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Offline Content-based Recommendation System for Wikimedia Commons Contents

Procedia computer Science 2025年 257卷 485-494页

作者： Ahidjo Maitaigahasse Jean Louis Kedieng Ebongue Fendji Marcellin Atemkeng Department Mathematics and Computer Science Faculty of Science University of Ngaoundere 454 Ngaoundere Cameroon Centre for Research Experimentation and Production School of Chemical Engineering and Mineral Industries University of Ngaoundere 454 Ngaoundere Cameroon Stellenbosch Institute for Advanced Study (STIAS) Wallenberg Research Centre at Stellenbosch University Stellenbosch South Africa Department of Robotics and Industrial Informatics School of Chemical Engineering and Mineral Industries University of Ngaoundere 454 Ngaoundere Cameroon Department of Mathematics Rhodes University P.O. Box 6140 Grahamstown South Africa

Recommendation systems are essential for enhancing digital experiences, but their reliance on internet connectivity limits accessibility in regions with limited or no access. This paper presents an offline content-based recommendation system designed to operate without internet dependency by leveraging precomputed Term Frequency-Inverse Document Frequency (TF-IDF) vectors and cosine similarity. To optimize offline performance, we store a locally computed TF-IDF matrix, allowing efficient retrieval of relevant media items through matrix multiplication instead of real-time computation. The system is evaluated using twenty simulated personas representing diverse user interests, demonstrating its ability to generate personalized relevant recommendations. By eliminating the need for online data access, our system makes educational content from Wikimedia Commons accessible in remote areas, schools, and offline learning environments. These findings highlight the potential of offline recommendation systems in bridging the digital divide and providing equitable access to personalized learning resources.

关键词： Content-based recommendation system Wikimedia commons TF-IDF Cosine Similarity

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Generic Drone Simulator: Design, Development and User Testing in a Virtual Search and Rescue Mission

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IEEE computer Graphics and Applications 2025年 PP卷 PP页

作者： Pappas, Georgios Lappas, Dimitrios Karampelas, Panagiotis Siegel, Joshua E. Templalexis, Ioannis Stavrou, Stavros Krishnan, Sriram Open University of Cyprus Lab of Educational Material and Methodology Latsia Nicosia Cyprus University of the Aegean Learning Technology and Educational Engineering Laboratory Rhodes Greece Hellenic Air Force Academy Department of Informatics & Computers Dekelia Attiki Greece Michigan State University Department of Computer Science and Engineering East LansingMI United States Hellenic Air Force Academy Department of Thermodynamics Propulsion and Power Systems Dekelia Attiki Greece Open University of Cyprus Cybersecurity and Telecommunications Research Lab Latsia Nicosia Cyprus Indian Institute of Technology The Energy Consortium Madras India

This study addresses the need for scenario-specific drone simulators to enhance research, education, pilot training, and performance evaluation across diverse mission contexts. A gamified simulator was developed for the joint master's degree program "Security and Defence" between the Open University of Cyprus (OUC) and the Hellenic Air Force Academy (HAFA), with a focus on Search and Rescue mission training and planning. The simulator balances interface design, physics accuracy, and environmental realism to support specialized professional training and research. A survey-based evaluation involving 57 participants –pilots, engineers, and air traffic controllers –demonstrated the tool's ability to provide realistic experiences for trained pilots and meaningful learning opportunities for novices. Statistical analysis revealed significant correlations between gaming familiarity, task completion times, and participant expertise. © 1981-2012 IEEE.

关键词： Drones

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Innovative Urban Solutions with IoT-Driven Traffic and Pollution Control

Innovative Urban Solutions with IoT-Driven Traffic and Pollu...

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Automation and Computation (AUTOCOM), International Conference on

作者： Kottala Sri Yogi Avinash Sharma V Dankan Gowda Rini Saxena Tarkeshwar Barua Khalid Mohiuddin Dept of Operations Symbiosis institute of Business Management Hyderabad Symbiosis International University Pune Hyderabad Telangana Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Business Informatics College of Business King Khalid University PO Box-3247 Abha Saudi Arabia

The Internet of Things (IoT) is rethinking the model for building the smart cities, and this paper makes just such a step in that direction. It then examines various ways of cutting urban pollution. Under increasing pressure from rising urbanization, smart cities represent a huge leap forward in regard to efficiency, scalability and livability. The key is IoTtechnology, which collects and analyzes data in real time and allows for more quick-witted and intelligent urban governance. This paper combines literature review, case study analysis and data-driven methods to focus on the opportunities offered by IoT applications for restructuring urban traffic systems and pollution control. This study explains what is missing in modern smart city initiatives, and offers a road map for bringing Internet of things solutions to municipal systems. The results can also serve as some answers to cope with the dilemma and bring more intelligence, cleanness, efficiency in making a city into a smart metropolis. The article ends by highlighting the significance of IoT for future urban development, and ideas for further research into this complex yet rapidly evolving area.

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Air Quality Improvement and Optimisation Using Machine Learning Technique

Air Quality Improvement and Optimisation Using Machine Learn...

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Advances in Computing, Communication and Applied informatics (ACCAI), International Conference on

作者： Rayavarapu Veeranjaneyulu Sampath Boopathi R. Krishna Kumari Anurag Vidyarthi J. Samson Isaac V Jaiganesh Department of Computer Science Engineering PACE Institute of Technology & Sciences Ongole Andhra Pradesh India Department of Mechanical Engineering Muthayammal Engineering College Namakkal Tamil Nadu India Department of Career Development Centre College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electronics & Communication Engineering Graphic Era Deemed to be University Dehradun Uttarakhand Department of Biomedical Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Department of Mechanical Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu India

Due to the increased use of automobiles, the manufacturing industry, and the emission of pollutants from other human activities, air pollution has risen above the expected safety level. Accurate estimating of the air quality index(AQI) is essential for effective pollution control. In this research, an AQI prediction ANFIS network model was created utilizing an already-existing data set. In this instance, the ANFIS system compares the performances of the back propagation neural network model, hybrid models, the Gaussian-BNN model, and the Gaussian-hybrid BNN model. Based on the actual raw data set, it was noted that the R and IA values of the Gaussian hybrid model are 0.9899. The ANFIS gauss-hybrid model might therefore be used to predict the most accurate model data.

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Hybrid Approach to Identify Druglikeness Leading Compounds against COVID-19 3CL Protease

arXiv

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arXiv 2022年

作者： Aqeel, Imra Majid, Abdul Biomedical Informatics Research Lab Department of Computer & Information Sciences Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan Pakistan Institute of Engineering & Applied Sciences Nilore Islamabad45650 Pakistan

SARS-COV-2 is a positive single-strand RNA-based macromolecule that has caused the death of more than 6.3 million people since June 2022. Moreover, by disturbing global supply chains through lockdown, the virus has indirectly caused devastating damage to the global economy. It is vital to design and develop drugs for this virus and its various variants. In this paper, we developed an in-silico study-based hybrid framework to repurpose existing therapeutic agents in finding drug-like bioactive molecules that would cure Covid-19. We employed the Lipinski rules on the retrieved molecules from the ChEMBL database and found 133 drug-likeness bioactive molecules against SARS coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into three classes active, inactive, and intermediate. Our comparative analysis demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has improved prediction results related to the bioactivity of chemical compounds as compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and Random Forest based regressor models. ADMET analysis is carried out to identify thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628, 222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898. These molecules are highly suitable drug candidates for SARS-COV-2 3CL Protease. In the next step, the efficacy of bioactive molecules is computed in terms of binding affinity using molecular docking and then shortlisted six bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and 365134. These molecules can be suitable drug candidates for SARS-COV-2. It is anticipated that the pharmacologist/drug manufacturer would further investigate these six molecules to find suitable drug candidates for SARS-COV-2. They can adopt these promising compounds for their downstream drug development stages. © 2022, CC BY.

关键词： SARS

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A Critical Analysis of Deployed Use Cases for Quantum Key Distribution and Comparison with Post-Quantum Cryptography

arXiv

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arXiv 2025年

作者： Aquina, Nick Cimoli, Bruno Das, Soumya Hövelmanns, Kathrin Weber, Fiona Johanna Okonkwo, Chigo Rommel, Simon Škorić, Boris Monroy, Idelfonso Tafur Verschoor, Sebastian Department of Electrical Engineering Eindhoven University of Technology Flux Building Eindhoven5612 AZ Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology MetaForum Building Eindhoven5612 AZ Netherlands Eindhoven Quantum Hub Eindhoven University of Technology Qubit Building Eindhoven5612 AZ Netherlands Informatics Institute University of Amsterdam Science Park 904 Amsterdam1098 XH Netherlands

Quantum Key Distribution (QKD) is currently being discussed as a technology to safeguard communication in a future where quantum computers compromise traditional public-key cryptosystems. In this paper, we conduct a comprehensive security evaluation of QKD-based solutions, focusing on real-world use cases sourced from academic literature and industry reports. We analyze these use cases, assess their security and identify the possible advantages of deploying QKD-based solutions. We further compare QKD-based solutions with Post-Quantum Cryptography (PQC), the alternative approach to achieving security when quantum computers compromise traditional public-key cryptosystems, evaluating their respective suitability for each scenario. Based on this comparative analysis, we critically discuss and comment on which use cases QKD is suited for, considering factors such as implementation complexity, scalability, and long-term security. Our findings contribute to a better understanding of the role QKD could play in future cryptographic infrastructures and offer guidance to decision-makers considering the deployment of QKD. © 2025, CC BY-SA.

关键词： Qubits

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Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

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Multimedia tools and applications 2022年第20期81卷 28799-28800页

作者： Amir Masoud Rahmani Elham Azhir Morteza Naserbakht Mokhtar Mohammadi Adil Hussein Mohammed Aldalwie Mohammed Kamal Majeed Sarkhel H Taher Karim Mehdi Hosseinzadeh Future Technology Research Center National Yunlin University of Science and Technology Douliu Yunlin Taiwan. Research and Development Center Mobile Telecommunication Company of Iran Tehran Iran. Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran. Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Iraq. Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq. Information Technology Department Faculty of Applied Science Tishk International University Erbil Iraq. Computer Department College of Science University of Halabja Halabja Iraq. Computer Networks Department Sulaimani Polytechnic University Technical College of Informatics Sulaymaniyah Iraq. Computer Science University of Human Development Sulaymaniyah Iraq.

[This corrects the article DOI: 10.1007/s11042-022-12952-7.].

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Location Functions for Self-stabilizing Byzantine Tolerant Swarms 1

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23rd International Symposium on Stabilization, Safety, and Security of Distributed Systems, SSS 2021

作者： Ashkenazi, Yotam Dolev, Shlomi Kamei, Sayaka Katayama, Yoshiaki Ooshita, Fukuhito Wada, Koichi Department of Computer Science Ben-Gurion University of the Negev Beersheba Israel Graduate School of Advanced Science and Engineering Hiroshima University Higashihiroshima Japan Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan Department of Applied Informatics Faculty of Science and Engineering Hosei University Koganei Japan

ISBN: (数字)9783030910815

ISBN: (纸本)9783030910808

This paper proposes a novel framework to realize self-stabilizing Byzantine tolerant swarms. In this framework, non-Byzantine robots execute tasks while satisfying location functions, that is, the robots use a policy for their location choice, which restricts their location to satisfy the functions. We give a general Byzantine-resilient self-stabilizing algorithm based on the location function, and then provide an efficient implementation of the self-stabilizing algorithms for special classes of tasks, called polynomial-based tasks and shape-based tasks. We also demonstrate the usefulness of the proposed framework by implementing typical tasks of robots. © 2021, Springer Nature Switzerland AG.

关键词： Location

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A Detailed Investigation of Implementation of Internet of Things (IOT) in Cyber Security in Healthcare Sector 2

A Detailed Investigation of Implementation of Internet of Th...

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

作者： Ganai, Prince Tehseen Bag, Akash Sable, Anita Abdullah, Khairul Hafezad Bhatia, Sandeep Pant, Bhasker Shri Jagdishprasad Jhabarmal Tibrewala University Department of Information Technology Rajasthan India Amity School of Law Amity University Chhattisgarh Raipur India University Maharashtra Pune India Universiti Teknologi MARA Perlis Branch Science Laboratory Unit Department of Academic Affairs Arau Campus Malaysia Electronics and Communication Engineering Raj Kumar Goel Institute of Technology U.P. Ghaziabad India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

The use of 'electronic healthcare technology' is widespread, and it has huge potential to improve 'clinical outcomes and transform' how therapy is administered. On the other hand, medical device and information security is becoming a significant problem. Security breaches were, obviously, an issue even before the advent of digital clinical information. The interdependency of today's documentation, on the other hand, offers various prospective entry points: the ability to obtain from anywhere (whereas earlier, hard copies would have been kept in healthcare institutions and only available through personal breaches);the capacity for security breaches to go unobserved;and access towards a more comprehensive medical record, offering a more precious asset for possible threats (whereas traditionally, medical files may have been divided between many various providers). Previously, a data leak might have affected hundreds or even thousands of people due to missing paper documents or a stolen computer;but, now that this content is digitised and accessible across several networks, a cyber-attack has the capability to harm millions of patients. To demonstrate the concept, celebrity healthcare information has long been a target for cybercriminals. Prior to the introduction of electronic medical records, meanwhile, these vulnerabilities were restricted to hospital workers who had direct access to the documentation. Celebrity health information may now be viewed remotely, thus raising the risk of a data leak. For mitigating this issue, IoT or Internet of Things is widely used in healthcare industry to increase service security. This research has considered secondary qualitative method to gather wide range of information related to the study. © 2022 IEEE.

关键词： Cybersecurity

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Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis

arXiv

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arXiv 2023年

作者： Bera, Asish Nasipuri, Mita Krejcar, Ondrej Bhattacharjee, Debotosh The Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Campus Rajasthan Pilani333031 India The Department of Computer Science and Engineering Jadavpur University Kolkata32 India The Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

Human body-pose estimation is a complex problem in computer vision. Recent research interests have been widened specifically on the Sports, Yoga, and Dance (SYD) postures for maintaining health conditions. The SYD pose categories are regarded as a fine-grained image classification task due to the complex movement of body parts. Deep Convolutional Neural Networks (CNNs) have attained significantly improved performance in solving various human body-pose estimation problems. Though decent progress has been achieved in yoga postures recognition using deep learning techniques, fine-grained sports, and dance recognition necessitates ample research attention. However, no benchmark public image dataset with sufficient inter-class and intra-class variations is available yet to address sports and dance postures classification. To solve this limitation, we have proposed two image datasets, one for 102 sport categories and another for 12 dance styles. Two public datasets, Yoga-82 which contains 82 classes and Yoga-107 represents 107 classes are collected for yoga postures. These four SYD datasets are experimented with the proposed deep model, SYD-Net, which integrates a patch-based attention (PbA) mechanism on top of standard backbone CNNs. The PbA module leverages the self-attention mechanism that learns contextual information from a set of uniform and multi-scale patches and emphasizes discriminative features to understand the semantic correlation among patches. Moreover, random erasing data augmentation is applied to improve performance. The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs. SYD-Net’s accuracy on other datasets is remarkable, implying its efficiency. Our Sports-102 and Dance-12 datasets are publicly available at https://***/view/syd-net/home. © 2023, CC BY.

关键词： Sports

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