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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Madhuri Tiwari Neeraj Giri Sanjay Kumar Suman Amit Kumar Chandanan Subhashini Peneti Shibanjali Choudhury Dept of Data Science GGITS Jabalpur M.P India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P India Dept. of ECE St. Martin’s Engineering College Secunderabad Telangana India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Bilaspur C G India Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept. of CSE Techno College of Engineering Agartala Tripura India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The innovative Gender-Empowered Leadership Development (GELD) programme promotes gender equality in fast-paced technology and machine learning leadership positions. This was a US government initiative. BiasMitigator, Skillenhancer, and CultureCatalyst work together to eliminate gender prejudice, improve employee skills, and improve workplace morale. Programs collaborate to achieve these aims. CultureCatalyst promotes a pleasant environment, SkillEnhancer improves skills over time, and BiasMitigator eliminates gender prejudice across the firm. All three projects assist the organization achieve its aims. This study compares GELD to normal approaches to better understand the situation. This illustrates how intricate its procedure is.

关键词： Leadership Government Employment Machine learning Intelligent systems Gender issues

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Temara, Sheetal Samanthapudi, Sagar Varma Rohella, Piyush Gupta, Ketan Ashokkumar, S.R. University of The Cumberlands Dept of Computer & Information Sciences WilliamsburgKY United States The Polis Center Indiana University Purdue University Indianapolis Applied Data Science IndianapolisIN United States Payments & Commerce GoDaddy EdmondsWA United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Centre For Blockchain and Cyber Security Department of Computer and Communication Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350394399

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure. © 2024 IEEE.

关键词： Artificial intelligence

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Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Computer Vision-Enabled Smart Traffic Monitoring for Sustain...

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International Conference on Transportation and Development 2022, ICTD 2022

作者： Shao, Yunli Wang, Chieh Ross Berres, Andy Yoshioka, Jovan Cook, Adian Xu, Haowen Buildings and Transportation Science Division Oak Ridge National Laboratory Oak RidgeTN United States Computational Sciences Engineering Division Oak Ridge National Laboratory Oak RidgeTN United States Dept. of Electrical Engineering and Computer Science Univ. of Tennessee Knoxville KnoxvilleTN United States

ISBN: (数字)9780784484319

ISBN: (纸本)9780784484319

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy's Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas. © ASCE.

关键词： Energy utilization

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Comprehensive Evaluation of Vision Transformer in MRI-based Brain Tumor Detection

Comprehensive Evaluation of Vision Transformer in MRI-based ...

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International Conference on Computer and Applications (ICCA)

作者： Alaa E. Ahmed Muhammad A. Elwany Muhammad A. Mahmoud Mohamed A. Fouad Muhammad A. Al-Ghareeb Tarek Dahab Mohamed R. Shoaib Ahmed F Abdallah Gamal A. El-Sheikh Heba M. Emara dept. of Electronic and Electrical Engineering PHI for Engineering and Technology Giza Egypt Shgardi Company Shgardi Company Riyadh Saudi Arabia College of Computing and Data Science (CCDS) Nanyang Technological University (NTU) Singapore

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Brain tumor detection using magnetic resonance imaging (MRI) plays a pivotal role in early diagnosis and treatment planning. This study presents a novel approach for brain tumor detection using MRI images, preceded by prepro-cessing steps including contrast enhancement, noise removal, and normalization. Leveraging the Vision Transformer (ViT) architecture, the proposed method is examined across different patch sizes (8×8, 16×16, 32×32, 64×64) to comprehensively evaluate its performance. Evaluation is conducted on the Brain Tumor MRI dataset, comparing the proposed method with four pretrained models: ResNet-50, DenseNet121, EfficientNetB0, and Inception V3. Performance metrics including accuracy, precision, recall, and F-score are employed for evaluation. Notably, the proposed Vision Transformer achieves an accuracy of 99% with a patch size of 8×8, 97% with a patch size of 16×16, 96% with a patch size of 32×32, and 93% with a patch size of 64×64. These results highlight the superior efficacy of the proposed approach in brain tumor detection compared to the pretrained models. Comparative analysis with state-of-the-art methods further confirms the proposed approach's superiority, indicating its potential for enhancing diagnostic accuracy in clinical settings. This study underscores the promise of Vision Transformer models in medical imaging tasks and their potential for advancing medical diagnostics.

关键词： Measurement Computer vision Accuracy Magnetic resonance imaging Noise Brain tumors Transforms Transformers Brain modeling Medical diagnosis

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Design and Implementation of a Conference Attendance Monitoring System Using Blockchain and AI Technologies 2

Design and Implementation of a Conference Attendance Monitor...

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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Vubangsi, Mercel Al-Turjman, Fadi Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept. Mersin 10 Nicosia Turkey Htttc Bambili University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Mersin 10 Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

In this work, we design and implement a conference attendance monitoring system based on blockchain and Artificial Intelligence (AI) technologies. This system is built with *** for front-end and makes use of Google's real-time database for storage. The system is designed to auto-track the users device location and screen behavior during a time slot allocated to a conference or conference session for which the user has subscribed. AI powered analytics of the data using custom mathematical models and JavaScript Machine Learning Libraries permits decision making on the data, facilitating the task on the conference organizers with regards to rating attendee performances. © 2022 IEEE.

关键词： Smart contract

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Multilevel Modelling of Parkinson’s Disease Symptom Progression in 123I FP-CIT SPECT

Multilevel Modelling of Parkinson’s Disease Symptom Progres...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Jose Antonio Simón Rodríguez F. J. Martinez-Murcia J. Ramirez D Castillo-Barnes J.M. Gorriz Dept. of Signal Theory Networking and Communications Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Granada Spain

Parkinson’s disease (PD) is the second most prevalent neurodegenerative disorder. It is due to the reduction in the neurotransmitter dopamine by the loss of dopamine neurons. Thanks to new high-precision markers, it is possible to study PD before the development of symptoms and thus to model the progression of the disease. In this study, we apply a non-linear decomposition of 123I FP-CIT SPECT imaging in order to unveil the sources of variability, and relate them to symptomatology and disease progression. Isomap was compared to PCA, and multi-level modelling was used to assess the relation of the resulting composite imaging features and different symptomatology biomarkers and neuropsychological scales. The main source of variability was found to be the intensity at the striata, significantly linked to progression scales such as UPDRS or SCOPA. On the other hand, the modelling found additional sources of variance related to rigidity, tremor or other relevant symptoms.

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OCI Runtime Comparison and Analysis Study 5

OCI Runtime Comparison and Analysis Study

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5th IEEE International Conference on Artificial Intelligence and Knowledge engineering, AIKE 2022

作者： Jeon, Sung Hwan Shim, Simon Chung, Haejin Nah, Yunmook Dept. of Data Science Graduate School Dankook University Yongin-si Korea Republic of Dept. of Computer Engineering San Jose State University San Jose United States Cha Mirisa College Duksung Women's University Seoul Korea Republic of Dept. of Applied Computer Eng. Dankook University Yongin-si Korea Republic of

ISBN: (数字)9781665471206

ISBN: (纸本)9781665471206

Containers are one of the technologies required for cloud services. Because containers use resources directly on the host, they are lighter and faster than traditional virtual machines. However, it has the disadvantage of poor security and isolation. To solve this problem, security-oriented runtimes such as Kata, gVisor, and Firecracker have emerged. Each runtime is created differently, so performance and resource usage are different. In this study, performance and evaluation of each lowlevel runtime are conducted. © 2022 IEEE.

关键词： Containers

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DEAL-YOLO: Drone-based Efficient Animal Localization using YOLO

arXiv

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arXiv 2025年

作者： Naidu, Aditya Prashant Gosalia, Hem Gakhar, Ishaan Rathore, Shaurya Singh Didwania, Krish Verma, Ujjwal Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India Dept. of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal India

Although advances in deep learning and aerial surveillance technology are improving wildlife conservation efforts, complex and erratic environmental conditions still pose a problem, requiring innovative solutions for cost-effective small animal detection. This work introduces DEAL-YOLO, a novel approach that improves small object detection in Unmanned Aerial Vehicle (UAV) images by using multi-objective loss functions like Wise IoU (WIoU) and Normalized Wasserstein Distance (NWD), which prioritize pixels near the centre of the bounding box, ensuring smoother localization and reducing abrupt deviations. Additionally, the model is optimized through efficient feature extraction with Linear Deformable (LD) convolutions, enhancing accuracy while maintaining computational efficiency. The Scaled Sequence Feature Fusion (SSFF) module enhances object detection by effectively capturing inter-scale relationships, improving feature representation, and boosting metrics through optimized multiscale fusion. Comparison with baseline models reveals high efficacy with up to 69.5% fewer parameters compared to vanilla Yolov8-N, highlighting the robustness of the proposed modifications. Through this approach, our paper aims to facilitate the detection of endangered species, animal population analysis, habitat monitoring, biodiversity research, and various other applications that enrich wildlife conservation efforts. DEAL-YOLO employs a two-stage inference paradigm for object detection, refining selected regions to improve localization and confidence. This approach enhances performance, especially for small instances with low objectness scores. © 2025, CC BY.

关键词： Unmanned aerial vehicles (UAV)

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Causal discovery for time series from multiple datasets with latent contexts

arXiv

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arXiv 2023年

作者： Günther, Wiebke Ninad, Urmi Runge, Jakob German Aerospace Center Institute of Data Science Jena07745 Germany Technische Universität Berlin Dept. of Electrical Engineering and Computer Science Berlin10623 Germany

Causal discovery from time series data is a typical problem setting across the sciences. Often, multiple datasets of the same system variables are available, for instance, time series of river runoff from different catchments. The local catchment systems then share certain causal parents, such as time-dependent large-scale weather over all catchments, but differ in other catchment-specific drivers, such as the altitude of the catchment. These drivers can be called temporal and spatial contexts, respectively, and are often partially unobserved. Pooling the datasets and considering the joint causal graph among system, context, and certain auxiliary variables enables us to overcome such latent confounding of system variables. In this work, we present a non-parametric time series causal discovery method, J(oint)-PCMCI+, that efficiently learns such joint causal time series graphs when both observed and latent contexts are present, including time lags. We present asymptotic consistency results and numerical experiments demonstrating the utility and limitations of the method. © 2023, CC BY.

关键词： Catchments

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Enhancing Cloud Resource Allocation Efficiency with Harris Hawk Optimization Algorithm(HHOA)

Enhancing Cloud Resource Allocation Efficiency with Harris H...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： V Manickavasagan Eswari Petchiammal S Rahini Sudha C Rajesh S Jindhiya Sree V S Yamini Dept. of CSBS Sri Sai Ram Engineering College Chennai India Dept. of Information Technology Velammal Institute of Technology Chennai India Dept. of CSBS Panimalar Engineering College Chennai India Dept. of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Information Technology Velammal Institute of Technology Chennai India

The demand for efficient cloud resource allocation has surged with the growing reliance on cloud computing for a wide array of applications. Optimizing resource allocation in cloud environments is crucial for cost-effectiveness, performance, and energy conservation. In this context, bio-inspired optimization algorithms have gained prominence for their ability to address complex allocation problems. This paper investigates the application of the Harris Hawk Optimization algorithm (HHOA) to enhance cloud resource allocation efficiency. HHOA, inspired by the hunting behavior of Harris’s hawks, is an efficient and versatile metaheuristic algorithm known for its ability to find optimal solutions in complex, dynamic environments. By harnessing the HHOA algorithm, this study aims to improve the allocation of resources such as virtual machines and storage within cloud infrastructures as demanded by the tasks. In our proposed approach we present a comprehensive examination of HHOA’s suitability for cloud resource allocation and compares its performance with conventional optimization algorithms like Stimulated Annealing (SA), Genetic Algorithm (GA) and Tabu Search (TS). Through a series of experiments, we found that Harris Hawk Optimization Algorithm (HHOA) is proven to be very effective in improving operational efficiency by enhanced reliability, lowered latency and lowered average response time.

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