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A recurrent neural network architecture for android mobile data analysis for detecting malware infected data

Soft computing 2024年第21期28卷 12917-12928页

作者： Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Garg, Shruti Department of ECE Saveetha School of Engineering Simats Chennai602105 India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Simats Chennai602105 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Applied Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technological Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India

One of the latest modern communication devices is a mobile device seriously affected by multiple malware. Malware is a virus software installed automatically by hackers on various computing devices. Malware corrupts the system software, kills *.exe files, and tries to access user-sensitive data from the device. Around the world, 80% of people use smartphones, and 70% use Android phones. All Android phones are installed with anti-virus software but not used properly. Thus, it creates a pathway for malware to enter Android mobile devices and deploy vulnerabilities. Several earlier methods have focused on using the malware detection method by analyzing static data, which cannot identify the dynamic behavior of the malware. Thus, this paper implemented one of the deep learning algorithms, RNN, for identifying and detecting malware activities in Android mobile devices statically and dynamically. An IoT network architecture is proposed for experimenting with the RNN algorithms verifying the performance. The results of the RNN method show that it is better than other models in malware detection in mobile devices. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Android malware

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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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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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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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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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Computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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INITIAL VALUE PROBLEMS FOR NONLINEAR NEUTRAL DELAY Ψ-CAPUTO FRACTIONAL HYBRID DIFFERENTIAL EQUATIONS WITH BOUNDED DELAYS

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Journal of Mathematical Sciences (United States) 2025年 1-11页

作者： Malghi, Hayat Taqbibt, Abdellah El Omari, M’hamed Qaffou, Abdelaziz Hilal, Khalid Department of Mathematics Applied Mathematics and Scientific Computing Laboratory Faculty of Sciences and Technology Beni Mellal Sultan Moulay Slimane University P.O. Box 523 Beni Mellal 23000 Morocco

In this work, we explore the existence of solutions to an initial value problem for nonlinear neutral delay Ψ-Caputo fractional hybrid differential equations with bounded delays. The existence results are established using fixed point theorems. To demonstrate the practical applicability of our findings, we provide an illustrative example. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fixed point Hybrid differential equation Neutral delay differential equation Ψ-Caputo derivative

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Intelligent Traffic Light Solution for Green and Sustainable Smart City 12

Intelligent Traffic Light Solution for Green and Sustainable...

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12th Mediterranean Conference on Embedded computing, MECO 2023

作者： Jafari, Omid Kolosov, Stanislav Vo, Nhan Magar, Asmita Thapa Heikkonen, Jukka Kanth, Rajeev School of Engineering and Technology Savonia University of Applied Sciences Kuopio70201 Finland University of Turku Department of Computing Turku Finland

ISBN: (纸本)9798350322910

This research aims to develop a smart traffic light system that can improve traffic flow in urban areas. The proposed system uses sensors, cameras, and software to adjust the timing of traffic signals based on real-time traffic conditions. In this study, a Raspberry Pi 4 and MATLAB software was used to build the smart traffic controller. The detection part of the system involves several steps, including removing noise and retrieving information to calculate the number of cars detected. The system then switches traffic lights based on the detected car count. The MATLAB Image Acquisition and Computer Vision toolboxes were used to obtain and analyze the video frames received from the connected cameras. The detector uses the Gaussian Mixture Model to suppress frequently occurring features and to detect abnormal features, which are then used to detect changes caused by moving objects. Morphological operations are used to remove noise from the output. Finally, the system counts the cars detected by the Foreground Detector and switches the traffic lights accordingly. The proposed approach can help reduce traffic congestion and improve the overall flow of traffic in urban areas. © 2023 IEEE.

关键词： MATLAB

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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2024 International Conference on Electrical, Electronics and computing Technologies, ICEECT 2024

作者： Palarimath, Suresh Maran, Pyingkodi Venkateswaran, R. Wilfred Blessing, N.R. Shiny, K.V. Renuga, S. University of Technology and Applied Sciences College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350378092

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence's framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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Empowering Precision Machine: Deep Learning Technique for Lung Cancer Diagnosis 2

Empowering Precision Machine: Deep Learning Technique for Lu...

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

作者： Jamil, Danish Shah, Syed Mehr Ali Khan, Muhammad Numan Ali Aljarwani, Fatma Mahfoodh Mohammed University of Santiago de Compostela Spain Sir Syed University of Engineering & Technology Department of Software Engineering India College of Computing & Information Sciences University of Technology and Applied Sciences Musandam Oman

ISBN: (纸本)9798331542573

This research paper delves into the realm of lung cancer diagnosis, a critical area in modern healthcare owing to its substantial global impact. Leveraging cutting-edge technology and machine learning methodologies, specifically employing the VGG16 model, our study aims to enhance the early detection of lung cancer through the analysis of CT scan images. The dataset utilized comprises CT scan images sourced from the Lung Image Database Consortium (LIDC/IDRI) Image Dataset, categorized into three classes: malignant, benign, and normal. By harnessing the capabilities of the VGG16 model, we seek to accurately predict the classification of CT scan images into these distinct categories. Through rigorous experimentation and analysis, our findings shed light on the efficacy of this approach in aiding medical professionals in the timely identification and classification of lung cancer cases. Ultimately, our research contributes to advancing the field of medical imaging and underscores the potential of deep learning in revolutionizing healthcare diagnostics. © 2024 IEEE.

关键词： Lung cancer

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An Adaptive on Demand Modified Ant Colony Optimization Routing for VANETs 23th

An Adaptive on Demand Modified Ant Colony Optimization Routi...

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23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Kumari, J. Jesy Janet Thangam, S. Raja, A. Saleem Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering University of Technology and Applied Science-Shinas Shinas Oman

ISBN: (纸本)9783031648496

Around the world, vehicle accidents claim many priceless lives. To tackle this problem, the Vehicular ad hoc networks (VANETs) are the most effective solution. Effective communication between vehicular nodes in VANETs is extremely important because of the rapid mobility of vehicles and different network topology. Currently, VANET mostly serves the public security, stability, information for voyageurs, and traffic Management, control of congestion, guidance, etc. Chaos on roadways can occur at any moment, anyplace. It is a situation in which the flow of traffic regulations might cease to function. Collisions, maintaining roads, violation of legal rules, etc., so to evade the congestion in the surrounding region. VANETs represent a dynamic and challenging communication environment characterized by high mobility, intermittent connectivity, and rapid changes in network topology. Efficient and reliable routing is crucial for enabling diverse applications such as traffic management, safety communication, and infotainment services in VANETs. In this research, propose a multi-faceted approach to improve routing performance in VANETs. An adaptive routing algorithm that considers vehicle mobility patterns and dynamically adjusts communication routes based on the anticipated movement of neighboring vehicles such as Modified Ant Colony Optimization (MACO) technique. This adaptability enhances the robustness of the network against frequent topology changes and improves the overall packet delivery ratio, end-to-end delay, and network reliability compared to traditional routing protocols. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Vehicular ad hoc networks

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