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Secured Storage of Information Using Audio Steganography 7th

Secured Storage of Information Using Audio Steganography

7th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2022

作者： Sowmya, M.R. Shreenath, K.N. Shetty, Saritha Shetty, Savitha Wajid, Salman Kantharaj, Yashas Department of Computer Science and Engineering NMIT Yelahanka Bangalore India Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Department of MCA NMAM Institute of Technology Karkala India Department of CSE NMAM Institute of Technology Karkala India

ISBN: (纸本)9789811954818

The use of smartphones and cloud storage has expanded dramatically as technology progresses. The data that are kept and used are now more vulnerable to breaches. Using the Internet services, the end users’ data files can be accessed and manipulated from any other computer. If the secrecy of the information is of very high value, it should be secured. If we want to prevent unauthorized disclosure or change of information, it must be protected. The old cryptographic methods rely on keys and have a point of failure, and they attract the third party who is on lookout for crucial information. Steganography can be used to tackle this problem. It is a secret method of encrypting data in another form so that it can be securely stored and transferred over any medium. In the following paper, the idea of steganography, implementation methods, and storing the data in an audio file is discussed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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An In-Depth Study of Handwritten Digit Recognition Methods: An Exploration of Deep Learning and Machine Learning Approaches

An In-Depth Study of Handwritten Digit Recognition Methods: ...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Singh, Nongmeikapam Thoiba Tiwary, Priyanshu Chanu, Asem Debala Chandigarh University Department of Computer Science and Engineering Punjab India Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India

ISBN: (纸本)9798350319200

This article explains how deep learning and machine learning techniques are being utilized to replace humans in a variety of activities, like item classification and sound addition. It emphasizes handwritten text recognition (HTR), which involves computers interpreting handwritten content from sources like paper documents and touchscreens. For recognizing handwritten digits, the study analyzes three algorithms: support vector machines (SVM), multilayer perception (MLP), and convolutional neural networks (CNN). This comparison aims to evaluate the correctness and execution times of different approaches. The evaluation is based on parameters like the dataset used, the number of epochs, algorithm complexity, accuracy, and hardware specifications. The study compares the effectiveness of CNN, Multilayer Perception (MLP), and Support Vector Machines using handwritten digits from the MNIST dataset. It evaluates these algorithms based on criteria such as runtime, complexity, rate of precision, the count of epochs, and the quantity of hidden layers specifically within deep learning techniques. SVM had the highest training data accuracy, but CNN outperformed others on the test dataset. The article's primary goal is to identify the most precise model for recognizing handwritten digits. It achieves this by assessing the performance of different algorithms and comparing them based on various characteristics. © 2023 IEEE.

关键词： convolutional neural networks execution time handwritten digits multilayer perceptron overfitting visualization

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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Modelling of Fuzzy Logic Based System for the Implementation of Model for Healthcare System with Accurate Prediction 4

Modelling of Fuzzy Logic Based System for the Implementation...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Md Srv Media Pvt. Ltd India India Pune Department of Cse Symbiosis Institute of Business Management Nagpur India Department of Cse India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350360165

Budgetary restrictions force healthcare institutions to endure ongoing deterioration, which calls for better building capital deterioration modeling. A more sophisticated predictive model is essential to implementing a proactive maintenance approach and lowering unexpected failures. Hospitals now make subjective deterioration estimates based just on an individual's age, which creates uncertainty. This research addresses the shortcomings of existing approaches by putting forward a novel stochastic degradation prediction model. The model, a fuzzy Markovian technique based on hybrid genetic algorithms, reduces subjectivity by anticipating based on the most recent observed circumstances. The model uses a nonhomogeneous transition matrix of probabilities (TPM) with fuzzy membership functions of condition, maturity, and relative degradation rate due to the paucity of data on the condition evaluation of important infrastructure. Genetic algorithm calibration improves TPM accuracy by addressing the limitations of expert-based models. A sensitivity analysis checks for changes in output when parameters are changed, ensuring the resilience of the model. Real hospital datasets are used for validation against a cutting-edge stochastic model, and the results show improved performance over existing approaches. In order to provide the best possible administration of healthcare facilities, this approach offers to assist decision-makers in creating effective maintenance of data-driven capital renewal strategies. © 2024 IEEE.

关键词： Prediction models

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The EM Algorithm is Adaptively-Optimal for Unbalanced Symmetric Gaussian Mixtures

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Journal of Machine Learning Research 2022年第1期23卷 1-79页

作者： Weinberger, Nir Bresler, Guy The Viterbi Faculty of Electrical and Computer Engineering Technion|Israel Institute of Technology Haifa3200004 Israel Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

This paper studies the problem of estimating the means ± θ2 Rd of a symmetric twocomponent Gaussian mixture δN(θ;I) + (1 - δ) N(-θ;I), where the weights δand 1 - δare unequal. Assuming that δis known, we show that the population version of the EM algorithm globally converges if the initial estimate has non-negative inner product with the mean of the larger weight component. This can be achieved by the trivial initialization θ0 = 0. For the empirical iteration based on n samples, we show that when initialized at θ0 = 0, the EM algorithm adaptively achieves the minimax error rate O ( min n 1 (1-2δ) q d n;1 kθk q d n;- d n )1/4 }) in no more than O ( 1 θ (1-2δ) ) iterations (with high probability). We also consider the EM iteration for estimating the weight δ, assuming a fixed mean θ (which is possibly mismatched to θ). For the empirical iteration of n samples, we show that the minimax error rate ∼O ( 1 θ q d n ) is achieved in no more than O ( 1 θ2 ) iterations. These results robustify and complement recent results of Wu and Zhou (2019) obtained for the equal weights case δ= 1/2. © 2021 Nir Weinberger and Guy Bresler.

关键词： Maximum principle

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CLAACS-IOD: Certificate-embedded lightweight authentication and access control scheme for Internet of Drones

CLAACS-IOD: Certificate-embedded lightweight authentication ...

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作者： Sadhukhan, Dipanwita Ray, Sangram Dasgupta, Mou Rodrigues, Joel J. P. C. Department of Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla India Department of Computer Application National Institute of Technology Raipur Raipur India Qingdao China Instituto de Telecomunicações Covilhã Portugal

In recent era, the unmanned aerial vehicles (UAVs) commonly known as drones has emerged as a one of the most significant and promising tools which has demonstrated its wide range of implementations variating from commercial domain to the field of defense due to its distinct capabilities such as inspection, surveillance, precision and so forth. Internet connected drones provides a propitious trend that boosts the flying safety, and service qualities of the UAVs where numerous low-altitude drones winged in different flying regions for executing a precise task such as gathering the real-time information from the unuttered environment to be interpretated by users. Nevertheless, the open-access insecure communications in hostile environment, the issues like safety and confidentiality threats, various security concerns such as the leakage of flying courses, identities, position, and gathered data by the drones are upstretched. To address these security concerns, access control mechanism provides a potential service in terms of authentication and key agreement for securing the communication between the individual drones within their respective flying regions. This article introduces a robust, efficient, lightweight, and privacy preserving ECC integrated access control approach by employing digital certificate with considering the high dynamicity and mobility of the drones. The designing of this proposed approach, that is, CLAASC-IoD is influenced by the aim of inter-drone and drone-to-ground station communication in the IoD paradigm. The detailed analysis of security using probabilistic random oracle model as well as simulation using well-accepted security verification tool AVISPA and comparative performance evaluation supports the claim of robustness, effectiveness, and proficiency. © 2023 John Wiley & Sons Ltd.

关键词： Internet of things

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A Neuromorphic Spiking Neural Network Using Time-to-First-Spike Coding Scheme and Analog Computing in Low-Leakage 8T SRAM

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IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2024年第5期32卷 848-859页

作者： Chen, Chao-Yu Dai, Yan-Siou Hong, Hao-Chiao Institute of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu300093 Taiwan Novatech Microelectronics Corporation Technology Innovation Department Hsinchu300092 Taiwan

This article demonstrates the first functional neuromorphic spiking neural network (SNN) that processes the time-to-first-spike (TTFS) encoded analog spiking signals with the second-order leaky integrate-and-fire (SOLIF) neuron model to achieve superior biological plausibility. An 8-kb SRAM macro is used to implement the synapses of the neurons to enable analog computing in memory (ACIM) operation and produce current-type dendrite signals of the neurons. A novel low-leakage 8T (LL8T) SRAM cell is proposed for implementing the SRAM macro to reduce the read leakage currents on the read bitlines (RBLs) when performing ACIM. Each neuron's soma is implemented with low-power analog circuits to realize the SOLIF model for processing the dendrite signals and generating the final analog output spikes. No data converters are required in our design by virtue of analog computing's nature. A test chip implementing the complete output layer of the proposed SNN was fabricated in 90-nm CMOS. The active area is 553.4 118.6 {m}{2}. The measurement results show that our SNN implementation achieves an average inference latency of 196 ns and an inference accuracy of 81.4%. It consumes 242 {W} with an energy efficiency of 4.74 pJ/inference/neuron. © 1993-2012 IEEE.

关键词： Neural networks

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PROMO: PROactive MObility-support model for task scheduling in fog computing

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International Journal of computers and Applications 2022年第11期44卷 1092-1101页

作者： Kaur, Navjeet Kumar, Ashok Kumar, Rajesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India School of Computer Application Lovely Professional University Phagwara India Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala India

In fog computing, closer proximity of fog devices to the end-user provides several benefits, such as reduced End-to-End (E2E) delay and better Quality of Service (QoS). Although, fog computing gives promising benefits of uninterrupted services to mobile users. The critical challenge is to decide when and where to re-schedule tasks according to the user's mobility. This paper targets this issue and proposes a Proactive Mobility-support (PROMO) model for re-scheduling tasks to mitigate the issues that influence QoS in mobility. The PROMO model is implemented and validated on MobFogSim, which significantly improves delay with 37% and around 17% downtime during task re-scheduling. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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A task clustering based QoS aware scheduling algorithm for task execution in cloud-Iot model for education services

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Multimedia Tools and Applications 2023年第29期82卷 44783-44800页

作者： Chowdhary, Sunil Kumar Rao, A.L.N. Faculty of Computer Science and Engineering Dr. A P J Abdul Kalam Technical University Uttar Pradesh Lucknow India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Noida India

Some of the conventional approaches to task scheduling are discussed in this section. The scheduling of tasks in the Internet of Things (IoT) application is a complex job in cloud computing due to the heterogeneity characteristics of the IoT. Therefore, an efficient scheduling approach is vital in improving the performance of the Cloud-IoT applications. The improvement in QoS mainly depends on efficient task scheduling and allocating the task to the appropriate Virtual Machine (VM). This paper uses a clustering-based approach to design an optimal solution for task scheduling and VM allocation for IoT applications in education services. The workflow is simplified with horizontal and vertical clustering, and VM is allocated. The task is classified into coarse and fine-grained, based on the average execution time of the total number of tasks. The average execution time is calculated based on the best and worst performance of VM. The vertical clustering approach clusters the tasks into sequential and parallel, thereby turning the huge volume of tasks into simple ones. The horizontal clustering approach selects the VM with the help of a bipartite matching graph which enhances the Quality of Service (QoS). The proposed task clustering has been evaluated in metrics like makespan, response time, throughput, total execution time, task completion time, and VM utilization. The proposed approach achieved a makespan of 2500 ms and throughput of 83 Mbps for 100 tasks, a response time of 0.12 ms, a total execution time of 640 ms, and a total task completion time of 340,000 ms for a 2 arrival rate based on performance evaluation results. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Quality of service

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Ransome Ware Detection Using Machine Learning and Deep Learning Models 12th

Ransome Ware Detection Using Machine Learning and Deep Learn...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vivek, Vinnakota Sai Lokesh, Vadlamudi Kaushik, Tata Kumaran, U. Umah, B. Devarajan, Gopal Ganesh Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

This research addresses the critical challenge of ransomware detection through the use of deep learning and machine learning methods. Because ransomware is a serious threat to cybersecurity, it is imperative that advanced techniques be used for prompt detection and mitigation. Using a large dataset, the study concentrates on pertinent features necessary for efficient machine learning model training. The suggested model shows promising results in identifying and averting ransomware attacks by utilizing cutting-edge algorithms. The model’s effectiveness is confirmed by the evaluation metrics (F1 score, recall, accuracy, and precision). By providing a proactive defense against emerging ransomware threats, the findings enhance cybersecurity measures. The study not only provides a strong methodology for ransomware detection but also encourages more research in the area with the goal of continuously bolstering cyber defenses against evolving cyber threats. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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