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Cloud Storage Performance Improvement Using Deduplication and Compression Techniques 4

Cloud Storage Performance Improvement Using Deduplication an...

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4th International Conference on Smart Systems and Inventive technology, ICSSIT 2022

作者： Kumar, P.M. Ashok Pugazhendhi, E. Nayak, Rudra Kalyan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur District Andhra Pradesh Vaddeswaram522 502 India Dept. of Information Technology Madras Institute of Technology Anna University Tamil Nadu 600044 India School of Computer Science and Engineering VIT Bhopal University Madhya Pradesh Sehore India

ISBN: (纸本)9781665401180

Cloud Computing, an internet based computing is composed of various applications and hardware deployed to the end-user. Existing system deals with block level de-duplication. In this deduplication process, unique chunks of data or byte patterns are identified and stored in the cloud file system. Then the other chunks are compared to the stored copy and whenever a match occurs, the redundant chunk is replaced with a small reference that points to the stored chunk. But in case of block-level deduplication maintenance of large number of blocks is highly difficult. It also requires high processing power when compared to other deduplication techniques. This is the reason why File-level Deduplication comes into picture. Replication is the presence of same file in all servers. This technique increases the efficiency by making the data available at all time. Replicating data avoids single point failure. At the same time it leads to wastage of space. For increasing the cloud storage efficiency, deduplication and compression techniques are adapted. Our approach to deduplication and compression aims at enhancing the efficiency of cloud storage. Deduplication, specialized data compression technique stores only single copy of the redundant data. Detecting the presence of duplicate files and storing it in compressed format in all other servers other than the original server improves the efficiency of cloud storage. In this approach, Deduplication is done at file level. Files present in the compressed format can be uncompressed (unzipped) and downloaded by the user, hence maximizing the storage efficiency and increasing the availability. © 2022 IEEE

关键词： Storage efficiency

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Reinforcement Learning for Unrelated Parallel Machine Scheduling with Release Dates, Setup Times, and Machine Eligibility

Reinforcement Learning for Unrelated Parallel Machine Schedu...

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Simulation Winter Conference

作者： Sang-Hyun Cho Hyun-Jung Kim Lars Mönch Dept. of Industrial and Systems Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Dept. of Mathematics and Computer Science University of Hagen Universitätsstraße 1 Hagen GERMANY

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

This paper presents a novel approach for solving unrelated parallel machine scheduling problems through reinforcement learning. Notably, we consider three main constraints: release date, machine eligibility, and sequence- and machine-dependent setup time to minimize total weighted tardiness. Our work presents a new graph representation for solving the problem and utilizes graph neural networks combined with reinforcement learning. Experimental results show that our proposed method outperforms traditional dispatching rules and an apparent tardiness cost-based algorithm. Furthermore, since we represent and solve the problem using graphs, our method can be used regardless of the number of jobs or machines once trained.

关键词： Metalearning Adaptation models Heuristic algorithms Stochastic processes Reinforcement learning Parallel machines Dynamic scheduling Size measurement Dispatching Graph neural networks

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Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Bounds 14

Revisiting the Complexity Analysis of Conflict-Based Search:...

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14th International Symposium on Combinatorial Search, SoCS 2021

作者： Gordon, Ofir Filmus, Yuval Salzman, Oren Technion–Israel Institute of Technology Computer Science Dept. Haifa Israel

ISBN: (纸本)9781713834557

The problem of Multi-Agent Path Finding (MAPF) calls for finding a set of conflict-free paths for a fleet of agents operating in a given environment. Arguably, the state-of-the-art approach to computing optimal solutions is Conflict-Based Search (CBS). In this work we revisit the complexity analysis of CBS to provide tighter bounds on the algorithm’s run-time in the worst-case. Our analysis paves the way to better pinpoint the parameters that govern (in the worst case) the algorithm’s computational complexity. Our analysis is based on two complementary approaches: In the first approach we bound the run-time using the size of a Multi-valued Decision Diagram (MDD)—a layered graph which compactly contains all possible single-agent paths between two given vertices for a specific path length. In the second approach we express the running time by a novel recurrence relation which bounds the algorithm’s complexity. We use generating functions-based analysis in order to tightly bound the recurrence. Using these technique we provide several new upper-bounds on CBS’s complexity. The results allow us to improve the existing bound on the running time of CBS for many cases. For example, on a set of common benchmarks we improve the upper-bound by a factor of at least 2 Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Electric circuit breakers

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Optimizing Road Extraction in Remote Sensing Imagery: A Lightweight Dilated Inception Deep Residual U-Net with Channel Attention (LDIDRU-CAN) Model and Pruning Strategies

Optimizing Road Extraction in Remote Sensing Imagery: A Ligh...

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International Conference on Computing and Networking technology (ICCNT)

作者： Palvi Sharma Rakesh Kumar Meenu Gupta Ahmed J. Obaid Dept. of Computer Science & Engineering Chandigarh University Punjab India Faculty of Computer Science & Mathematics university of Kufa Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The major challenge in Road Extraction is to extract accurate and complete road network and to get high accuracy while minimizing computational power and ensuring faster prediction. It plays important role in urban planning, disaster management, automatic vehicle navigation and GPS navigation. Several earlier studies used complex deep networks that required large amounts of computational power and large storage. In order to address issues such as high storage requirements, computational load, access to memory delays, and low hardware deployment efficiency this research work presents a novel network model called LDIDRU-CAN. The proposed model is fast, compact, and efficient and requires less storage. The objective of this work, is to compress the original model using structured pruning strategy. This strategy simplified the original model and reduced their computation cost as well as storage requirements. For the reduction of the parameters, we focused on the Row Scale Regular Pruning (RSRP) and Low Learning Rate (LLR) retraining. After applying RSRP pruning approach it reduces storage requirements, parameters, and also contributes to a more than 50% reduction in hardware resource consumption and successfully enhances the parallelism of the model. Testing and validations are performed on the Massachusetts Road Dataset. With only 45 M parameters and 180 MB model size, the suggested network achieves remarkable performance with 87.91% precision, 85.51% recall, 86.69% F1-score and 98.91% accuracy.

关键词： Accuracy Navigation Computational modeling Roads Urban planning Memory management Disaster management Parallel processing Computational efficiency Load modeling

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Indicator for the Water Level Using Bluetooth

Indicator for the Water Level Using Bluetooth

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2021 International Conference on Computing, Communication, Electrical and Biomedical Systems, ICCCEBS 2021

作者： Janet, J. Sureshkumar, S. Ahamed, A Riyaz Kaviyaraj, R. Dept of Computer Science Sri Krishna College of Engineering and Technology Coimbatore India Dept of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

The significance of water rationing has increased over the past few decades. It is hard to estimate level of water in tanks manually. When the water tank is full, the client may not realise the condition that the motor is turned ON which leads to water wastage. To tackle the issues related with the water tank, the water level pointer and regulator framework are utilized. The water level can likewise be tried utilizing a sensor, so the siphon consequently turns ON if the water goes down. The proposed system can even notify clients about the damages in the tank. © Published under licence by IOP Publishing Ltd.

关键词： Water tanks

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On the Feasibility of a Direct Injection Probe with a Capacitively Coupled Return and Integrated Voltage Monitor

On the Feasibility of a Direct Injection Probe with a Capaci...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Aaron Harmon Daniel Szanto Victor Khilkevich Daryl Beetner Dept. of Electrical and Computer Engineering Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

Characterizing the susceptibility of an IC while it is integrated within a system can be challenging. Characterization is even harder if one wants to know the waveform at the target IC pin when injecting a signal on the pin. In this work, the feasibility of a direct injection probe with a capacitively coupled return and integrated voltage monitor is proposed. This probe is advantageous because it does not need to be soldered to the test device and its ability to provide a measurement of the waveform on the target IC pin during the injection. Methods for reconstructing the pin waveform based on probe measurements are discussed. Initial results indicate that the presented probe is generally insensitive to landing position variations and can accurately provide the waveform at the target IC during an injection. Future work is focused on further validation of the presented probe.

关键词： Pins Probes Monitoring

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Real Time Facial Emotion Recognition using Deep Learning and CNN

Real Time Facial Emotion Recognition using Deep Learning and...

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International Conference on computer Communication and Informatics (ICCCI)

作者： S.K. LokeshNaik A. Punitha P. Vijayakarthik Ajmeera Kiran A. Narsimha Dhangar B.Jathin Reddy M. Sudheeksha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Annamalai University TamilNadu India Dept of Computer Science and Engineering R.L.Jalappa Institute of Technology Dodabalapur Bengaluru

In this universe, a person’s mood may be read from their facial expressions. But one thing is for sure: every expression is a way that people communicate their emotions. Despite the fact that every person has a unique set of facial features and a range of emotions, artificial intelligence (AI)technology allows us to recognize these emotions. A face emotion-recognition AI is built using a variety of techniques, one of which is deep learning to build a convolutional neural network (CNN) to train the data set and obtain high prediction accuracy. Recognizing seven of the fundamental facial emotions out of hundreds is the major goal here. The most common facial emotions are those of happiness, sadness, neutrality, anger, surprise, fear, and disgust. There are plenty of ways to achieve this facial emotion recognition like using deep face algorithms, machine learning, by AI techniques, by Tensor flow etc. The accuracy of our model is between 70% to 80%. It is perhaps the simplest and efficient approach of all of them.

关键词： Deep learning computers Training Emotion recognition Tensors Face recognition Neural networks

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A Review on Bioinformatics using DNA Cryptography 8

A Review on Bioinformatics using DNA Cryptography

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8th International Conference on Electronics, Materials engineering and Nano-technology, IEMENTech 2025

作者： Kairi, Animesh Bhadra, Tapas Karmakar, Samit Verma, Aman Saha, Sonia Gupta, Soumili Chowdhury, Souraja Aliah University Computer Science Kolkata India University of Engineering & Management Dept. of Electronics & Communication Engineering Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798331520762

DNA-based encryption with bioinformatics has emerged as a promising field for secure data storage and transfer. This study analyzes the bioinformatics aspects of DNA cryptography, focusing on important advancements and challenges. We look at techniques like DNA origami, enzymatic reactions, and DNA strand displacement that use DNA sequences to encode and decode data. Additionally discussed are the security implications of DNA-based cryptography, including privacy issues, data integrity issues, and the potential for DNA-based attacks. Finally, we outline some possible directions for further review, such as developing more robust error-correction systems, looking at cutting-edge DNA-based encryption methods using bioinformatics, and addressing the ethical and societal implications of this emerging technology. © 2025 IEEE.

关键词： Bioinformatics

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The Current State of Software engineering Employing Methods Derived from Artificial Intelligence and Outstanding Challenges 1

The Current State of Software Engineering Employing Methods ...

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1st IEEE International Conference on Innovations in High Speed Communication and Signal Processing, IHCSP 2023

作者： Kumar, Rajesh Naveen, V. Kumar Illa, Pavan Pachar, Sunita Patil, Pratap Chandigarh University CSE Department Chandigarh India Madanapalle Institute of Technology & Science Department of Computer Science and Technology AP Madanapalle India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Department of Information Technology Hyderabad India GLA University IBM Department Mathura India Amity University in Tashkent Dept of IT and Engineering Uzbekistan

ISBN: (纸本)9798350345957

Although AI has become a buzzword for self-organizing IT applications;its significance to software engineering has not been rigorously examined. To evaluate the current state of advancement, the potential for future development and the risks of AI application to software engineering, this study combines a systematic review of previous research in the field with five qualitative interviews with software developers who use or want to use AI tools in their daily work routines. Research organises findings about the software improvement life sequence. Results show that AI's greatest achievements and potential lie in three areas: a) the systematic evaluation of these data in neural networks;b) the structured analysis of big data pools to discover patterns and novel information clusters;and c) the automated automation of lengthy routine jobs in software development and testing using algorithms, such as for debugging and documentation. As a result, AI shortens the development cycle, reduces expenses, and increases productivity. AI has thus far been dependent on human-made structures and has primarily been reproductive;nonetheless, the automation of software engineering jobs has a major advantage: the creative capacity of human developers is multiplied when using AI tools effectively. © 2023 IEEE.

关键词： Software design

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Deep learning based DeepFake video detection

Deep learning based DeepFake video detection

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Guefrachi, Sarra Ben Jabra, Marwa Alsharabi, Naif A. Ben Othman, Mohamed Tahar Alharabi, Yasser O. Alkholidi, Abdulsalam Hammam, Habib Université de Moncton Faculty of Engineering MonctonNBE1A3E9 Canada Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia Computer Engineering University of Ha'il Computer Science Amran University Yemen Ha'il55476 Saudi Arabia College of Computer Qassim University Department of Computer Science Buraydah51452 Saudi Arabia College of Computer Sc. & Engneering Ha'il University Computer Science and Information Ha'il Saudi Arabia Canadian Institute of Technology College Department of Software Engineering Tirana Albania International Institute of Technology and Management Commune d'Akanda BP Libreville1989 Gabon School of Electrical Engineering University of Johannes-burg Dept of Electrical and Electronic Eng. Science Tirana Albania

ISBN: (纸本)9798350347050

Recent advances in DeepFake face-swapping technology have made it simple to create fake videos that appear remarkably real. Since it has been employed in numerous instances for deceit, extortion, and the falsification of facts, its widespread use has generated a huge social, security, and political risk. Its use on websites and social media has become more widespread. Detecting this crime is becoming more and more important due to the potential harm false videos may inflict on a global scale. This research offers a method for building a deep learning model that really can tell the difference between authentic and false videos. The article describes how to create new models based on the VGG16 neural network, a previously created neural network that does image categorization, using transfer learning in the computer vision field. Deep learning is still becoming better at both producing and spotting DeepFakes. DeepFake detection algorithms are developed using dated public datasets, and as a result, they may become obsolete with time. and require continual updating. The research findings are encouraging, and our results reached an accuracy rate of over 90%. © 2023 IEEE.

关键词： Crime

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