guangzhou510006 China University of Macau Faculty of Science and Technology Macau China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Macau Polytechnic University Faculty of Applied Sciences Macau China Guangdong University of Foreign Studies School of Information Science and Technology Guangzhou510420 China Princess Nourah bint Abdulrahman University College of Computer and Information sciences Department of Information Systems Riyadh11671 Saudi Arabia Fujian Medical University Union Hospital Department of Radiation Oncology Fujian province Fuzhou China Fujian Key Laboratory of Intelligent Imaging and Precision Radiotherapy for Tumors Fujian Medical University Fujian province Fuzhou China Fujian province Fuzhou China

With the rapid improvement of medical consumer electronics (CE), the transmission of multiple images has become increasingly common in the medical field. During the transmission and storage of image data, sensitive information, including medical images, may be accessed by unauthorized parties, resulting in possible privacy violations. Consequently, several multi-image encryption (MIE) algorithms have been proposed to solve this real-world problem. Nevertheless, current MIE schemes exhibit several drawbacks, including insufficient security in permutation and diffusion algorithms, as well as the insensitivity of chaotic maps. This paper presents a new encryption algorithm called DPPAD-IE. The algorithm primarily consists of three components. First, we developed an innovative hyperchaotic map called the 2D Cross Gaussian Hyperchaotic Map (2D-CGHM), which has an expanded chaotic interval and is proficient in producing chaotic sequences with improved pseudorandomness. Second, we introduce a new Dynamic Polyhedra Permutation algorithm (DPP) that successfully alters pixel locations of the image, hence diminishing the connection between neighboring pixels. Third, we propose an Arnold Diffusion algorithm (AD), using chaotic sequences to further encrypt the permuted image. The DPPAD-IE is capable of encrypting images of any quantity and size concurrently. The experimental results confirm that this approach is both robust and effective, capable of defending against various security attacks. The code are available at:https://***/QuincyQAQ/DPPAD-IE. © 1975-2011 IEEE.

关键词： Medical imaging

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Realization of Dual-Band Topological States in Su–Schrieffer–Heeger-Like Photonic Crystal Slabs

SSRN

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SSRN 2023年

作者： Chen, Ying Zheng, Ze-Huan Wang, Hai-Xiao Wu, Feng Chen, Huanyang Department of physics College of Information Science and Engineering Huaqiao University Xiamen361021 China School of Computer Science and Information Engineering Xiamen Institute of Technology Xiamen361021 China School of Physical Science and Technology Guangxi Normal University Guilin541004 China School of Optoelectronic Engineering Guangdong Polytechnic Normal University Guangzhou510665 China Department of Physics College of Physical Science and Technology Xiamen University Xiamen361005 China

We design the bilayer mirror-symmetric (I-shaped) photonic crystal slabs supporting dual-band topological edge states simultaneously, which is confirmed by the topological phases and localized field distributions. By adjusting the coupling strengths at different positions of the I-shaped structure, the band structures can display different topology in different frequency bands, and the dual-band or single-band edge states can emerge flexibly at two or one band gaps, thus enabling high performance in manipulating topological states. Moreover, we also investigate the influence of broken mirror-symmetry [[EQUATION]] on the dual-band states. The study provides a promising way to realize multiband topological states and improve the efficiency of robust light transport. © 2023, The Authors. All rights reserved.

关键词： Mirrors

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