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2nd International Conference on computing and Data Analytics, ICCDA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Wilfred Blessing, N.R. Kavitha, S.J. Cibi, A. Sutherlin, Subitha G. College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Pallavaram Chennai India Virtusa Consulting Services Private Limited Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Kanyakumari India

ISBN: (纸本)9798331542573

The growing occurrence of cybersecurity hazards, such as Android zero-day vulnerabilities, poses substantial difficulties because they are unattended and imperceptible. With the increasing popularity of Android smartphones, hackers are taking advantage of these weaknesses to quickly spread advanced malware. Conventional detection approaches frequently prove inadequate due to the absence of shown criteria for emergent threats. In order to address this crucial problem, this article presents the Zero-Vuln method, an advanced technique specifically developed to identify and categorize previously unidentified malware on Android platforms. Zero-Vuln combines sophisticated supervised deep learning methods with zero-shot learning algorithms to accurately detect zero-day threats. Through the utilization of comprehensive datasets, the method attains an average classification metric score of 84.22%, showcasing exceptional accuracy, precision, and recall. This advanced solution significantly improves the ability to identify and handle Android zero-day threats, representing a major progress in cybersecurity research and implementation. The Zero-Vuln strategy provides a strong and adaptable solution for the constantly changing mobile security risks, representing a notable advancement in the sector. © 2024 IEEE.

关键词： Zero-shot learning

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Avian Hazard Mitigation: A Classification Approach to Bird Strike Analysis

Avian Hazard Mitigation: A Classification Approach to Bird S...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Najiba Said Hamed Al-Zadjali Charles Savarimuthu B. Sudaravadivazhagan Emanuel O. Rances College of Computing and Information Sciences University of Technology and Applied Sciences-Muscat Muscat Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences-Al Musanah Musanah Sultanate of Oman College of Engineering and Technology University of Technology and Applied Sciences-Al Musanah Musanah Sultanate of Oman

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Bird strikes during landing or takeoff can result in fatalities and catastrophic accidents, raising concerns about loss of life for the aviation sector. This study explored the potential of employing machine-learning algorithms to mitigate bird strikes in aviation. Bird strike data were collected, and the Boruta algorithm feature-selection technique was applied to identify relevant features for an experiment. Irrelevant features were omitted, and relevant features related to bird strikes were identified for analysis. The multinomial Naive Bayes algorithm is then used to segregate objects based on their features. The experimental results illustrate the power of using machine learning techniques to segregate and analyze bird strike data, thereby aiding in determining the causes and effects of damage.

关键词： Machine learning algorithms Accuracy Prevention and mitigation Machine learning Birds Prediction algorithms Hazards Classification algorithms Trajectory Naive Bayes methods

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Designing a hybrid neural network framework for real-time anomaly detection in cybersecurity applications

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International Journal of Information technology (Singapore) 2025年 1-7页

作者： Jain, Aaditya Ead, Waleed M. Alshahrani, Mohammed Altamimi, Jawaher Suliman Aklan, Fawaz Rihan, Shaza Dawood College of Computing Sciences and Information Technology Teerthanker Mahaveer University Uttar Pradesh Moradabad India Faculty of Computing and Information Al-Baha University Al-Baha Saudi Arabia Faculty of Computers and Artificial Intelligence Beni-Suef University Beni-Suef Egypt Computers Department Applied College Najran University Najran Saudi Arabia Applied College Najran University Najran Saudi Arabia Department of Computer Science Faculty of Applied Science Taiz University Taiz P.O. Box 6803 Taiz Yemen

As cybersecurity is becoming a prominent domain, it is an imperative to provide real-time anomaly detection for early threat detection and to keep networks safe. In this paper, an optimized hybrid neural network-framework is proposed to detect anomalies at several attack vectors with high efficacy. The framework comprises convolutional neural network equipped with long-short term memory (CNN-LSTM) for temporal and spatial representation of data. As the system accommodates new data and keeps on learning, it adjusts itself becoming a significant mean in counteracting against emerging threats with less latency in detection. In order to verify the efficiency and robustness of the proposed framework, a series of real-time simulation is designed using key performance metrics such as detection rate, false alarm rate and processing time. The results show higher improvement in accuracy and speed of anomaly detection, which makes the framework a useful instrument to reinforce cybersecurity defense during real-time scenarios. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Anomaly detection Convolutional neural network (CNN) Cybersecurity Hybrid approach Long-short term memory (LSTM)

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Smart IoT-Based Solution for Emergency Assistance during Sandstorms and Landslides 10

Smart IoT-Based Solution for Emergency Assistance during San...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Regula, Thirupathi Hoqani, Warda Al Cheriyan, Sunitha Reddy, Sharmila K. Kumar, Naveen Tazeen, Shazia Kiran, Kumar S. G. College of Computing and Information Sciences University of Technology and Applied Science Department of It Muscat Oman Vaagdevi Engineering College Department of Cse Telangana India Nit Department of Cse Telangana Warangal India

ISBN: (纸本)9798331532895

Natural disasters, which are caused by changes in the climate and geography, pose a major threat to human life and economic stability. These events, which include cyclones, tsunamis, landslides, and earthquakes, can result in serious ecological damage as well as fatalities. The speed at which climate change is occurring increases the unpredictability of such disasters, especially in vulnerable regions like deserts and forests. This study offers a solution to the challenges of rescuing those who are stranded by such catastrophes, where traditional rescue methods and communication may not be effective. The system uses Internet of Things (IoT) and mobile phone technologies to send location information and real-time alerts through Global Positioning System (GPS) and Geographic Information System (GIS), enabling the timely location of a person or vehicle for effective emergency response. This approach aims to improve disaster preparedness and survival rates in remote, catastrophe-prone areas. © 2024 IEEE.

关键词： Landslides

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Innovating Economic Models for Smart Green Cities Through Artificial Intelligence Techniques

Innovating Economic Models for Smart Green Cities Through Ar...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Nasim Al Balushi College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

The main challenges resulting from urbanization include poverty, environmental degradation, and unsustainable resource extraction. Urbanization is not only one of the most significant human activities but also an inevitable trend in the 21st century. With over 80% of the global population expected to reside in cities by the end of this century, sustainable urbanization will likely play a critical role in reducing consumption and emissions. This paper explores the integration of artificial intelligence (AI) into economic models to aid in the development of smart, green cities. AI technologies, leveraging data analytics, machine learning, and predictive modeling, can optimize resource allocation and inform urban planning decisions to enhance the sustainability of urban environments. AI enables the implementation of smart grids, traffic management systems, and the automation of public sector activities, all of which contribute to reducing energy consumption and minimizing environmental impact. By combining AI with the Internet of Things (IoT) and other modern technologies, cities can achieve proactive urban planning, foster public participation, and improve resource management. This study highlights the potential of AI in driving climate neutrality and promoting sustainable economic growth in future urban landscapes.

关键词： Economics Biological system modeling Urban areas Green products Urban planning Data models Resource management Internet of Things Artificial intelligence Sustainable development

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INVARIANCE ANALYSIS, SYMMETRY REDUCTION AND CONSERVATION LAWS FOR A BIOLOGICAL POPULATION MODEL IN POROUS MEDIA

arXiv

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arXiv 2024年

作者： Joshi, Urvashi Sharma, Aniruddha Kumar Arora, Rajan Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee India

This research paper talks about using complex mathematical tools to study and figure out the behavior of biological populations in porous media. Porous media offer a unique environment where various factors, including fluid flow and nutrient diffusion, significantly influence population dynamics. The theory of Lie symmetries is used to find inherent symmetries in the governing equation of the population model, helping to find conservation laws and invariant solutions. The derivation and analysis of the optimal system provide insights into the most influential parameters affecting population growth and distribution. Furthermore, the study explores the construction of invariant solutions, which aid in characterizing long-term population behavior. The article concludes with the non-linear self-adjointness property and conservation laws for the model. Copyright © 2024, The Authors. All rights reserved.

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Design and Application of Teaching System Using Artificial Intelligence technology

Design and Application of Teaching System Using Artificial I...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Vimal Kumar Stephen Ramesh Palanisamy Anand Muthukumarappan Kumar Kaliyamoorthy College of Computing and Information Sciences University of Technology and Applied Sciences Ibra Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

In recent years, a large number of academic institutions have examined and experimented with teaching system evaluations with varying degrees of success. Techniques and tools for assessing the quality of the teaching system in general higher education institutions are required in order to comprehend the quality of learning and the teaching system in universities and colleges, to enhance school circumstances, and to develop the AI of teaching management in Oman. Important elements of the contemporary education institute and teaching system are lesson planning, implementation, test preparation, in-class audiovisual aid use, classroom instruction and practice, and assessment. Because of this, the success of instruction greatly depends on the design of the system of instruction. This study presents an IFH-DAdaBoost algorithm, which is based on artificial intelligence (AI), as a solution to the current issue of the skills fostered by universities and colleges not always aligning with societal needs regarding creativity, practices in engineering, and international competitiveness. The real-world teaching effect shows that modern students prefer and give better evaluation ratings to AI designs. The experiment results also show the stability, accuracy of the suggested method, which overcomes the design constraints of the traditional teaching system. Thus, by enhancing their training in practical abilities, an AI-driven teaching system would significantly improve students' overall quality and innovative capability.

关键词： Training Technological innovation Ethics Accuracy Education Learning (artificial intelligence) Planning Artificial intelligence System analysis and design Numerical stability

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A Comparison of Cloud Security Mechanisms 2

A Comparison of Cloud Security Mechanisms

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2nd International Conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Saravanan, D. Maragatharajan, M. Nirmala, E. Naveen, P. Francis Saviour Devaraj, A. School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Kothrikalan India Department of Electrical and Electronics Engnieering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of IT College of Computing & Information Sciences University of Technology and Applied Sciences Ibri Oman

ISBN: (纸本)9798350340235

Cloud computing is a broad term that can be understood differently depending on the context. Its primary goal is to reduce capital expenses on infrastructure, operating systems, deployment, and storage. The National Institute of Standards and technology (NIST) defines cloud computing as on-demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. Cloud computing operates using virtualization and parallel computing principles and provides services like IaaS, PaaS, SaaS, and NaaS. The cloud is classified into public, private, hybrid, and community clouds, each with its own unique features. The major issue of cloud computing is to transfer the data with security breaches. Various security systems are available to protect the data security in the cloud. This research study has discussed some security models like Advanced encryption standard algorithm, Biometric model, Elliptic-curve cryptography with Diffie-Hellman algorithm and a new technique vector-matrix based encryption algorithm. The execution time for these algorithms were compared and results were discussed. © 2023 IEEE.

关键词： Advanced encryption standard algorithm Biometric model Cloud security Decryption Diffie-Hellman algorithm Elliptic-curve cryptography Hybrid cloud vector-matrix based encryption algorithm

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Adaptive MOOC - An Approach to Identify Learners' Preferences to Enhance Their Engagement

Adaptive MOOC - An Approach to Identify Learners' Preference...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： Mangesh Wanjari Amal Al Abri Someshvar Vashish College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Massive Open Online Courses have completely revolutionized the delivery of education, affording new levels of access to learners of all walks of life around the world. However, one of the greatest challenges facing educators and the providers of platforms on which these courses are delivered is how to keep learners engaged in the courses and motivate them to complete the courses they are registered into. This paper proposes a novel method of improving learner engagement in a course by determining and appealing to individual preferences in content delivery. The proposed framework draws on learner log data from the LMS (Moodle) to cluster students according to their interactions with different types of course material. Based on the preferred content types for each cluster, we will be able to recommend the most suitable materials to the individual learners and enhance overall engagement and motivation towards active participation in the course. It also includes the results of a questionnaire circulated to these students, in order to cross-check the results of the clustering analysis and provide important insights with respect to the effectiveness of the proposed approach.

关键词： Technological innovation Learning management systems Ethics Electronic learning Adaptive systems Education Artificial intelligence

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