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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Sangeetha, M. Gunasekaran, Hemalatha Azalini, T. Nakshathra, J. Uma Mageshwari, P. Manjula Devi, R. Dr.N.G.P.Institute of Technology Department of Information Technology Tamilnadu Coimbatore India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350372748

Alzheimer's disease (AD) is a significant global health concern, accounting for the sixth highest cause of mortality worldwide. Early diagnosis is critical to improving patient care and treatment outcomes. This study aims to classify Alzheimer's disease (AD) using Magnetic Resonance Imaging (MRI) brain images, allowing for prompt intervention and optimal disease treatment. The dataset consists of 6400 MRI pictures classified into four categories: very mildly demented, mildly demented, moderately demented, and non-demented, with an 80-10-10 split for training, validation, and testing. Initially, the utilization of VGG16 architecture results with an accuracy of 87%. To improve accuracy and reduce training time, the GoogleNet (Inception V3) Convolutional Neural Network (CNN) architecture was used. This method resulted with an accuracy of 97.62%. © 2024 IEEE.

关键词： Magnetic resonance imaging

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AI-Powered Mental Health Diagnosis: A Comprehensive Exploration of Machine and Deep Learning Techniques

AI-Powered Mental Health Diagnosis: A Comprehensive Explorat...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： M.S. Supriya Antham Aniket Rakshitha N. M Aakanksha J Kenith Peter Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bengaluru India

Individuals suffering from mental illnesses frequently communicate their sentiments and emotional states on social media through their posts. It is a challenge to recognize individuals suffering from mental health illnesses at an early stage and to provide them with necessary treatment. In this paper, an experimental methodology based on publicly available Reddit data to develop a mental health diagnosis system has been proposed. Natural language processing (NLP) techniques are exploited to help machine understand social media posts from Reddit, and deep learning and machine learning models are trained to classify the social media posts into one of the following four mental illnesses: depression, anxiety, schizophrenia and bipolar. A comparative study of these models' is performed and the results revealed that random forest outperformed other models, by obtaining 90% accuracy. In this study, we also design a web-based platform for deploying the model to aid in diagnosis of mental diseases because people suffering from anxiety or depression refrain from sharing their feelings with doctors, relatives, family, or friends.

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Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation

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IEEE Access 2024年

作者： Stuhr, Bonifaz Brauer, Jurgen Schick, Bernhard Gonzalez, Jordi Autonomous University of Barcelona Department of Computer Science Bellaterra Barcelona08193 Spain University of Applied Sciences Kempten Department of Computer Science Kempten87435 Germany University of Applied Sciences Kempten Department of Mechanical Engineering Kempten87435 Germany

A common goal of unpaired image-to-image translation is to preserve content consistency between source images and translated images while mimicking the style of the target domain. Due to biases between the datasets of both domains, many methods suffer from inconsistencies caused by the translation process. Most approaches introduced to mitigate these inconsistencies do not constrain the discriminator, leading to an even more ill-posed training setup. Moreover, none of these approaches is designed for larger crop sizes. In this work, we show that masking the inputs of a global discriminator for both domains with a content-based mask is sufficient to reduce content inconsistencies significantly. However, this strategy leads to artifacts that can be traced back to the masking process. To reduce these artifacts, we introduce a local discriminator that operates on pairs of small crops selected with a similarity sampling strategy. Furthermore, we apply this sampling strategy to sample global input crops from the source and target dataset. In addition, we propose feature-attentive denormalization to selectively incorporate content-based statistics into the generator stream. In our experiments, we show that our method achieves state-of-the-art performance in photorealistic sim-to-real translation and weather translation and also performs well in day-to-night translation. Additionally, we propose the cKVD metric, which builds on the sKVD metric and enables the examination of translation quality at the class or category level. © 2013 IEEE.

关键词： Generative adversarial networks

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Design of Evolutionary Algorithm Based Unequal Clustering for Energy Aware Wireless Sensor Networks

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computer Systems science & engineering 2023年第10期47卷 1283-1297页

作者： Mohammed Altaf Ahmed T.Satyanarayana Murthy Fayadh Alenezi E.Laxmi Lydia Seifedine Kadry Yena Kim Yunyoung Nam Department of Computer Engineering College of Computer Engineering&SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Chaitanya Bharathi Institute of Technology HyderabadTelanganaIndia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeKristiansandNorway Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of ICT Convergence Soonchunhyang UniversityAsan 31538Korea

Wireless Sensor Networks(WSN)play a vital role in several real-time applications ranging from military to *** the benefits of WSN,energy efficiency becomes a major part of the challenging issue in WSN,which necessitate proper load balancing amongst the clusters and serves a wider monitoring *** clustering technique for WSN has several benefits:lower delay,higher energy efficiency,and collision *** clustering protocol has several *** a large-scale network,cluster-based protocols mainly adapt multi-hop routing to save energy,leading to hot spot problems.A hot spot problem becomes a problem where a cluster node nearer to the base station(BS)tends to drain the energy much quicker than other nodes because of the need to implement more *** article introduces a Jumping Spider Optimization Based Unequal Clustering Protocol for Mitigating Hotspot Problems(JSOUCP-MHP)in *** JSO algorithm is stimulated by the characteristics of spiders naturally and mathematically modelled the hunting mechanism such as search,persecution,and jumping skills to attack *** presented JSOUCPMHP technique mainly resolves the hot spot issue for maximizing the network *** JSOUCP-MHP technique elects a proper set of cluster heads(CHs)using average residual energy(RE)to attain *** addition,the JSOUCP-MHP technique determines the cluster sizes based on two measures,i.e.,RE and distance to BS(DBS),showing the novelty of the *** proposed JSOUCP-MHP technique is examined under several experiments to ensure its *** comparison study shows the significance of the JSOUCPMHP technique over other models.

关键词： Wireless sensor networks energy efficiency cluster heads unequal clustering hot spot issue lifetime enhancement

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ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

ProspectCV: LLM-Based Advanced CV-JD Evaluation Platform

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Students Conference on engineering and Systems (SCES)

作者： Ghanashyam Vagale Shashidhar Y Bhat P. Padma Priya Dharishini Prajwal GK Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India

ISBN: (数字)9798350374711

ISBN: (纸本)9798350374728

This paper presents a novel framework that utilizes Google's Gemini Pro Vision large language model (LLM) and natural language processing (NLP) techniques to analyze and compare resumes or CVs with job descriptions. The framework not only evaluates the compatibility between the resume and job description but also provides personalized feedback and suggestions for improvement. By identifying potential gaps in skills and qualifications, and recommending relevant resources, the framework assists both human resources professionals in streamlining candidate shortlisting and empowers candidates to enhance their CVs. Gemini processes a resume as a Base64-encoded string and cleans up the job description using NLP. It then generates a personalized report with insights, including a compatibility score, skills analysis, improvement suggestions, and resource recommendations. Initial results demonstrate the framework's effectiveness in accurately assessing candidate suitability and providing valuable feedback. Future work will focus on expanding the framework to analyze multiple resumes simultaneously and developing more sophisticated algorithms for enhanced accuracy and reliability. This framework has the potential to revolutionize the recruitment process by streamlining candidate selection, empowering candidates to improve their CVs, and ultimately leading to better candidate job matches and a more efficient and equitable hiring landscape.

关键词： Accuracy Large language models Heuristic algorithms Resumes Natural language processing Internet Reliability

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Proposed National Artificial Intelligence Strategy

Proposed National Artificial Intelligence Strategy

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Bilal Zahran Belal Ayyoub Department of computer science University of Petra Jordan Department of computer information system Al-Balqa Applied University Jordan Department of Electrical Engineering Al-Balqa Applied University Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The rapid pace of development in Artificial Intelligence (AI) necessitates formulating effective national strategies for its maximization and minimizing its accompanying repercussions. In this article, a proposed National Artificial Intelligence Strategy for Jordan is formulated, with an orientation towards a systemic and organized national structure relevant to Jordan's socio-economic environment. What is new in this proposed National AI Strategy is its localized orientation, blending AI-facilitated economic development, governance, and ethics relevant to Jordan. It also formulates a governance model, with coordination between principal entities such as government, academia, and private sectors, for effective implementation. The proposed strategy sets specific digital transformation targets, in consonance with Jordan's Vision 2030 and National Strategy for Digital Transformation (2021–2025). AI use in critical sectors such as national security, energy, healthcare, and education takes a top position, with a view towards adhering to regulations, utilizing AI in an ethical manner, and developing technology in a sustainable manner. Outcomes include enhancing AI literacy, diversifying economies, enhancing employment in AI-related industries, and national competitiveness at a global level. This work forms part of an emerging portfolio of studies in national AI strategies and sets a country-specific model in consonance with Jordan's specific development aims, offering a model for AI strategy development in emerging economies.

关键词： Ethics Digital transformation Medical services Regulation Proposals Artificial intelligence Socioeconomics National security Portfolios Videos

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Design of A Rotated 3D-Printed Lens Antenna Assembly with An Ultra-Wideband Antenna for Beam Steering Applications 12

Design of A Rotated 3D-Printed Lens Antenna Assembly with An...

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12th International Electrical engineering Congress, iEECON 2024

作者： Sangpet, Patchadaporn Thaiwirot, Wanwisa Akkaraekthalin, Prayoot Chudpooti, Nonchanutt King Mongkut's University Of Technology North Bangkok Bangkok Faculty Of Engineering Department Of Electrical And Computer Engineering Bangkok Thailand King Mongkut's University Of Technology North Bangkok Bangkok Faculty Of Applied Science Department Of Industrial Physics And Medical Instrumentation Bangkok Thailand

ISBN: (纸本)9798350383591

The paper presents the rotated 3D-printed dielectric lens assembly with an ultra-wideband (UWB) antenna for use in the beam-steering applications. The focus is on achieving beam steering by combining the rotated 3D-printed dielectric lens assembly with a hemispherical lens and UWB antenna. The UWB antenna is fabricated on an FR-4 substrate, while the hemispherical lens and the rotated 3D-printed lens assembly are made from Acrylonitrile Butadiene Styrene (ABS). To enable the hemispherical lens to rotate to the specified angle, a set of rotatable 3D-printed lenses was etched to determine five angles: -60degrees, -30degrees, 0degrees, 30degrees, and 60degrees. Both simulation and measurement results indicate that at a beginning frequency of 3.5 GHz, the beam cannot be steered when using the UWB antenna with the 3D-printed ABS lens rotated hemispherical lens assembly. However, at middle and high frequencies of 7.0 GHz and 10.0 GHz, respectively, the beam can be steered according to the direction of rotation of the hemispherical lens. © 2024 IEEE.

关键词： Microstrip antennas

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A Hybrid GRU-MHA model for accurate battery RUL forecasting with feature selection

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Energy Reports 2025年 14卷 294-309页

作者： Abeer Aljohani Saad Aljohani Department of Computer Science Applied College Taibah University Medina 42353 Saudi Arabia Electrical and Computer Engineering Western Michigan University Kalamazoo USA

The State of Health (SoH) and Remaining Useful Lifetime (RUL) forecasting of batteries is an important part of the health prognostics of electronic devices. This research presents a combination of Deep Learning (DL) and Machine Learning (ML) models to forecast the RUL battery’s linear and nonlinear behavior. The proposed method is separated into two phases. The initial phase separates the features into correlated and uncorrelated features, and the second phase determines the architecture of the proposed methods based on the two sets of correlated and uncorrelated features. The proposed method combines Gated Recurrent Units (GRU) and Multi Head Attention (MHA) with dense layers as the memory units. Finally, ridge regression is used for final forecasting. NMC-LCO 18650 and NASA lithium batteries datasets are used to evaluate the proposed method. Experimental results indicate 0.002 MAE, 0.044 MSE, and 99.99 R2 score for RUL forecasting of NMC-LCO 18650. The evaluation results on NASAB0005 indicated 0.005 MAE, 0.0706 MSE, and 99.64 R2 score for capacity estimation. The proposed method is compared with similar research and ML models such as Support Vector Regression (SVR) and DL models like Transformers and outperformed them. This research indicates superior performance in predicting declining trends of battery capacity using GRU and MHA with feature selection.

关键词： Attention Gated Recurrent Units Remaining Useful Lifetime Heath prognostics

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

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

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

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

ISBN: (纸本)9783031644948

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

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

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Enhanced prairie dog optimization with Levy flight and dynamic opposition-based learning for global optimization and engineering design problems

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Neural Computing and Applications 2024年第19期36卷 11137-11170页

作者： Biswas, Saptadeep Shaikh, Azharuddin Ezugwu, Absalom El-Shamir Greeff, Japie Mirjalili, Seyedali Bera, Uttam Kumar Abualigah, Laith Department of Mathematics National Institute of Technology Agartala Tripura Agartala India Unit for Data Science and Computing North-West University 11 Hoffman Street Potchefstroom2520 South Africa Faculty of Natural and Agricultural Sciences School of Computer Science and Information Systems North-West University Vanderbijlpark South Africa Centre for Artificial Intelligence Research and Optimization Torrens University Australia Fortitude Valley BrisbaneQLD4006 Australia Research Center University of Tabuk Tabuk71491 Saudi Arabia Computer Science Department Al al-Bayt University Mafraq25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia College of Engineering Yuan Ze University Taoyuan32003 Taiwan MEU Research Unit Middle East University Amman11831 Jordan

This study proposes a new prairie dog optimization algorithm version called EPDO. This new version aims to address the issues of premature convergence and slow convergence that were observed in the original PDO algorithm. To improve performance, several modifications are introduced in EPDO. First, a dynamic opposite learning strategy is employed to increase the diversity of the population and prevent premature convergence. This strategy helps the algorithm avoid falling into local optima and promotes global optimization. Additionally, the Lévy dynamic random walk technique is utilized in EPDO. This modified Lévy flight with random walk reduces the algorithm’s running time for the test function’s ideal value, accelerating its convergence. The proposed approach is evaluated using 33 benchmark problems from CEC 2017 and compared against seven other comparative techniques: GWO, MFO, ALO, WOA, DA, SCA, and RSA. Numerical results demonstrate that EPDO produces good outcomes and performs well in solving benchmark problems. To further validate the results and assess reliability, the authors employ average rank tests, the measurement of alternatives, and ranking according to the compromise solution (MARCOS) method, as well as a convergence report of EPDO and other algorithms. Furthermore, the effectiveness of the EPDO algorithm is demonstrated by applying it to five design problems. The results indicate that EPDO achieves impressive outcomes and proves its capability to address practical issues. The algorithm performs well in solving benchmark and practical design problems, as supported by the numerical results and validation methods used in the study. © The Author(s) 2024.

关键词： Global optimization

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