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International Conference on Emerging Technologies, ICET

作者： Hassan Bin Khalid Abu Bakar Siddique Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Hidden Markov Models have proved to be a very significant tool for various time-series related problems, especially where context is important. One such problem is Part-of-speech tagging. The work uses a customized HMM to propose an effective and advanced solution to POS tagging. With a precision rate of 0.9657, recall of 0.9656, and F1-score of 0.9655, this proposed HMM-based model achieves an exceptional level of accuracy, exhibiting its accurate identification of the POS of words in a sentence. The statistical model employed by the HMM-based method predicts the most likely POS tags while taking into account the probabilities of transition between various POS tags. The model's dependability and resilience were demonstrated when it was tested on a different dataset after being trained on a extensive collection of text data. The study's findings demonstrate that the HMM-based strategy outperforms current POS tagging techniques, making it a significant contribution to the field of natural language processing. In addition, this research has significant implications for a number of NLP applications, including sentiment analysis, machine translation, and text categorization, paving the way for additional innovation and exploration in this domain.

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Diving into Brain Complexity: Exploring Functional and Effective Connectivity Networks

Diving into Brain Complexity: Exploring Functional and Effec...

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International Conference on Emerging Technologies, ICET

作者： Abu Bakar Siddique Hassan Bin Khalid Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The human brain's intricate functions are under-pinned by a vast network of synapses that enable chemical impulses between neurons. Neuroscientists employ two key approaches, functional and effective connectivity, to understand the brain's complexity, focusing on its operations, cognition, and behavior. While both methods utilize graph theory for network analysis, functional connectivity, which investigates associated brain activity, has seen more substantial research efforts than effective connectivity, which examines information processing's inter-regional impacts. In this research paper, we aim to present an extensive examination of the emerging discipline that combines graph theory with the analysis of brain characteristics, shedding light on how these characteristics arise from the interactions among different groups of neurons. Our primary focus lies in exploring the diverse cognitive and neurological applications that leverage functional Magnetic Resonance Imaging (fMRI) as a tool. Additionally, we offer a comprehensive overview of the methods employed to construct brain networks based on functional and efficient connections. Throughout the discussion, we emphasize the advantages and limitations associated with these approaches.

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Revolutionizing Campus Exploration with GikiLenS: A Deep Learning-Powered Object Detection App

Revolutionizing Campus Exploration with GikiLenS: A Deep Lea...

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International Conference on Emerging Technologies, ICET

作者： Iqra Mueed Usama Arshad Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

For newcomers and tourists, navigating university campuses can be difficult, resulting in aggravation and lost time. We respond by introducing “GikiLenS”, an object identification application driven by deep learning that revolutionizes campus exploration by accurately identifying buildings and landmarks while enhancing user experience. GikiLenS is a comprehensive and user-friendly smartphone application that precisely caters to the demands of newcomers and visitors, unlike previous studies in campus navigation. Our study intends to close this gap and develop a reliable method for identifying campus buildings, optimizing navigation, and enhancing user experience. Through rigorous testing, GikiLenS has proven to have remarkable real-time building detection accuracy, highlighting its potential as an important tool for campus exploration. The app's outcomes and conclusions demonstrate how well it works to give users specific building information, promoting a more knowledgeable and enjoyable campus experience. The importance of our findings lies in the development of a unique, user-centered app that offers a cutting-edge method of campus mobility. Our contribution consists of a game-changing method for building detection that improves campus exploration and user pleasure.

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Enhanced Healthcare Through Metaverse Integration: Explainable AI and Blockchain for Trust, Security, and Immersive Experiences

Enhanced Healthcare Through Metaverse Integration: Explainab...

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

作者： Bilal Ibrahim Sowan Hazem Qattous Mohammad Fasha Ahmad Altarawneh Dept. of Bus. Intell. & Data Analytics University of Petra Amman Jordan Dept. of Software Engineering Princess Sumaya University for Technology Amman Jordan Dept. of Computer Science Brunel University of London London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The metaverse, a dynamic blend of virtual and augmented reality, holds extraordinary promise for reshaping the healthcare landscape. By integrating advanced technologies such as virtual reality (VR), artificial intelligence (AI), blockchain, and digital twins, the metaverse introduces transformative opportunities in patient care, medical education, and healthcare operations. These tools foster immersive, personalized, and data-driven environments, significantly improving diagnostic precision, therapeutic strategies, and training methodologies. This paper provides a framework to utilize this potential while navigating key concerns, including systems scale, user customization, and ethical adequacy. In that case, the framework's goal should be eliminating existing limitations and barriers within healthcare systems by utilizing state-of-the-art technologies with real-world applications. The framework involves virtual consultation sites that enable provider-patient interactions, intelligent virtual agents with specific care recommendations, blockchain integration for data integrity, and dynamic VR segments that are functional in delivering healthcare education and training. The study focuses on scenario-based modeling and the use of pilot deployment in various healthcare settings to test for practicability and feasibility. The research also emphasizes that ethical issues and inclusion during the design and implementation of the innovations should also consider society's values regarding the medical field and promote equal access to healthcare. This framework proves a comprehensive blueprint for continuous progress and places the metaverse as one cornerstone in the global healthcare system.

关键词： Training Ethics Technological innovation Solid modeling Metaverse Medical services Blockchains Digital twins Security Medical diagnostic imaging

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A systematic literature review on generating higher-order mutant 8

A systematic literature review on generating higher-order mu...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Amare, Melashu Mohapatra, Sudhir Kumar Panigrahy, Tarini Prasad Addis Ababa Science Technology University Dept. of Software Engineering Addis Ababa Ethiopia Gandhi Institute for Technological Advancement Dept. of Computer Sc. and Engineering Bhubaneswar India

ISBN: (纸本)9789380544434

Mutation testing is a fault-based testing technique that is used to evaluate the quality of a given set of test cases by inserting a single change into the program under test and then checking whether tests are good enough to detect the artificial injected defects or not. The objective of mutation testing is not testing the actual program rather it is focusing on measuring the effectiveness of test cases by using muted code to check whether the test suite is capable to identify the real fault. This paper summarizes and analyzes the current knowledge about higher-order mutants through a systematic literature review. This Systematic Literature Review analyzes the results of more than one hundred articles in the area of generating higher order mutant. The article presents qualitative results for the surveyed articles. In addition, it augments these results with scientific findings and quantitative results from the primary literature. As a result of this work, this SLR presents an outline for many future works. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Testing

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Blockchain Revolution: Empowering the Electric Vehicle Industry through Integration and Case Study Analysis

Blockchain Revolution: Empowering the Electric Vehicle Indus...

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IEEE Conference on Vehicular Technology (VTC)

作者： Ajmery Sultana Md Moniruzzaman Lian Zhao Dept. of Computer Science Algoma University Brampton Canada Dept. of Software Engineering Lakehead University Thunder Bay Canada Dept. of Electrical Computer and Biomedical Eng. Toronto Metropolitan University Toronto Canada

In the rapidly evolving landscape of electric vehicles (EVs), blockchain technology emerges as a transformative force, empowering the industry with enhanced security, transparency, and efficiency in transactions and energy management. However, the successful integration of blockchain technology in the context of EV is still in its infancy. Thus, this work aims to provide a comprehensive study of this young field from a broader perspective. We first discuss the integration process of blockchain technology in the EV domain. Then we explore the potential applications of blockchain technology in enhancing the efficiency, reliability, and sustainability of EV infrastructure and discuss some of the promising research in each category. Finally, we provide a typical application scenario and its specific embodiment in the EV infrastructure and strive to shed light on all-inclusive future research directions, which may facilitate the integration of blockchain technology in the EV ecosystem from theory to practice.

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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Optimizing Spot Instance Reliability and Security Using Cloud-Native Data and Tools

arXiv

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arXiv 2025年

作者： Saqib, Muhammad Malhotra, Shubham Mehta, Dipkumar Jangid, Jagdish Yashu, Fnu Dixit, Sachin Texas Tech University Dept. of Computer Science United States Rochester Institute of Technology Dept. of Software Engineering United States C.K.Pithawalla College of Eng. and Tech India Infinera Corp United States Stony Brook University Dept. of Computer Science United States Stripe Inc United States

This paper presents "Cloudlab," a comprehensive, cloud-native laboratory designed to support network security research and training. Built on Google Cloud and adhering to GitOps methodologies, Cloudlab facilitates the creation, testing, and deployment of secure, containerized workloads using Kubernetes and serverless architectures. The lab integrates tools like Palo Alto Networks firewalls, Bridgecrew for "Security as Code," and automated GitHub workflows to establish a robust Continuous Integration/Continuous Machine Learning pipeline. By providing an adaptive and scalable environment, Cloudlab supports advanced security concepts such as role-based access control, Policy as Code, and container security. This initiative enables data scientists and engineers to explore cutting-edge practices in a dynamic cloud-native ecosystem, fostering innovation and improving operational resilience in modern IT infrastructures. Copyright © 2025, The Authors. All rights reserved.

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Prediction of Continuous Targets by Explainable Imbalanced Regression from Omics Data in Childhood Obesity 1

Prediction of Continuous Targets by Explainable Imbalanced R...

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1st Workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

作者： Arteaga, María Torres-Martos, Álvaro Anguita-Ruiz, Augusto Alcalá-Fdez, Jesús Alcalá, Rafael Gacto, María José Dept. of Computer Science and Artificial Intelligence Univ. of Granada Granada Spain Center of Biomedical Research Univ. of Granada Inst. de investigación Biosanitaria ibs.GRANADA Granada Spain Inst. de Salud Carlos III Madrid Spain Barcelona Spain Univ. of Granada Granada Spain Dept. of Software Engineering Univ. of Granada Granada Spain

Childhood obesity is a persistent challenge for society since it is highly related to insulin resistance and a wide range of other chronic diseases, which impair not only the health of the people, but also the health system itself due to extra costs of treating them. In this context, it is imperative to consider innovative approaches, like eXplainable Artificial Intelligence, to understand the factors underlying childhood obesity. Data imbalance has been thoroughly studied in classification, but scarcely studied in regression since classes do not exist. However, extreme values in continuous domains, which are usually minority are often the most clinically relevant. Facing the imbalance in regression while obtaining explainable models has never been studied. In this application work, we adopt a Machine Learning approach to obtain explainable regression models for imbalanced continuous targets in childhood obesity, as HOMA-IR and Waist Circumference. We consider 79 variables, including targeted metabolomics, targeted proteomics, exposomic data (e.g., the physical activity subdomain), hematological parameters and anthropometry of Spanish children from 3 to 18 years with significant imbalance ratios around 12% in HOMA-IR and Waist Circumference. Even though we mainly focus on extensions of linguistic fuzzy rule-based systems, particularly designed for explainability, we also consider highly accurate complementary approaches as Random Forest that could additionally provide contrast interesting information by post-hoc SHAP. The models so obtained are better considering the relevant minority information (17% improvements in F1). Moreover, they seem to properly explain biologically meaningful relations, as in the case of physical activity data or the one with the follicle stimulating hormone, among others. © 2024 Copyright for this paper by its authors.

关键词： Nutrition

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A Fully Memristive Spiking Neural Network with Unsupervised Learning

arXiv

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arXiv 2022年

作者： Zhou, Peng Choi, Dong-Uk Eshraghian, Jason K. Kang, Sung-Mo The Department of Electrical and Computer Engineering UC Santa CruzCA United States The Dept. of Electrical Engineering and Computer Science University of Michigan MI United States The Dept. of Computer Science and Software Engineering University of Western Australia Australia

We present a fully memristive spiking neural network (MSNN) consisting of physically-realizable memristive neurons and memristive synapses to implement an unsupervised Spike Timing Dependent Plasticity (STDP) learning rule. The system is fully memristive in that both neuronal and synaptic dynamics can be realized by using memristors. The neuron is implemented using the SPICE-level memristive integrate- and-fire (MIF) model, which consists of a minimal number of circuit elements necessary to achieve distinct depolarization, hyperpolarization, and repolarization voltage waveforms. The proposed MSNN uniquely implements STDP learning by using cumulative weight changes in memristive synapses from the voltage waveform changes across the synapses, which arise from the presynaptic and postsynaptic spiking voltage signals during the training process. Two types of MSNN architectures are investigated: 1) a biologically plausible memory retrieval system, and 2) a multi-class classification system. Our circuit simulation results verify the MSNN’s unsupervised learning efficacy by replicating biological memory retrieval mechanisms, and achieving 97.5% accuracy in a 4-pattern recognition problem in a large scale discriminative MSNN. © 2022, CC BY.

关键词： Neural networks

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