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Elevating Image Steganography:A Fusion of MSB Matching and LSB Substitution for Enhanced Concealment Capabilities

computers, Materials & Continua 2024年第5期79卷 2923-2943页

作者： Muhammad Zaman Ali Omer Riaz Hafiz Muhammad Hasnain Waqas Sharif Tenvir Ali Gyu Sang Choi Department of Information Technology The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science The Islamia University of BahawalpurBahawalpur63100Pakistan School of Computer Science and Engineering Yeungnam UniversityGyeongbuk38541Korea

In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.

关键词： Steganography most significant bit(MSB) least significant bit(LSB) peak signal-to-noise ratio(PSNR)

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Implementation of Deep Learning Techniques for Deepfake Classification: A comparative study using ResNet-50 and VGG16 7

Implementation of Deep Learning Techniques for Deepfake Clas...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Yesugade, Kiran Jadhav, Rohini College of Engineering Department of Computer Engineering Pune India College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798331527822

This work investigates the use and assessment of ResNet-50 and VGG16 deep learning models for detecting deepfake images using the Labelled Faces in the Wild (LFW) dataset. The methodology encompassed thorough preparation of the dataset and the utilisation of both models to differentiate between authentic and altered facial photos. The models' performance was evaluated using fundamental criteria including accuracy, precision, recall, and F1 score. VGG16 demonstrated superior performance compared to ResNet-50, achieving an accuracy of 93.87%, recall of 96.23%, and an F1 score of 94.41%, while ResNet-50 achieved a precision of 92.54%, recall of 95.26%, and F1 score of 93.88%. Although both models achieved good accuracy, VGG16 had superior stability and generalization across the whole dataset. The study determines that VGG16 is the more dependable model for detecting deepfakes on the LFW dataset. It suggests that future efforts should concentrate on enhancing ResNet-50 or creating ensemble methods to enhance detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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WGSDMM+GA: A genetic algorithm-based service clustering methodology assimilating dirichlet multinomial mixture model with word embedding

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Future Generation computer Systems 2023年第1期145卷 254-266页

作者： Agarwal, Neha Sikka, Geeta Awasthi, Lalit Kumar Computer Science and Engineering Department Indian Institute of Information Technology Karnataka Raichur584135 India Department of Computer Science and Engineering National Institute of Technology Delhi Delhi110036 India 246174 India

Advancement of web 2.0 results in the expeditious growth of services in repositories and service portals, which raises the demand for service management. With the clusters of services, different processes like discovery, selection, ranking, etc., can be expedited efficiently. K-Means clustering is highly recommended for service clustering but it can be stuck in the local optimum values. Developers generally use short text to describe the functionality of Web services. For effective service clustering, it is required that relevant features of services are mapped in vector space based on semantic relations. Gibbs Sampling algorithm for Dirichlet Multinomial Mixture model (GSDMM) model is able to provide good performance on short text because it works on the assumption that there will be one topic related to one document. In this paper, a novel technique for web service clustering, i.e., WGSDMM+GA, is proposed in which relevant features, semantic relations, and their mappings in vector space are analyzed based on an assimilated approach of Word2Vec and GSDMM models. As K-means clustering cannot provide effective results due to local optima problem, GA-based clustering is applied in which fitness function is based on Manhattan distance. The proposed model is applied to five real-time datasets, and the performance is evaluated based on standard evaluation measures. Experimental results illustrate that WGSDMM+GA yields better results, and the accuracy score is increased by 23%, 17%,17%, 21%, and 25% on five real-time datasets. © 2023 Elsevier B.V.

关键词： Genetic algorithms

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SciAR Labs: Exploring Science Through Augmented Reality

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SN computer Science 2025年第5期6卷 1-14页

作者： Sondagar, Bhavisha Sheth, Dhruvi Kachare, Kaustubh Pawar, Renuka Kalbande, Dhananjay Department of Information Technology Sardar Patel Institute of Technology Mumbai India Department of Computer Science and Engineering Sardar Patel Institute of Technology Mumbai India

SciAR Labs presents a science education Augmented Reality (AR) application in response to the changing landscape of educational technologies. Thorough studies of the research demonstrate how AR can improve student motivation, engagement, and performance. Research highlights the benefits of augmented reality (AR) in social learning environments, special education, and distant learning, but it also notes drawbacks, including expensive hardware, poor content quality, and teacher shortages. In order to overcome these issues, SciAR Labs is developing an engaging and user-friendly augmented reality experience for science investigations. The initiative aims to break through obstacles in conventional learning methods by utilizing AR’s advantages. It’s leading the way in integrating technology into education with the goal of improving student learning and adding to the conversation about augmented reality’s potential in the classroom. By combining scientific material with user-friendly interfaces, it hopes to make an influence on science education and encourage students’ interest and understanding. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Augmented reality (AR) Education Science labs Unity User experience Virtual labs Vuforia

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SiC JFETs Modular Cascaded Method with Active Clamp Control Strategy for DC Solid-state Circuit Breaker

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CSEE Journal of Power and Energy Systems 2023年第6期9卷 2435-2446页

作者： Hong Duan Wei Wang Zhikang Shuai Dong He Daixin Chen Z.John Shen the College of Electrical and Information Engineering Hunan UniversityChangsha 410006China the Department of Electrical and Computer Engineering Illinois Institute of TechnologyChicagoIL 60616 USA.

Power semiconductor devices cascaded topology is one of the most common solutions for solid-state circuit breakers(SSCBs)working in medium-voltage DC ***,when the number of cascaded devices is large,current series technologies of power semiconductor devices will be difficult to be applied since with uneven voltage sharing ***,this paper proposes a novel cascade method of multi-SiC JFETs based on modules *** method consists of two parts:one is a single-gate driver cascaded SiC JFETs topology which is used to form the module,the other one is an active clamp control strategy which ensures that each module is protected from overvoltage when modules are *** proposed cascade method can effectively suppress voltage overshoot of cascading power devices in the switching *** on the proposed cascade method,a 5kV/63A SSCB prototype is ***,an experiment is conducted based on the designed *** results validate the effectiveness of the proposed cascaded method for SSCB.

关键词： Active control clamp DC solid-state circuit breaker modular SiC JFETs series connection

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Identifying Authentic Reviews on Amazon Dataset using Transformer-Based Models

Identifying Authentic Reviews on Amazon Dataset using Transf...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Azam, Hadiqa Chaudhry, Hassan Nazeer Majid, Muhammad Computer Department University of Engineering and Technology Taxila Pakistan Computer Science Department Barani Institute of Information Technology Rawalpindi Pakistan

ISBN: (纸本)9798331504465

Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-based model that utilizes GloVe (Global Vectors for Word Representation) semantic embeddings to detect fraudulent reviews. Our approach captures subtle linguistic patterns indicative of fake reviews by fine-tuning a pre-trained GloVe model on a labelled dataset. The GloVe embeddings improve the model’s grasp of word semantics, while an attention-based neural network classifies reviews as genuine or fraudulent. Our model has surpassed traditional machine learning methods, achieving 98% accuracy across various datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Fake News Classification: Past, Current, and Future

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computers, Materials & Continua 2023年第11期77卷 2225-2249页

作者： Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry Department of Computer Science University of Engineering and TechnologyLahore54890Pakistan Department of Computer Science&Information Technology Abu Dhabi UniversityAbu Dhabi59911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbulTurkey

The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social *** can quickly fabricate comments and news on social *** most difficult challenge is determining which news is real or ***,tracking down programmed techniques to recognize fake news online is *** an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media *** study shows past,current,and possible methods that can be used in the future for fake news *** different publicly available datasets containing political news are utilized for performing *** supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text *** contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer ***,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future.

关键词： Supervised learning algorithms fake news classification online disinformation transformers recurrent neural network(RNN)disinformation transformers recurrent neural network(RNN)

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A voting-based trustworthy distributed IoT attack detection model

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Personal and Ubiquitous Computing 2025年第1期29卷 103-118页

作者： Sharma, Priya Sharma, Sanjay Kumar Dani, Diksha University School of Information & Communication Technology Gautam Buddha University Greater Noida India Department of Computer Engineering SVKM’s NMIMS Mukesh Patel School of Technology Management & Engineering Mumbai India

Besides the enhancement of the Internet of Things (IoT) distributed environment, anomalous activities are also escalating rapidly. Therefore, improving the trustworthiness of distributed networks is required for the extensive adoption of IoT infrastructure. Establishing a security mechanism in IoT networks is a challenging task as communication links are lossy and connected devices are resource-dependent. Conventional security techniques such as intrusion detection systems (IDS) are insufficient to shelter the IoT-distributed environment due to less computational capacity, restricted upgraded devices, and mismatched protocols. This paper proposes a novel machine learning-based trustworthy model for IoT attack detection. The proposed system combines the capability of Ada-boost and Gradient-boost to classify anomalous activities with low computational capacity proficiently and within a minimum time frame. Experiments were conducted on Distributed Smart Space Orchestration System (DS2OS) IoT dataset to assess the significance of the novel attack detection model. The demonstration shows that the proposed model obtains 98.28% training accuracy, 98.26% testing accuracy, and 99.25% AUC score. A confusion matrix has also been calculated to check the authenticity of the results—a comparison of the proposed model with individual learners and existing models proves that the proposed model outperformed. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Network intrusion

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Stairdepth: a novel staircase detection through depth maps generated by depth anything V2

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International Journal of information technology (Singapore) 2025年第4期17卷 2075-2086页

作者： Shekar, Avire Laxmi Chandra Chandrika, Mukkolla Bhuvana Naidu, Vakkalagadda Hemanth Muppalaneni, Naresh Babu Department of Computer Science and Engineering Indian Institute of Information Technology Design and Manufacturing Kurnool Kurnool India

Detection of a staircase is an important task in the fields of both assistive technology and autonomous navigation with an aim to enable substantial improvement in safety and accessibility for both those with limited vision and those with mobility problems. Classic methods often perform poorly under challenging conditions, such as low lighting, irregular architectural arrangements, or occluded views. Whereas the integration of physical depth sensors has increased detection accuracy, this integration introduces increased complexity, cost and greater maintenance issues. Our work proposes a novel architecture capable of directly estimating depth maps from color images using a pre-trained model without relying on physical sensors for depth estimation. It detects the staircase efficiently, including the classification of stair lines into concave or convex, with the classification of surfaces as a tread or riser, hence localizing staircases accurately. Our proposed methodology demonstrated exceptional results, with 83.2% Precision, 90.8% Recall, and 72.2% Intersection over Union on stair line classification and 97.1% Pixel Accuracy and 82.9% Mean Pixel Accuracy on stair surfaces by outperforming the efficacy of current approaches pertaining to stair line and surface classification. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Depth image Segmentation Stair surfaces Staircase detection StairDepth Stairline

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Fault tolerance challenges in wearable computing for vital applications: a survey

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Journal of Medical engineering and technology 2024年第2期48卷 48-63页

作者： Sepahvand, Majid Meqdad, Maytham N. Abdali-Mohammadi, Fardin Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Department of Intelligent Medical Systems Al-Mustaqbal University Babil Hillah Iraq

Wearable computers can be used in different domains including healthcare. However, due to suffering from challenges such as faults their applications may be limited in real practice. So, in designing wearable devices, designer must take into account fault tolerance techniques. This study aims to investigate the challenging issues of fault tolerance in wearable computing. For this purpose, different aspects of fault tolerance in wearable computing namely hardware, software, energy, and communication are studied;and state of the art research regarding each category is analysed. In this analysis, the performed works using the fault tolerance techniques are included in the form of 25 components and referred to as "fault tolerance plan". Using this fault tolerance plan and the appropriate profile, the fault tolerance of any wearable system can be evaluated. In this article, fault tolerances of several of the most prominent works conducted in the field of wearable computing were evaluated. The obtained results, with the medical profile, showed that only one wearable system had a fault tolerance of 91%, with the other systems having a fault tolerance of 24% or less. Also, the results obtained from evaluating these works, with the military profile, showed that only one wearable system had a fault tolerance of 76%, with the other systems having a fault tolerance of 19% or less. These mean that few studies have been conducted on the fault tolerance of wearable computing. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fault tolerance

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