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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Tripathi, Amrita Singh, Tripty Sulochana, Balla Charisma Pragada, Bhavya Sri Kiran, Boya Chaitanya Reddy, Gaddam Anvith Duraswamy, Prakash Shivakumar, Swamy S Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India University of Wisconsin-Green Bay Wisconsin USA Head Radiation Oncology HCG Cancer Hospital Bangalore

ISBN: (纸本)9798350381689

This study concerns the use of complex imaging methods like integrating ResNet, Generative Adversarial Networks (GAN), and U- Net with a single set of variables. Improving medical image analysis for lung-related diseases is the primary objective. This study performs in- depth analysis of lung CT data, aiming at improving UNet parameters, GANs and ResNet for better segmentation experience effects on 3D volume rendering images classification. The major effort here is tuning the parameters of the U-Net architecture, training GANs for generative tasks and applying ResNet to improve feature extraction and classification. To provide more detailed understanding of structures in the lung, advanced 3D visualization techniques are used to give greater insights into abnormalities. With the use of U-Net, GAN and ResNet together can classify lung CT scans. This provides a way to distinguish between general patterns and specific problems. The main goal is to fuse these solid building blocks in order to process medical pictures for better results, that will increase the effectiveness and accuracy of diagnosing diseases related with lungs. © 2024 IEEE.

关键词： Generative adversarial networks

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RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection 39

RaCMC: Residual-Aware Compensation Network with Multi-Granul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Xinquan Sheng, Ziqi Lu, Wei Luo, Xiangyang Zhou, Jiantao School of Computer Science and Engineering Ministry of Education Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou510006 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450002 China State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China

ISBN: (纸本)157735897X

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Modal analysis

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Internet of Things Enabled Gas Leakage Detection Over Industrial Areas using Powerful MQ Series Sensor and Controller 7

Internet of Things Enabled Gas Leakage Detection Over Indust...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jalinder Jadhav, Rahul Radhakrishnan, P. Arun Jadhav, Dattatraya Ashreetha, B. Divya, J. Mukherjee, Saptarshi Yashwantrao Mohite Institute of Management Karad India Tagore Engineering College Electronics & Communication Engineering India Keystone School of Engineering Electronic And Telecommunication Department Pune India Mohan Babu University School of Engineering Department of Electronics And Communication Engineering Andhra Pradesh Tirupati India R.M.K.Enginnering College Dept of Computer Science And Design India GIAR Goldsmith Institute of Advanced Research Department of Mtech Bioinformatics West Bengal Kolkata India

ISBN: (纸本)9798350359299

This work presents a comprehensive overview of an Internet of Things (IoT)-enabled gas leakage detection system deployed in industrial areas using powerful MQ series gas sensors and controllers. The primary objective is to enhance safety measures by providing real-time monitoring and rapid response capabilities to potential gas leaks. The system incorporates MQ2, MQ7, and MQ135 gas sensors strategically placed in different zones within the industrial facility, capable of detecting a wide range of gases. These sensors continuously collect data, which is then transmitted to a centralized controller for analysis and decision-making. The system's architecture allows for remote accessibility, enabling authorized personnel to monitor sensor readings and take timely actions from anywhere with an internet connection. The MQ series gas sensors provide accurate and dynamic measurements of gas concentrations, including flammable hydrocarbons, toxic substances, and other hazardous gases. The system's integration with existing safety systems ensures a holistic approach to hazard prevention, allowing for coordinated responses with alarms, emergency shutdown systems, and ventilation controls. Key features of the system include scalability, cost-efficiency, and seamless integration with industrial processes. The scalability allows for the expansion of sensor networks to adapt to evolving industrial environments. The cost-efficiency is realized through the prevention of potential damage and operational downtime. Furthermore, the system integrates seamlessly with existing safety and automation systems, creating a unified safety infrastructure. © 2024 IEEE.

关键词： Internet of things

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Detecting and Characterising Mobile App Metamorphosis in Google Play Store

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IEEE Transactions on Mobile computing 2025年

作者： Denipitiyage, D. Silva, B. Gunathilaka, K. Seneviratne, S. Mahanti, A. Seneviratne, A. Chawla, S. University of Sydney School of Computer science Australia University of Moratuwa Department of Computer Science and Engineering Sri Lanka Sydney Australia Qatar Computing Research Institute Qatar

App markets have evolved into highly competitive and dynamic environments for developers. While the traditional app life cycle involves incremental updates for feature enhancements and issue resolution, some apps deviate from this norm by undergoing significant transformations in their use cases or market positioning. We define this previously unstudied phenomenon as 'app metamorphosis.' In this paper, we propose a novel and efficient multi-modal search methodology to identify apps undergoing metamorphosis and apply it to analyse two snapshots of the Google Play Store taken five years apart. Our methodology uncovers various metamorphosis scenarios, including re-births, re-branding, re-purposing, and others, enabling comprehensive characterisation. Although these transformations may register as successful for app developers based on our defined success score metric (e.g., re-branded apps performing approximately 11.3% better than an average top app), we shed light on the concealed security and privacy risks that lurk within, potentially impacting even tech-savvy end-users. © 2002-2012 IEEE.

关键词： Mobile commerce

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Enhancing Android Security: Static Analysis for Robust Protection and Resilient Defences using Deep Learning 7

Enhancing Android Security: Static Analysis for Robust Prote...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Abhishek, S. Anjali, T. Prakash, Prathiba Nair, Arun Karunakaran Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India University of Greenwich Old Navel College Department of Computer Science LondonSE10 9LS United Kingdom

ISBN: (纸本)9798350341485

Android has been a popular target for criminal actions due to its widespread use as a prominent mobile operating system, requiring the creation of sufficient security measures. Android has grown exposed to illegal activities due to its open-source design and ability to install third-party apps without centralized oversight. The suggested study thoroughly examines the rights that programs ask for to see whether there are any noticeable differences between the permissions used by malware samples and their benign equivalents. Based on complex authorization patterns, various machine learning methods are applied to distinguish between the two groups effectively. The discoveries spur the creation of solid security frameworks, eventually reducing the dangers of potential malware attacks. © 2023 IEEE.

关键词： Mobile security

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Yoga in digital Age: A Hybrid Approach using ML and computer vision 11

Yoga in digital Age: A Hybrid Approach using ML and Computer...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Achaiah, K.K. Pramod, Arjun S. Tesfahun, Abebe Department of Computer Science Amrita School Of Computing Amrita Vishwa Vidyapeetham Mysuru campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

—In today’s fast-paced world, stress and pressure affect people of all ages, both physically and mentally. Yoga has emerged as a highly sought-after solution due to its numerous benefits. In the present technology-dependent era of digital age, it’s only natural that we turn to technology for assistance in their yoga journey. This is where the proposed article comes into play, serving as one’s digital Yoga Guru. In this paper, seven classifiers, out of which six are Machine Learning classifiers namely Support Vector Machine, Naïve Bayes, Random Forest, K-Nearest Neighbor, Logistic Regression, and Ensembler are employed to classify the dataset. These classifiers exhibit a promising average accuracy exceeding 90%. Additionally, the proposed method incorporates a Deep Learning model, specifically the Multi-Perceptron Neural Network, which has been trained to predict outputs with an impressive accuracy of 92%. This Paper utilizes the power of MediaPipe, a cutting-edge technology that plays a pivotal role in this innovative approach to yoga. MediaPipe is capable of identifying individuals on the screen and provides 33 key-points, including critical markers like facial key-points, and main keypoints of the body such as shoulders, elbows, knees, feet, wrists, and etc. These key points are essential for pose recognition, and assessing the correctness of yoga postures. What sets the proposed method apart is its real-time, live tracing feature, boasting an impressive accuracy rate of 99%. As one engages in yoga practice, this technology tracks their movements, and compares them to the ideal yoga postures. This immediate feedback ensures that one performs each pose correctly, promoting better results and minimizing the risk of injury. In essence, the proposed method bridges the gap between ancient yoga wisdom and modern technology, offering a digital Yoga Guru that guides one to achieve physical and mental well-being with precision and confidence. ©2023 IEEE.

关键词： Decision trees

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DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING FORENSHIELD 11

DETECTION OF AUTHENTICITY – OF CONTENT FOR FORENSICS USING ...

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11th IEEE International Conference on Cloud computing in Emerging Markets, CCEM 2023

作者： Govindarajan, Priya Surendran, Aswin P. Kumar, Sanjeev S. Tesfahun, Abebe Department of Computer Science Amrita School of Computing Amrita Vishwa Vidyapeetham Mysuru Campus India School of Electrical & Computer Engineering Scientific Director Debre Markos Institute of Technology Debre Markos University Ethiopia

ISBN: (纸本)9798350360059

The rampant spread of deepfake videos, a significant threat to media integrity, occurs widely on social media and news platforms. Detecting Deepfakes is a formidable challenge. The proposed study involves a novel algorithm to detect the deepfake. The model comprises of various pre-existing classifiers and a novel algorithm named ForenShield – which detects the deepfakes with an accuracy of 97%. The proposed novel technique works on voting strategy and it outperforms all the pre-existing classifiers, exhibiting the models excellence through the same. ©2023 IEEE.

关键词： Classifiers Deepfake ForenShield Voting Strategy

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Recommendation Systems in Education : A Bibliometric Analysis 9

Recommendation Systems in Education : A Bibliometric Analysi...

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9th International Conference on Business and Industrial Research, ICBIR 2024

作者： Mayatopani, Hendra Murad, Dina Fitria Yahaya, Nor Adnan School of Computing University Malaysia of Computer Science and Engineering Selangor Malaysia Pradita University Information System Tangerang Indonesia Binus Online Bina Nusantara University Information Systems Department Jakarta Indonesia

ISBN: (纸本)9798350383010

In today's digital age, recommendation systems have become an important tool in many sectors, including education. This study presents a comprehensive bibliometric analysis of the literature related to recommendation systems in an educational context. Using major scientific databases and bibliometric analysis tools, we evaluate publications from the last 5 years to identify research trends, collaboration patterns, and methodological developments. Our results show a significant increase in the number of publications and interest in this field, with a wide variety of research themes ranging from personalized learning to optimization of educational resources. The most dominant analytics techniques include collaborative filtering and content-based filtering, while hybrid and deep learning approaches show growing potential. In addition, we identified geographic and institutional concentrations of research, as well as a set of challenges such as data privacy and the need for adaptation to learner dynamics. This research underscores the importance of recommendation systems in improving tailored educational experiences and supporting data-driven academic decisions. We also offer insights for future research directions, including the need for interdisciplinary research and closer international collaboration. © 2024 IEEE.

关键词： Collaborative filtering

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Empowering Oral Healthcare: CNN-Assisted Diagnosis for Improved Patient Outcomes 2

Empowering Oral Healthcare: CNN-Assisted Diagnosis for Impro...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Garrepalli, Vaishnavi Gupta, Vishwanath Abhishek, S. Anjali, T. Amrita School Of Computing Amrita Vishwa Vidyapeetham Department Of Computer Science And Engineering Amritapuri India Verizon Data Services India Pvt Ltd Hyderabad India Rolls-Royce Inc Bangalore India

ISBN: (纸本)9798350381665

In today's world, health is very important, and among the many aspects of well-being, oral wellness is one of the crucial components. Diagnostic investigation in everyday clinical procedures primarily consists of reviewing periapical radiography, which visualizes dental anatomy such as teeth, bone, and soft tissues within the oral cavity. Nonetheless, subjective errors may arise during human interpretation of dental radiographs which also take a lot of time. Most of these dental X-rays have an important role in diagnosing various conditions like Implant, Fillings, Cavity, and Impacted teeth under accurate assessment of classification of dental radiographs. These imaging techniques can help dentists detect anomalies within the mouth through this diagnosis process. To improve the accuracy of diagnosis and reduce the costs of clinical operations, this study presents a novel approach that automates the detection of problematic teeth and other dental diseases using these x-ray images using CNN (Convolutional Neural Networks) architecture VGG19 for making the right diagnosis choices. © 2024 IEEE.

关键词： Image classification

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