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Minimization of data centers in survivable dynamic SDM-EONs

Minimization of data centers in survivable dynamic SDM-EONs

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International Conference on Computing and Networking Technology (ICCNT)

作者： Sourabh Chandra Khokan Mondal Dept. of Computer Science & Engineering Calcutta Institute of Technology Howrah India Dept. of Computer Science & Engineering Techno India University Kolkata India

Energy consumption due to massive use of data center (DC) is increasing rapidly. The need to improve the energy efficiency of such DC aims to develop an effective DC-placement algorithm. Again, a sudden disaster in the network can affect both fiber path as well as the DC nodes resulting in fiber cuts and huge data loss. Space division multiplexing-based elastic optical network (SDM-EON), being more efficient than the traditional elastic optical network (EON) due to the inclusion of multiple cores in a single fiber link, tries to maximize the utilization of resources by reducing blocking probability. This paper presents a novel DC-placement algorithm in dynamic DC-based SDM-EONs which focuses on enhancing energy efficiency in the network, thereby ensuring the survivability of the network against any sudden disaster. Rigorous simulation analysis proves the efficacy of the proposed algorithm over other existing algorithms.

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Architectural Design for AdhaarAssist — A Forward-Looking Lock-Free STM Solution for Adhaar

Architectural Design for AdhaarAssist — A Forward-Looking L...

引用

Signal Processing and Advance Research in Computing (SPARC), International Conference on

作者： Sana Jafar Dept. of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

Any parallel program is judged based on its efficiency in doing multiple tasks at the same time without getting stuck due to an underlying problem. The main purpose of a parallel program is to effectively and quickly solve a large and complicated problem at hand. This is why; parallel programs form the basis of many powerful supercomputers where parallel computing is supported through special hardware which is complemented with parallel software achieving hundreds of petaflop/s of performance. The biggest challenge for a parallel programmer is to build a program that has a minimum tradeoff for increased parallelism and good concurrency control among the parallel tasks. One of the biggest applications of parallelism is the huge, ever-increasing database of Adhaar biometric. Its crucial to have a robust, fast and efficient way of accessing and manipulating this data so that along with faster results, consistency of the database is also maintained. This paper proposes a schema AdhaarAssist which is a lock-free STM based algorithm that will work on a shared in-memory data structure. Some of its features are increased concurrency by lazy conflict detection, partial aborts that optimize the algorithm for maximum throughput when complex and lengthy transactions are employed, memory barriers and timestamping. The algorithm proposed in this paper will open the way for leveraging it for distributed-shared in-memory databases like the one used by Adhaar.

关键词： Multicore processing Signal processing algorithms Parallel processing system recovery Signal processing Data structures Throughput Supercomputers Software Testing

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Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning Techniques

Advancements in Breast Cancer Detection: A Comprehensive Rev...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Drishti Arora Shruti Gupta Jashekam Singh Chawla Rakesh Garg Dept. of Computer Science and Engineering Amity University Uttar Pradesh India Dept. of Computer Science and Engineering Amity University Uttar Pradesh India

This paper addresses the pressing issue of breast cancer-related mortality by comprehensively analyzing six distinct deep learning models utilized in the past decade. With a focus on timely and precise diagnosis, the study evaluates the performance metrics of each model across diverse datasets, image modalities, and methodologies. The primary goal is to identify the most efficient and precise deep learning architecture tailored to classify breast cancer tumors, with potential implications for optimizing early detection strategies and advancing clinical decision-making processes. The findings hold significant implications for refining clinical decision-making processes, potentially leading to more efficacy in timely detection strategies. This research emphasizes on the broader field of image analysis and its role in combating breast cancer.

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Design and Optimization of High-Performance Health Monitoring systems Using Advanced Sensor Networks

Design and Optimization of High-Performance Health Monitorin...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Heena Mehta Mukesh Singla Dept. of Computer Science and Engineering Baba Mastnath University Rohtak Haryana India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

This research elucidates the development and optimization of advanced sensor network-based health monitoring systems. In response to the increasing need for accurate health data in real-time, advanced gadgets capable of continuously monitoring essential indicators have been created. Contemporary sensor technologies, such as biosensors, IoT devices, and WSN s, enable these systems to facilitate data collection and processing with little disturbance. Optimization techniques like PSO, including machine learning algorithms and energy conservation procedures, are used to enhance the system's performance, reliability, and battery longevity. The study focuses on developing robust and scalable systems suitable for various healthcare applications, while also investigating data processing, communication protocols, network architecture, and sensor selection. Advanced sensor networks will be crucial for future health monitoring, as research indicates substantial improvements in monitoring accuracy, system efficiency, and patient outcomes. Present approach is supposed to provide scalable and efficient solution. Moreover, it would support privacy in order to provide secure solution.

关键词： Wireless sensor networks Accuracy Protocols system performance Medical services Network architecture Real-time systems Biosensors Monitoring Optimization

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Identification of Cancer Biomarkers for Multi-class Diagnostics through Network Analysis of RNAseq Data of Tumor-Educated Platelets

Identification of Cancer Biomarkers for Multi-class Diagnost...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Toccacieli, Ali Petti, Manuela Sapienza University of Rome MSc Program in Engineering in Computer Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9781665468190

Tumor-educated platelets (TEPs) are circulating blood cells with a distinct tumor-driven phenotype and act as carriers and protectors of metastases. To date, some studies have shown that the TEPs transcriptome can be used for cancer diagnostics. The objective of this study is to propose a procedure based on differential gene expression and differential gene co-expression analyses to identify a set of key genes for multi-class cancer diagnostics. To reach this aim, we analyzed RNA-seq data (57736 genes) of 130 subjects, of whom 40 patients with glioblastoma multiforme (GBM), 35 patients with pancreatic adenocarcinoma (PAAD), and 55 healthy donors (HC). We focused our analysis on the subset of differentially expressed genes (DEGs), and we used these genes to build and analyze the differential co-expression networks, identifying the hub genes. With this procedure, we obtained a restricted set of DEGs that maximize the accuracy in classifying patients according to their conditions (GBM, PAAD, or HC). Indeed, we validated our results by comparing the achieved classification accuracy with that resulting from random selections of DEGs and we obtained that genes selected by differential co-expression (DCE) network analysis have greater predictive power than any other set of differentially expressed genes, including using all of them. © 2022 IEEE.

关键词： Platelets

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Enacting Rural Empowerment: Implementing Cooperative Commerce in Communities using Machine Learning

Enacting Rural Empowerment: Implementing Cooperative Commerc...

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Networking and Communications (ICNWC), International Conference on

作者： Skandarsini R R Sangavi M S Ravitla Harika Varun Cm Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

In order to revitalize rural economies through the empowerment of self-help organizations and the promotion of economic growth, this study offers a revolutionary cooperative commerce platform. The platform integrates cutting-edge features such as digital ledger and inventory management for sellers, demand forecasting, and dynamic pricing, thereby fostering optimized production and efficient marketplaces. Central to the platform's efficacy is the implementation of user segmentation, which categorizes users based on income and spending behaviors, enabling targeted marketing strategies and continuous application improvements to enhance user engagement and profitability. Leveraging machine learning algorithms, personalized user experiences are cultivated, elevating buyer satisfaction and overall engagement. Additionally, administrative tools provide valuable insights for informed decision-making. Preliminary results demonstrate improved financial management for self-help groups, heightened economic activities, and an enriched user-centric experience. Identified challenges and proposed future enhancements underscore the platform's commitment to adaptability and responsiveness to the evolving needs of rural communities, ensuring sustained relevance and impact.

关键词： Profitability Decision making Demand forecasting Pricing Machine learning Production Organizations

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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1_score of 97%, 96% and 98% respectively.

关键词： Support vector machines Sensitivity Simulation Epilepsy Signal processing algorithms Transforms Signal processing Electroencephalography Discrete wavelet transforms Prognostics and health management

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“Vulnerabilities in Smart Contracts: A Detailed Survey of Detection and Mitigation Methodologies”

“Vulnerabilities in Smart Contracts: A Detailed Survey of D...

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Emerging Technologies in computer science for Interdisciplinary Applications (ICETCS), International Conference on

作者： Nayantara K Kumar Niranjan V Honnungar M Sharwari Prakash J J Lohith Dept. of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (数字)9798350372502

ISBN: (纸本)9798350372519

In the ever-changing world of blockchain technology, the emergence of smart contracts has completely transformed the way agreements are executed, offering the potential for automation and trust in decentralized systems. Despite their built-in security features, smart contracts still face persistent vulnerabilities, resulting in significant financial losses. While existing studies often approach smart contract security from specific angles, such as development cycles or vulnerability detection tools, this paper adopts a comprehensive, multidimensional perspective. It delves into the intricacies of smart contract security by examining vulnerability detection mechanisms and defense strategies. The exploration begins by conducting a detailed analysis of the current security challenges and issues surrounding smart contracts. It then delves into established frameworks for classifying vulnerabilities and common security flaws. The paper examines existing methods for detecting, and repairing contract vulnerabilities, evaluating their effectiveness. Additionally, it provides a comprehensive overview of the existing body of knowledge in smart contract security-related research. Through this systematic examination, the paper aims to serve as a valuable reference and provide a comprehensive understanding of the multifaceted landscape of smart contract security.

关键词： Surveys systematics Generative AI Smart contracts Ecosystems Static analysis Maintenance engineering

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A Wireless Intrusion Detection system using Feature Selection with Random Forest

A Wireless Intrusion Detection System using Feature Selectio...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Abhijit Sharad Warhekar Deepak D. Kshirsagar Dept. of Computer Science and Engineering COEP Technological University Pune Maharashtra India

ISBN: (数字)9798350391770

ISBN: (纸本)9798350391787

Wireless networks have become integral to modern communication infrastructure, facilitating seamless connectivity in various domains. However, their presence everywhere also makes them vulnerable to various attacks. Those can cause failure in network services. Indeed, a robust Wireless Intrusion Detection system (WIDS) is essential for identifying and mitigating attacks on wireless networks. The key objective of WIDS is to detect wireless traffic and classify it as an attack or normal. In this situation, machine learning (ML) algorithms are used to detect attacks or intrusions in wireless systems. However, these algorithms need to accurately detect attacks early. Feature selection (FS) techniques are used to reduce this detection time and increase accuracy. These allow our system to identify attacks against local nodes. The proposed system is implemented, experimented and tested on AWID3 dataset. The performance of the system is determined by using a random forest (RF) algorithm and FS selection techniques. The experimentation conducted on AWID3 shows that the proposed methodology achieved an accuracy of 99.9990% for the binary model using 143 significant features and an accuracy of 99.9985% for multilabel classification using 144 significant features

关键词： Radio frequency Accuracy Machine learning algorithms Wireless networks Intrusion detection Telecommunication traffic Feature extraction Communication system security Security Random forests

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Decentralized Finance and Cross-Chain Interoperable Automated Market Maker - Using BlockChain

Decentralized Finance and Cross-Chain Interoperable Automate...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： Sarumathi S Apoorv Raj Saxena Yash Raj Saxena Mehreen S Md Sana Saritha Verma Sneha Roy Dept. of Computer Science Engineering HKBK College of Engineering Bangalore India ClayStack Bangalore India

Decentralized Finance (DeFi) represents an alternative paradigm in financial infrastructure, operating a top the Ethereum Blockchain. DeFi leverages Automated Market Makers (AMMs) to facilitate the exchange of coins/tokens within Liquidity Pools (LPs). However, the current framework limits transactions to a single chain, resulting in prolonged processing times for cross-chain transactions. The principal drawbacks of existing AMMs include protracted transaction confirmation periods and constraints in single-chain functionality. This paper introduces Mixta, a web-based multi-chain interoperable AMM, aiming to address these limitations. Mixta empowers users to seamlessly exchange assets across different chains, ultimately reducing transaction confirmation times.

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