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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kale, Geetaniali Joshi, Atharva Shukla, Ishaan Bhosale, Abhishek Pune Insitute of Computer Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350306613

The practice of hiding data within other data such that it is not exposed to any entity other than the intended one is known as steganography. Video steganography is a form of steganography in which private information is embedded into digital video recordings. In the proposed method, the text information is hidden into video frames as well as the audio component of the video. The introduction of randomness in the selection of frames in which information is to be hidden, as well as the use of audio steganography to store additional information aims at boosting security of transmission of information. For additional security, the text is first preprocessed and hashed using the MD5 hashing algorithm before being hidden inside the video components. © 2024 IEEE.

关键词： Cybersecurity Data Hiding Information Exchange Information Security Privacy Video Steganography

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A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4379-4387页

作者： Mishra, Vinaytosh Gupta, Kishu Saxena, Deepika Singh, Ashutosh Kumar Thumbay Institute for Ai in Healthcare Gulf Medical University Ajman United Arab Emirates National Sun Yat-sen University Department of Computer Science and Engineering Kaohsiung804 Taiwan The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965 Japan University of Economics and Human Sciences Department of Computer Science Warsaw01-043 Poland Indian Institute of Information Technology Bhopal Department of Computer Science and Engineering Bhopal462003 India

Electronic Health Records (EHR) are crucial for the success of digital healthcare, with a focus on putting consumers at the center of this transformation. However, the digitalization of healthcare records brings along security and privacy risks for personal data. The major concern is that different countries have varying standards for the security and privacy of medical data. This paper proposed a novel and comprehensive framework to standardize these rules globally, bringing them together on a common platform. To support this proposal, the study reviews existing literature to understand the research interest in this issue. It also examines six key laws and standards related to security and privacy, identifying twenty concepts. The proposed framework utilized K-means clustering to categorize these concepts and identify five key factors. Finally, an Ordinal Priority Approach is applied to determine the preferred implementation of these factors in the context of EHRs. The proposed study provides a descriptive then prescriptive framework for the implementation of privacy and security in the context of electronic health records. Therefore, the findings of the proposed framework are useful for professionals and policymakers in improving the security and privacy associated with EHRs. © 1975-2011 IEEE.

关键词： K-means clustering

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Phish Block:A Blockchain Framework for Phish Detection in Cloud

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computer Systems Science & engineering 2023年第1期44卷 777-795页

作者： R.N.Karthika C.Valliyammai M.Naveena Department of Computer Technology Madras Institute of Technology CampusAnna UniversityChrompet600044India

The data in the cloud is protected by various mechanisms to ensure security aspects and user’s ***,deceptive attacks like phishing might obtain the user’s data and use it for malicious *** Spite of much techno-logical advancement,phishing acts as thefirst step in a series of *** technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the *** phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain *** phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud *** cloud servers are becoming the trusted tool for executing these *** prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of *** these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is *** proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.

关键词： Cloud server phishing URLs phish detection blockchain safe files smart contract

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Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

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computer Systems Science & engineering 2023年第4期45卷 869-886页

作者： Sirisati Ranga Swamy SPhani Praveen Shakeel Ahmed Parvathaneni Naga Srinivasu Abdulaziz Alhumam Department of CSE Vignan’s Institute of Management and Technology for Women GhatkesarTelanganaIndia Department of CSE PVPSITVijayawadaAndhra Pradesh520007India Department of Computer Science College of Computer Sciences and Information TechnologyKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science and Engineering-AIML VNR Vignana Jyothi Institute of Engineering and TechnologyHyderabadTelangana500090India

Coronavirus 2019(COVID-19)is the current global buzzword,putting the world at *** pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources,which are already *** established nations would not be in a perfect position to manage this epidemic correctly,leaving emerging countries and countries that have not yet begun to grow to address the *** problems can be solved by using machine learning models in a realistic way,such as by using computer-aided images during medical *** models help predict the effects of the disease outbreak and help detect the effects in the coming *** this paper,Multi-Features Decease Analysis(MFDA)is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography(CT)scan *** are various features associated with chest CT images,which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from *** current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better *** model’s performance is assessed using Receiver Operating Characteristic(ROC)curve,the Root Mean Square Error(RMSE),and the Confusion *** is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient.

关键词： Chest CT COVID-19 classification ROC curves multi-feature disease analysis

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An energy efficient Dantzig Wolfe Decomposition based parallel optimization for software-defined industrial IoT

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Journal of Intelligent and Fuzzy Systems 2023年第2期44卷 2441-2454页

作者： Sathish, S. Kavitha, K. Poongodi, J. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Electronics and Communication Engineering Kumaraguru College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Ranganathar Institute of Engineering and Technology Tamil Nadu Coimbatore India

The industrial world including the merits of Internet of Things (IoT) paradigm has wide opened the evolution of new digital technology to facilitate promising and revolutionizing dimensions in diversified industrial application. However, handling the deployment challenges of security awareness, energy consumption, resource optimization, service assurance and real-time big data analytics in Industrial IoT Networks is a herculean task. In this paper, Dantzig Wolfe Decomposition Algorithm-based Service Assurance and Parallel Optimization Algorithm (DWDA-SAPOA) is proposed for guaranteeing QoS in energy efficient Software-Defined Industrial IoT Networks. This DWDA-SAPOA is proposed for achieving minimized energy consumption on par with the competitive network routing algorithms which fails in satisfying the strict requirements of heterogeneous Quality of Service (QoS) during the process of optimizing resources under industrial communications. It is proposed as a service assurance and centralized route optimization strategy using the programmability and flexibility characteristics facilitating by the significant Software Defined Networking (SDN) paradigm which is implemented over a multi-layer programmable industrial architecture. It supports bandwidth-sensitive service and ultra-reliable low-latency communication type of heterogeneous flows that represents a routing optimization problem which could be potentially modelled as a multi-constrained shortest path problem. It further adopts Dantzig Wolfe Decomposition Algorithm (DWDA) to handle the complexity of NP-hard involved in solving the multi-constrained shortest path problems. The simulation experiments of the proposed DWDA-SAPOA prove its predominance in minimizing energy consumption by 24.28%, flow violation by 19.21%, packet loss by 21.28%, and end-to-end delay by 29.82%, and bandwidth utilization by up to 26.22% on par with the benchmarked QoS provisioning and energy-aware routing problem. © 2023 - IOS Press. All

关键词： Internet of things

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IVIDNet: Intelligent iris vitality detection via weighted prediction score level fusion

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Multimedia Tools and Applications 2023年第29期82卷 45959-45981页

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Department of Computer Science and Engineering Indian Institute of Technology Jammu PO-Nagrota Jammu181221 India Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

Iris recognition is one of the most widely used human authentication mechanism that has gained huge popularity due to its security and efficiency. However, recently attackers have breached the security of these systems via synthetically generated fake iris traits using a variety of artefacts such as paper printouts, video attacks, and cosmetic lenses. To countermeasure these attacks, an anti-deception sub-system is instilled between sensor and feature extractor modules of the system. These sub-systems intelligently measure the vitality of the presented iris traits and are popularly known as iris vitality detection (IVID) or iris anti-spoofing techniques. Recently, the deep learning-based models are widely deployed for iris anti-spoofing approaches, but these methods result in additional training overhead as well as require larger training dataset. In this paper, we expound a novel iris vitality detection network (IVIDNet) that employs the robust features of two pre-trained deep convolutional neural networks (DCNN). The extracted features are then integrated via a new weighted score-level fusion technique. To demonstrates the efficacy of the proposed technique, experimental analysis is carried out on benchmark iris liveness detection Notre Dame 2017 and Notre Dame cosmetic lenses dataset (NDCLD) 2015. The proposed IVIDNet exhibits excellent performance in the known attack scenario with an average classification error rate (ACER) of 0.60%. Moreover, IVIDNet shows superior performance in the unseen attacks scenario (i.e. cross-database) with an ACER of 9.72%. In overall, the proposed model outperforms other similar iris anti-spoofing approaches and efficient in terms of computational overhead. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Biometrics

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Requirement Classification using Deep Learning and Nature-Inspired Optimization Technique

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第6期49卷 117-146页

作者： Sonawane, Sonal Puthran, Shubha Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering SVKM’s NMIMS Mumbai400056 India

Recent advancements in requirements engineering (RE) methods have increasingly leveraged machine learning (ML) algorithms to address challenging RE issues such as the identification and classification of software requirements in requirement documents. Classifying software requirements accurately and efficiently is crucial for the success of the project. Functional and nonfunctional requirements define the core attributes and constraints of a system. When requirements are documented in natural language, they often introduce uncertainties such as ambiguity, inconsistency, or poor readability making the process challenging. Further, manual extraction of requirements is tideous and error prone as it requires precise interpretation, thereby mitigating the risks of miscommunication and errors in the development *** address these challenges, it is vital to employ Natural Language Processing (NLP)techniques to enhance the clarity of requirements. This paper proposes a method that combines deep learning models with NLP techniques, supplemented by Flower Pollination Optimizer (FPO) algorithm, to automate the classification of requirements. The methodology leverages NLP to extract significant features for training a Convolutional Neural Network (CNN) model. CNN model is enhanced using the FPO algorithm to ensure better convergence. The performance of the proposed CNN-FPO framework is evaluated using an industry SmartNet dataset, with results indicating that the accuracy of the classification can reach up to 98% as compared to other traditional machine learning(ML) *** integration of NLP with an optimized CNN model creates a robust framework for requirement classification, addressing the challenges posed by natural language documentation in requirements engineering. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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A New Median Filter Circuit Design Based on Atomic Silicon Quantum-Dot for Digital Image Processing and IoT Applications

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IEEE Internet of Things Journal 2025年第13期12卷 23000-23007页

作者： Ahmadpour, Seyed-Sajad Avval, Danial Bakhshayeshi Navimipour, Nima Jafari Rasmi, Hadi Heidari, Arash Kassa, Sankit Misra, Neeraj Kumar Navin, Ahmad Habibizad Mosleh, Mohammad Hosseinzadeh, Mehdi Patidar, Mukesh Kadir Has University Department of Computer Engineering Istanbul Turkey Sakarya University Department of Information Systems Engineering Sakarya Turkey Department of Computer Engineering Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Yunlin64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan Islamic Azad University Department of Computer Engineering Dez.C. Dezful Iran İstanbul Atlas University Faculty of Engineering and Natural Science Department of Computer Engineering Istanbul Turkey Symbiosis International Deemed University Symbiois Institute of Technology Pune India VIT-AP University School of Electronics Engineering Andhra Pradesh Amaravathi522237 India Islamic Azad University Department of Computer Engineering Tabriz Branch Tabriz*** Iran Islamic Azad University Department of Computer Engineering Dezful Branch Dezful Iran Duy Tan University Institute of Research and Development Da Nang Viet Nam Indore Institute of Science and Technology Department of Electronics and Communication Engineering Madhya Pradesh Indore453111 India

Digital Image Processing (DIP) is the ability to manipulate digital photographs via algorithms for pattern detection, segmentation, enhancement, and noise reduction. In addition, the Internet of Things (IoT) acts as the eye and system for all DIP in various applications. It can possess a camera or another image sensor in order to capture real-time data from its environment. All vital data is processed by image processing in such a way that it recognizes the object, detects an anomaly, and automatically decides in real-time. In addition, in an IoT system, the median filter is the technique used for noise reduction by substituting the value of the pixel with the central value of the surrounding pixels. It provides speed and efficiency for quick analysis in all IoT systems. However, the images can get corrupted, especially in resource-constrained IoT devices with small cameras, because of random glitches. Moreover, using new quantum technology like atomic-scale silicon dangling bond (DB) logic circuits, which have advanced in fabrication and become a strong contender for field-coupled nano-computing, can solve previous problems in IoT systems. In this paper, we propose a unique quantum CSM based on two new proposed Mux and De-mux. The proposed CSM can be used for computational circuits like median filter circuits (MFC) in a wide range of digital circuits, specifically IoT devices. The proposed design is verified and validated using the powerful SiQAD tool. When comparing CSM to the newest designs, the suggested quantum circuit uses 85% less energy and takes up 61% less area. © 2014 IEEE.

关键词： Dangling bonds

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Detection Of Phishing Attacks Based On Url By Using Machine Learning 10

Detection Of Phishing Attacks Based On Url By Using Machine ...

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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mekalarani, T.M.S. Subhasree, R.S. Reddy, R. Thrisha Chandini, Shaik Kumar, Yadhallapalle Satheesh Valli Shaik, Ashraf Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9798350353778

Phishing, a cybercrime orchestrated by intruders or hackers, aims at duping unsuspecting individuals into revealing sensitive information via deceptive websites. The primary objective of this nefarious activity is to obtain personal or classified data from victims, which can then be exploited for diverse purposes. The stolen information, often sold in online black markets, commands varying prices based on its importance, ranging from a few dollars to thousands. Techniques like machine learning (ML) and deep learning (DL) offer strong instruments for analyzing and thwarting such complex threats. The project aims to protect users and websites from these impending risks by putting forth a machine learning-based phishing detection system. Drawing upon a URL-based dataset sourced from a reputable repository, encompassing attributes from both phishing and legitimate URLs from over eleven thousand websites, the process involves several preprocessing stages. Later, other machine learning methods are used, including Random Forest and Decision Trees, combined to build a strong detection model. To enhance efficacy, the system integrates TF-IDF values of webpages, resulting in superior accuracy compared to prior methodologies. © 2024 IEEE.

关键词： Phishing

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Experimental Study on Designing Advanced Driver Assistance System for Two-Wheelers Using Mobility and Physiological Sensors 18

Experimental Study on Designing Advanced Driver Assistance S...

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18th IEEE International Conference on Vehicular Electronics and Safety, ICVES 2024

作者： Vyas, Daiwat Amit Chaturvedi, Manish Kumar, Naveen Kamath, Karan Gupta, Piyusha Dwivedi, Aryan Department of Computer Science and Engineering India Institute of Technology Nirma University India IITRAM Department of Computer Science and Engineering India Vadodara India SRM Institute of Science and Technology India

ISBN: (纸本)9798350387278

Advanced Driver Assistance Systems (ADAS) enhance driver safety through various technologies. In high-end cars, ADAS typically uses computer vision-based solutions, such as eye-tracking, to monitor driver attention, mood, and drowsiness. However, these ADAS solutions are challenging to implement for two-wheeler (2W) drivers, who constitute about 80% of drivers population in India, mainly due to helmet usage and the unique dynamics of 2W vehicles. This paper investigates improving 2W driver safety through integrating mobility and physiological sensors for ADAS. Experimental studies evaluated a novel ADAS framework combining mobility sensor data (e.g., speed, acceleration, position) with physiological parameters (e.g., heart rate, etc.). This fusion of sensors data approach enables monitoring vehicle dynamics and driver conditions for providing adaptive safety interventions by identifying aggressive/non-aggressive and stress/non-stress patterns. To facilitate the collection of mobility and physiological sensors data a mobile application was developed using Flutter. The physiological parameters were captured from the smart-watch paired with the smart-phone and the mobility parameters were captured from the mobility sensors that were in-built in the smart-phone. The data was collected in Navi-Mumbai area of India. Preliminary experiments conducted using these integrated sensors data show promising results. To incorporate supervised approach for Driver Behaviour Analysis (DBA) requires a tedious task of manual tagging and each driver reacts differently to the different situations that arise while driving. So, an un-supervised learning approach has been incorporated and clustering techniques like k-Means, Gaussian Mixture Model (GMM), Hierarchical Clustering, and k-Means with Dynamic Time Wrapping (DTW) analysed the sensor data to identify patterns indicating potential hazards or driver impairment. The findings based on the preliminary experimental studies done indicate t

关键词： Advanced driver assistance systems

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