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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Naudiyal, Anjali Singh, Vineeta Joshi, Kapil Roka, Sanjay Mamoria, Pushpa Shakya, Rajeev Kumar School of Applied and Life Sciences Uttaranchal University Premnagar Dehradun India School of Engineering and Technology Chhatrapati Shahu Ji Maharaj University Department of Computer Science and Engineering Uttar Pradesh Kanpur208024 India Uttaranchal University Department of Computer Science & Engineering Dehradun India Graphic Era deemed to be university dehradun India School of Electrical Engineering and Computing Adama Science and Technology University Department of Electronics and Communication Engineering Adama Ethiopia

ISBN: (纸本)9798350379716

In the era of artificial intelligence, academic activities tend to use Chat GPT more frequently. A Chabot called Chat GPT is used for brainstorming, writing, and learning. Although most academicians use Chat GPT to their advantage when taking notes, it is detrimental to research. A few authors wrote the papers swiftly by using AI techniques. These days, chat GPT is employed in a wide range of tasks. It prepares any type of content for the internet. The likelihood of plagiarism is rising as a result of its use. At present, plagiarism is the biggest challenge in the field of research. Plagiarism occurs when an author uses this technology to modify another person's work and transmit it off as their own without providing a reference or credit. Plagiarism is a term used to describe when someone copies something from another source without providing credit to the original author. Google is working quickly in the image field, but at that point, it will be used to detect plagiarism that is generated with the aid of artificial intelligence. The less papers that are utilized in favor of online resources, the more academics are working to make the globe more environmentally friendly. In the presented paper, a few software applications are discussed and compare a number of different techniques to detect the plagiarism in academics field like scientific equations or cross-lingual or multilingual texts. Various applications are developed in research to identify instances of plagiarism in research papers, assignments, and source code. After reviewing the papers pertaining to various tools for detecting plagiarism, not much more work is done in the area of detecting plagiarism in images or in image text. The article that is being presented comes to the conclusion that no software tool has been developed to identify plagiarism in images or in the text associated with images. © 2024 IEEE.

关键词： Application programs

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Modified Sine Cosine Optimization with Adaptive Deep Belief Network for Movie Review Classification

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Intelligent Automation & Soft computing 2023年第7期37卷 283-300页

作者： Hala J.Alshahrani Abdulbaset Gaddah Ehab S.Alnuzaili Mesfer Al Duhayyim Heba Mohsen Ishfaq Yaseen Amgad Atta Abdelmageed Gouse Pasha Mohammed Department of Applied Linguistics College of LanguagesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of English College of Science&Art at MahayilKing Khalid UniversityMuhayel Aseer63311Saudi Arabia Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversityAl-Aflaj16828Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sentiment analysis(SA)is a growing field at the intersection of computer science and computational linguistics that endeavors to automati-cally identify the sentiment presented in *** linguistics aims to describe the fundamental methods utilized in the formation of computer methods for understanding natural *** is classified as a negative or positive assessment articulated through *** can be commonly used for the movie review classification that involves the automatic determination that a review posted online(of a movie)can be negative or positive toward the thing that has been *** learning(DL)is becoming a powerful machine learning(ML)method for dealing with the increasing demand for precise *** this motivation,this study designs a computational intelligence enabled modified sine cosine optimization with a adaptive deep belief network for movie review classification(MSCADBN-MVC)*** major intention of the MSCADBN-MVC technique is focused on the identification of sentiments that exist in the movie review ***,the MSCADBN-MVC model follows data pre-processing and the word2vec word embedding *** the classification of sentiments that exist in the movie reviews,the ADBN model is utilized in this *** last,the hyperparameter tuning of the ADBN model is carried out using the MSCA technique,which integrates the Levy flight concepts into the standard sine cosine algorithm(SCA).In order to demonstrate the significant performance of the MSCADBN-MVC model,a wide-ranging experimental analysis is performed on three different *** comprehensive study highlighted the enhancements of the MSCADBN-MVC model in the movie review classification process with maximum accuracy of 88.93%.

关键词： Computational linguistics movie review analysis sentiment analysis sentiment classification deep learning

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Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings

Mental Health Stigma: Strategies for Destigmatization in Hea...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Verma, Aishwarya Panda, Krishna Tanaya Sharma, Vandana Tripathy, Hrudaya Kumar Al-Khasawneh, Mahmoud Ahmad Alkhayyat, Ahmed Kalinga Institute of Industrial Technology India CHRIST University Computer Science Department India Skyline University College University City Sharjah School of Computing Sharjah1797 United Arab Emirates Applied Science Private University Applied Science Research Center Amman Jordan Jadara University Research Center Jadara University Jordan The Islamic University College of Technical Engineering Najaf Iraq

ISBN: (纸本)9798350378092

Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. © 2024 IEEE.

关键词： Electronic health record

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Learning Channels to Support Interaction and Collaboration in CourseMapper 22

Learning Channels to Support Interaction and Collaboration i...

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Proceedings of the 14th International Conference on Education Technology and computers

作者： Qurat Ul Ain Mohamed Amine Chatti Shoeb Joarder Ilia Nassif Benjamine Stella Wobiwo Teda Mouadh Guesmi Rawaa Alatrash Social Computing Group Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Germany

ISBN: (纸本)9781450397766

Massive Open Online Courses (MOOCs) have proved to be revolutionary in the development and widespread of educational technology over the past few years. It has been identified that these systems, despite all the benefits, lack interaction and collaboration around learning materials. This paper explores how to leverage learning channels to support interaction and collaboration in MOOC environments. We present the design, implementation, and evaluation details of learning channels in CourseMapper, a MOOC platform which supports collaboration and interaction around learning materials using collaborative Pdf and video annotations. More specifically, we report on the application and results of a human-centered design (HCD) approach used to systematically design learning channels. Three design iterations with five new users in each iteration (N=15) were conducted from an initial user interview to the final design. After the development, user experience with the system was evaluated with end users (N=10) based on the Technology Acceptance Model (TAM). The evaluation results show that the introduction of learning channels provides a valuable mechanism to support effective interaction and collaboration in a MOOC environment.

关键词： Human-Centered Design (HCD) Technology-Enhanced Learning (TEL) Learning channels Collaborative annotation Collaborative learning Massive Open Online Course (MOOC)

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Adaptive energy-preserving algorithms for guiding center system

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Plasma science and Technology 2023年第4期25卷 11-22页

作者：朱贝贝刘健张嘉炜祝爱卿唐贻发 Department of Applied Mathematics School of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing 100083Peopleʼs Republic of China School of Nuclear Science and Technology University of Science and Technology of ChinaHefei 230026Peopleʼs Republic of China Advanced algorithm Joint Lab Shandong Computer Science CenterQilu University of TechnologyJinan 250014Peopleʼs Republic of China The State Key Laboratory of Scientific and Engineering Computing The Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190Peopleʼs Republic of China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049Peopleʼs Republic of China

We develop two types of adaptive energy preserving algorithms based on the averaged vector field for the guiding center dynamics,which plays a key role in magnetized *** adaptive scheme is applied to the Gauss Legendre’s quadrature rules and time stepsize respectively to overcome the energy drift problem in traditional energy-preserving *** new adaptive algorithms are second order,and their algebraic order is carefully *** results show that the global energy errors are bounded to the machine precision over long time using these adaptive algorithms without massive extra computation cost.

关键词： guiding center system energy-preserving methods adaptive energy conservation

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A Collaborators Recommendation Method Based on Multi-feature Fusion 1

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3rd International Conference on Neural computing for Advanced Applications, NCAA 2022

作者： Yuan, Qi Shao, Lujiao Zhang, Xinyu Yu, Xinrui Sun, Huiyue Ma, Jianghong Meng, Weizhi Gao, Xiao-Zhi Zhang, Haijun Departmant of Computer Science Harbin Institudte of Technology Shenzhen China Department of Applied Mathematics and Computer Science Technical University of Denmark Kongens Lyngby Denmark School of Computing University of Eastern Finland Kuopio Finland

ISBN: (数字)9789811961427

ISBN: (纸本)9789811961410

This research introduces a new collaborators recommendation model based on multi-feature fusion. Specifically, we use a tree structure to integrate scholar information and extract content features from a scholar tree by using a Tree2vector-CLE model. Then, from the heterogeneous academic network, we extract the meta-path feature between scholars, which quantifies the similarity and co-operation potential between scholars from a multidimensional perspective. By combining content and meta-path features, we reconstruct a co-authorship network. Finally, we use the network representation learning method to represent the nodes in the reconstructed co-authorship network where the top-k collaborators are recommended for the target scholar with the random walk strategy controlled by the meta-path feature weighting. Experimental results on a real dataset demonstrate that our proposed method is effective in the task of collaborators recommendation. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Learning systems

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Reseed Skipping of Linear Congruential Generator for Multi-level Image Steganography Security of Financial Data

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SN computer science 2023年第2期4卷 124页

作者： Danso, Juliana Mantebea Missah, Yaw Marfo Gyamfi, Enoch Opanin Dankwa, Stephen Kwabena, Sarpong Department of Computer Science Kwame Nkrumah University of Science and Technology (KNUST) Kumasi Ghana School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Cyber Security and Computer Engineering Technology School of Computing and Information Sciences C.K. Tedam University of Technology and Applied Sciences Upper East Region Navrongo Ghana School of Automation Engineering University of Electronic Science and Technology of China Chengdu China

This paper proposed a multi-level image steganography model that integrates with image and plaintext encryption schemes. The model starts by partitioning the cover-image pixel positions into two regions of respective purposes, one is reserved to hold the data, and through the implementation of the dynamic difference expansion principle, the other sloughs some parts to the reserved region in responsive to the size of the data to embed. Continuously reseeding and initializing the parameter values of a Linear Congruential Generator (LCG), generate a set of random pixel positions as members of the reserved region. Before data embedding, the region reserved to hold the data as well as the data bits to embed, are then encrypted using AES with respective keys. Encryption keys are randomly generated using a Cryptographically Secure Pseudo-Random Number Generator (CSPRNG), the Fortuna algorithm. Multiplex security nature of using a randomization generator to reserve image region for encryption and encrypting plaintext data before performing steganography still ensures stability in computational complexities better than a logarithmic O(n log n). A visual inspection by evaluators who made 3000 choices to correctly differentiate stego images from its cover-images shows that only 47 choices were correct, 1884 choices were wrong, while 1069 choices were uncertain. An experiment shows an average Embedding Rates (ERs) of 0.00109 bpp and an average PSNR of 72. This concludes, as our integrated steganography and image encryption which implements the dynamic difference expansion principle, outputs encrypted stego mages that can deceive the naked human eyes into identifying them as rather cover-images, its embedding rate is faster (i.e. ER < 0.1), suitably good, and ensures better imperceptibility of cover-image alterations. When comparing these experimental results with existing methods, our proposed model shows significant competitiveness. We even show real-life scenarios on how this

关键词： AES encryption scheme Dynamic difference expansion principle Linear congruential generator Pseudo-random number generator Reserving room before encryption Steganography

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Mathematical model of COVID-19 dynamics in the presence of multiple controls

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Quality and Quantity 2024年第1期59卷 261-290页

作者： Akanni, J.O. Fatmawati Ajao, S. Asamoah, J.K.K. Abimbade, S.F. Department of Mathematical and Computing Sciences Koladaisi University Oyo State Ibadan Nigeria Department of Mathematics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia Department of Mathematics and Computer Science Elizade University Ondo State Ilara-Mokin Nigeria Department of Mathematics Saveetha School of Engineering SIMATS Chennai India Department of Mathematics Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Pure and Applied Mathematics Ladoke Akintola University of Technology Oyo State Ogbomoso Nigeria

The coronavirus disease 19 (COVID-19), caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), is an infectious disease constituting the most significant challenge to human and socio-economic development across the globe. Many studies have been conducted to gain more knowledge on the dynamic spread of SARS-COV-2 in the community of people. In this study, a new mathematical model made up of a system of non-linear ODEs is designed and meticulously analyzed to understand the dynamics of transmission of COVID-19. Specific necessary properties exhibited by the COVID-19 model are examined through the theory of non-negativity and boundedness of solutions. The threshold parameter measuring the invasion potential of COVID-19 in the population is worked out by employing the Next Generation Matrix approach. The model is shown to have two endemic equilibria whenever basic reproduction number R₀<1. The analysis of the endemic equilibrium stability is explored using a suitably constructed Lyapunov function. It is shown that the COVID-19 model is globally asymptotically stable whenever the associated basic reproduction number is beyond unity. The Latin Hypercube Sampling or Partial Rank Correlation Coefficient is utilized to examine how parameter changes affect the COVID-19 spread in the population. Furthermore, the study considers six optimal control interventions to flatten the curve of the coronavirus pandemic in society. Numerical experiments of the COVID-19 model are executed in MatLab to corroborate the qualitative analysis of the model. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： 49J15 cost-effectiveness analysis COVID-19 Dynamical system LHS/PRCC Mathematical modelling Optimal control analysis Sensitivity analysis

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Streamlining Task Planning Systems for Improved Enactment in Contemporary computing Surroundings

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SN computer science 2024年第8期5卷 993页

作者： Menon, Sindhu Addula, Santosh Reddy Parkavi, A. Subbalakshmi, Ch. Dhandayuthapani, V. Bala Pokkuluri, Kiran Sree Soni, Anita School of Computing and Information Technology REVA University Karnataka Bangalore India Department of Information Technology University of the Cumberlands Williamsburg KY United States Dept of CSE M S Ramaiah Institute of Technology Bangalore India Department of Computer Science and Engineering Guru Nanak Institution Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences Shinas Campus Oman Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India CSE Department IES University Bhopal Madhya Pradesh Bhopal India

Task planning algorithms are essential for maximizing efficiency and enhancing performance in modern computing systems, given the escalating demand for computational resources. This paper delves into the effectiveness of various scheduling algorithms across different computing environments, characterized by their unique workload dynamics and resource limitations. This method integrates adaptive algorithms and machine learning to dynamically optimize task planning, enhancing performance and efficiency in cloud, IoT, and distributed computing environments. These algorithms are crucial for optimizing key factors such as task completion times, job prioritization, and resource allocation while also addressing quality of service (QoS), cost, reliability, and specific resource needs. One significant challenge is that many task scheduling approaches do not account for the potential failure of tasks or resources. While some strategies effectively reduce overall completion time (makespan), they can result in severe workload imbalances. To address these issues, our study proposes a new approach that harnesses the processing capabilities of grid systems, thus boosting application performance and throughput. Our algorithm particularly focuses on balancing workload and accelerating scheduling operations, even in the face of grid node failures, by incorporating QoS metrics. This allows for a more robust and adaptable scheduling solution. Comparative analysis with existing methods demonstrates that our algorithm not only improves resource utilization but also significantly diminishes flow time and makespan, confirming its efficacy. Through this research, we contribute to the evolving field of autonomous task scheduling, presenting a solution that responds dynamically to changing environmental conditions and workload demands. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CPU Flow time Makespan MSHA QoS

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Image Based Feature Separation Using RBM Tech with ADBN Tech for Accurate Fruit Classification 5

Image Based Feature Separation Using RBM Tech with ADBN Tech...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Revathi, V. Kavin, Balasubramanian Prabhu Thirumalraj, Arunadevi Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Department of Applied Sciences - Physics Ring Road Bellandur Post Bengaluru India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Tamil Nadu Kattankulathur603 203 India K.Ramakrishnan College of Technology Department of Computer Science Engineering Tamil Nadu Trichy India Loyola College Department of Computer Science Chennai600034 India Delhi India Symbiosis Institute of Technology Pune Aiml Department Pune412115 India

ISBN: (纸本)9798350383522

One of the fastest-growing subfields of computer vision and picture categorization is fruit identification. The fruit industry could start using a standardised categorization system so that shoppers can easily distinguish between different types and quality levels of fruit. From a health perspective, fruit quality is essential. The task of classifying fruits is crucial in many commercial contexts. A supermarket cashier may use a fruit categorization system to better recognise the varieties of fruit and their associated costs. It might also be used to determine whether or not a given kind of fruit is suitable for a person's diet. The rising use of intelligent imaging equipment may be directly attributed to the proliferation and development of AI software. Deep learning methods, such as CNN, are now being used by researchers for picture categorization. CNN's superior performance may be achieved without using any manually-crafted characteristics, in contrast to the older methods. For automated categorization, it employs a plethora of filters that strip relevant visual information. Classifying fruits accurately is a challenge in the horticulture sector and calls for the knowledge of an expert. For accurately and quantitatively analysing fruits, the currently mentioned categorization techniques fall short. Therefore, it is beneficial to review the latest suggestions for categorizing fruits. To solve this problem, we need a computerised system that can automatically sort fruits into their respective categories. Deep learning models, such as the Restricted Boltzmann Machine (RBM) and the Adam based Deep Belief Network (DBN), are used in this assignment to categorise fruits according to the attributes that have been determined to be optimum and generated. The proposed approach has been shown to have effective accuracy and quantitative analysis outcomes, and this is just the beginning of this realisation. In addition, the suggested scheme's relatively strong computational mo

关键词： Deep Belief Network Fruits Classification Fruits Grading Handcrafted features Restricted Boltzmann Machine

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