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An Efficient Use of Toffoli Quantum Gate for Boosting the Embedding Capacity in DNA Sequence

An Efficient Use of Toffoli Quantum Gate for Boosting the Em...

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2022 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2022

作者： Nahar, Mahbubun Kamal, A.H.M. Jatiya Kabi Kazi Nazrul Islam University Dept. of Computer Science and Engineering Trishal Bangladesh

ISBN: (纸本)9781665453059

While hiding data in DNA sequence, the steganograpy schemes try to increase the embedding payload, enhance the security of implanted data, protect the stego expansion and preserve the biological characteristics of DNA sequence. In this research, we have proposed a new data hiding method where the scheme implants secrets in DNA sequence using a classical Toffoli quantum gate mechanism. The proposed scheme implants 4-bits of information per nucleotide. Experimental results demonstrate that the proposed scheme achieves massive improvement over the others which is 4 to 12-times higher than the competing schemes. © 2022 IEEE.

关键词： Steganography

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An Efficient CNN-LSTM Approach for Smartphone Sensor-Based Human Activity Recognition System 5

An Efficient CNN-LSTM Approach for Smartphone Sensor-Based H...

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5th International Conference on Computational Intelligence and Networks, CINE 2022

作者： Choudhury, Nurul Amin Soni, Badal National Institute of Technology Silchar Dept. of Computer Science & Engineering Assam Silchar788010 India

ISBN: (纸本)9781665464659

Human Activity Recognition - HAR is one of the most popular area in the filed of sensor technology and smart learning algorithms. Deep learning algorithms are immensely exploited in HAR systems as it eliminates the need of manual feature engineering. Researchers use normal and hybrid deep learning schemes for training and comparing the models. This paper proposes an efficient CNN-LSTM model for recognising daily human activities using smartphone sensor data. A contemporary CNN-LSTM model is created using time distributed feature extraction layers as it can efficiently handle hierarchical features and can selects the relevant features easily using LSTM memorization scheme. The proposed CNN-LSTM model is compared with two other models - DNN and LSTM in terms of accuracy, precision, recall, F1- score, training loss and computational times. The proposed model managed to outperform other models optimally in all the evaluation metrics. Using holdout training and testing split, the model managed to achieve an average accuracy of 97.609% and 98.69% with relu activation function and 100 training iteration. On validating the different models, the hybrid models takes less computational time and managed to achieve an computational efficiency of (76.23 ± 140.76)% from other models. © 2022 IEEE.

关键词： Wearable sensors

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VOID - An Assistant (Chatbot) to Education Queries System

VOID - An Assistant (Chatbot) to Education Queries System

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Chakraborty, Debaissh Mishra, Anjana Kumar, Vikash Singh, Saloni Hani, Harshita C.V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665461092

Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot afford man-power to interact with clients and customers 24×7 so that chatbot has come into play which does the same work with no extra cost. It is a new and emerging technology that is in high demand across a wide range of businesses. It is used to simulate a human like conversation and a machine to relieve the workload. It can be easily deployed in any field where products must be developed and evaluated before being released. During the admission time in college, reception is packed, and people have to wait for their turn, and this entire process takes too much time. Although every college has their own official website, but everyone cannot find the appropriate answer of their query. The objective of this paper is to reduce the workload by developing an interactive environment between a human and a machine that gives a reply to the query of the user on behalf of a human for the education system. © 2022 IEEE.

关键词： Query processing

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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A Semantic Driven Framework for Energy Efficient Task Scheduling in Dynamic Cloud Environment

A Semantic Driven Framework for Energy Efficient Task Schedu...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Aarav Kannan J R.S. Amshavalli Venkateswara Reddy D Vegesna Surya Vinay Varma G. Kavitha Dept of Biomedical Engineering SRM Institute of Science and Technology Chennai India Dept of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The rapid expansion of cloud computing has led to increased energy consumption, raising concerns about its environmental impact. Despite advancements in scheduling algorithms, existing methods face critical challenges, including suboptimal energy efficiency, limited adaptability to dynamic cloud environments, and insufficient context-aware decision-making. Recent techniques, such as Meta Reinforcement Learning, Genetic Algorithms, and Energy-Aware Scheduling, show potential but often struggle with high computational complexity, static resource allocation, or scalability issues. These shortcomings result in inefficient resource utilization, elevated energy usage, and difficulties in meeting Service Level Agreement (SLA) requirements. To address these challenges, this study introduces the Energy Efficient Semantic Scheduling Framework (ESSF), a novel approach combining semantic reasoning and energy profiling to optimize task scheduling in dynamic cloud environments. The framework integrates three key components: (1) Dynamic Energy Profiling (DEP) for real-time energy monitoring, (2) Semantic Resource Matching (SRM) utilizing knowledge graphs for context-aware allocation, and (3) Adaptive Workload Balancing (AWB) to dynamically redistribute tasks for improved resource utilization and energy efficiency. Experiments using the Google Cluster Trace Dataset demonstrate that ESSF reduces energy consumption by up to 25% compared to traditional methods while achieving 90% resource utilization and 97% SLA compliance. These results highlight the potential of the proposed framework to overcome existing limitations, offering a scalable, adaptive, and environmentally sustainable solution for task scheduling in cloud computing.

关键词： Cloud computing Energy consumption Processor scheduling Semantics Knowledge graphs Dynamic scheduling Energy efficiency Real-time systems Resource management Service level agreements

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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Skin Cancer Detection Using CNN and Ensemble Learning 1

Skin Cancer Detection Using CNN and Ensemble Learning

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1st IEEE International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024

作者： Samantaray, Ruturaj Afrin, Karishma Behera, Bichitrananda Bag, Kirtimayee Tata Consultancy Services Limited Tcs BaNCS Bangalore560066 India C. V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar752054 India

ISBN: (纸本)9798350355093

The global prevalence of skin cancer has seen a concerning increase over recent decades, now accounting for one in every three cancer cases worldwide. The unchecked growth of aberrant cells in the skin can lead to skin cancer, a potentially fatal illness. There are different types of skin cancer, namely, DF (Dermatofibroma), AKIEC (Actinic Keratoses and Intraepithelial Carcinoma), Mel (Melanoma), BCC (Basal Cell Carcinoma), VASC (Vascular Lesions), BKL (Benign Keratosislike Lesions), and NV (Melanocytic Nevi). Skin cancer can spread to nearby tissues in our body if it is not caught early. Presently, diagnosing skin cancer is a time-consuming and financially burdensome process for both patients and healthcare systems. Moreover, diagnosing skin cancer heavily relies on human cell detection. Skin cancers can be dangerous if they are not treated before they have a chance to spread. However, existing technology lacks the ability to detect skin cancer accurately. Traditional machine learning algorithms display limitations when classifying different classes of skin cancer. In this research, we have proposed skin cancer detection with classification using CNN and ensemble learning. CNN, known as the Convolutional Neural Network, represents a specialized architecture within deep learning algorithms primarily tailored for image recognition and feature extraction. It comprises convolutional, pooling, and fully connected layers in its mathematical structure. In this work, CNN (Convolutional Neural Network) is employed for image feature extraction, followed by utilizing three distinct classifier algorithms, namely, SVM (Support Vector Machine), KNN (K-Nearest Neighbour), and DT (Decision Tree), integrated through an ensemble approach utilizing a voting system. Despite utilizing multiple classifiers, the model exhibits impressive performance, achieving a classification accuracy of 98%. Notably, our research leverages the HAM10000 dataset for this study. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling Data Flows of a System for Analyzing Messages in Social Networks

Modeling Data Flows of a System for Analyzing Messages in So...

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2022 International Conference "Quality Management, Transport and Information Security, Information Technologies", IT and QM and IS 2022

作者： Bekeneva, Yana A. Lopushansliy, Alexandr M. Kulikov, Alexandr I. Saint Petersburg Electrotechnical University Leti Dept. of Computer Science and Engineering St-Petersburg Russia

ISBN: (数字)9781665488709

ISBN: (纸本)9781665488709

There is a problem associated with an overabundance of information: users write hundreds of posts every day, leave thousands of comments, which makes it difficult to search, structure and analyze useful data. Especially in the case of creating a distributed network, it is also important to calculate the load on the network so that it works smoothly and as quickly as *** paper describes the creation of a system for modeling data flows of a message analysis system in social networks with the ability to change the composition and structure of a distributed network to estimate the amount of transmitted traffic with a different number of processing points for incoming data. © 2022 IEEE.

关键词： Data transfer

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Detecting Retinal Damage From OCT Images

Detecting Retinal Damage From OCT Images

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International Conference on Computing and Networking Technology (ICCNT)

作者： Kundrapu Vineetha Md Jaffer Ali Narisetty Prathima Tripty Singh Prakash Duraisamy Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Computer Science and Engineering University of Wisconsin- Green Bay Wisconsin USA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Optical coherence tomography (OCT) is a crucial imaging modality in ophthalmology, offering detailed retinal images essential for diagnosing various retinal disorders. In this study, we investigate the application of convolutional neural networks (CNNs) and transfer learning to classify retinal OCT images. We compare a custom CNN model with two pretrained models, MobileNet and VGG16, to explore their effectiveness in categorizing retinal diseases. Our study focuses on improving classification accuracy through image enhancement techniques, training methodologies, and model evaluation using confusion matrices. The results highlight the VGG16-based CNN model as the most effective, demonstrating its potential for accurate classification of retinal disorders based on OCT images. Furthermore, we delve into strategies to further enhance the performance of these models, such as data augmentation, finetuning, and hyperparameter tuning. We discuss the importance of these techniques in optimizing model performance and reducing overfitting. Additionally, we emphasize the significance of evaluating models using confusion matrices to gain insights into their performance across different classes. Overall, our findings suggest that CNNs, particularly the VGG16based model, hold promise for improving the automated diagnosis of retinal diseases using OCT imaging, which could lead to better patient outcomes in ophthalmology.

关键词： Training Accuracy Optical coherence tomography Transfer learning Retina Feature extraction Ophthalmology Convolutional neural networks Tuning Diseases

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An Analysis of Security Protocols for Cloud Computing Algorithms in Mobile Ad Hoc Networks 3

An Analysis of Security Protocols for Cloud Computing Algori...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Mohammed, Abdul Sajid Dasari Sreeramulu, Madhusudhan Neravetla, Anuteja Reddy Gupta, Ketan Yuvaraj, N. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States American Express Dept of Cyber Security United States University of the Cumberlands Department of Information Technology KY United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Shakthi Institute of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore641062 India

ISBN: (纸本)9798350384598

Cellular ad Hoc Networks (MANETs) have become increasingly popular in cell computing packages, inclusive of cellular computing, fitness care, automobile, and army packages. As such, safety protocols used in the transmission of records over MANETs are of essential importance for their hit usage. In this paper, we endorse an evaluation of safety protocols for cloud computing algorithms in MANETs. We first discuss the main safety necessities that have to be satisfied. At the same time, a cloud set of rules is adopted in a MANET and then provides a survey of different security protocols that can be used to relax the cloud algorithms for MANETs. We are aware of precise cloud algorithms for MANETs: Opportunistic Routing and Cooperative Caching. We look at the safety of these cloud algorithms under three one-of-a-kind security protocols - SSL/TLS, IPSec, and 2FA - and examine the effect of these protection protocols on their execution. Finally, we summarize our findings and offer guidelines for the adoption of security protocols in MANETs for cloud-based total computing algorithms. © 2024 IEEE.

关键词： Cloud security

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