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International Conference on Secure Cyber Computing and Communication (ICSCCC)

作者： Pawan Kumar Patidar Deepak Singh Tomar R. K. Pateriya Yogesh Kumar Sharma Dept. of Computer Science Engineering Maulana Azad National Institute of Technology Bhopal INDIA

Agriculture is the most important source of livelihood. Crop segmentation has become an important role in precision agriculture which helps farmers to make decisions about crop damage and its production. However, it's a challenging task to achieve precision in the agriculture field. Drone Surveillance helps to achieve that crop yield assessment, crop damage, crop health, and other parameters. This paper focuses on image segmentation of crops, classified into categories like sparse and dense crops with the multitemporal data image taken by Drone. This model proposed and studied shows the loss percentage in crop identification by image segmentation process, it helps farmers to get good compensation for crops to survey through Drone (UAV) techniques. A detailed analysis with outcome of thisis explained further.

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A Novel Hybrid Deep Learning Model for Detecting Breast Cancer

TechRxiv

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TechRxiv 2023年

作者： Banerjee, Avishek Chakraborty, Debasis Bhattacharyya, Basanti Nath, Ira Dey, Debashis Garg, Deepak Dept. of Computer Science and Business Systems Asansol Engineering College West Bengal 713305 India Dept. of Computer Science and Engineering Asansol Engineering College Asansol713305 India Dept. of Computer Science and Engineering Pailan College of Management and Technology West Bengal 700139 India Dept. of Computer Science and Engineering JIS College of Engineering West Bengal 741235 India Dept. of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology West Bengal 700064 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

Cancer is a deformity of the body cells that grow out of control and spread to other parts of body. According to the American Cancer Society, early identification of cancer resulted in a 99% chance of survival in the localized stage. In this article, a benchmark cancer dataset is considered for the purpose of investigation. After data preprocessing, we attempt to predict cancer type using our proposed deep learning model named as CNN-Soft-T that combines the feature selection (FS) and classification. FS is introduced using histogram technique for anomaly detection and dimension reduction. We have compared the performance of this model to other cutting-edge machine learning approaches including Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Neural Network (NN) and Decision Tree (DT) in order to demonstrate its utility. We have experimentally exhibited the exceptional empirical effectiveness and biological applicability of our suggested hybrid model for cancer diagnostics and medication development. © 2023, CC BY.

关键词： Decision trees

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Penalty-Enabled Serverless Architecture for Cloud-based Startup Solutions

Penalty-Enabled Serverless Architecture for Cloud-based Star...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Shajulin Benedict Rubiya Subair Tanya Gupta Vedanta S.P. Dept. of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kerala India

The step to the success of startups is through overcoming competitors by adopting software innovations that improve businesses. Serverless computing model, recently, has intrigued a sizable number of startup professionals belonging to various sectors, including financial or IoT-enabled application developers. One of the main flaws is its heavy dependency on cloud providers, which can still result in hefty pricing to startups and stalling functions in applications. This article proposes a penaltyenabled serverless architecture for startups. The architecture can boost the economy of startups and can analyze the serverlessoriented cost-saving options in applications. The penalty-oriented approach could enable cloud architects, developers, and startups, to rethink the utilization of serverless functions; to gleam of with future innovations.

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3D AttU-NET for Brain Tumor Segmentation with a Novel Loss Function

3D AttU-NET for Brain Tumor Segmentation with a Novel Loss F...

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International Conference on Information Systems and computer Networks (ISCON)

作者： Rahul Roy B. Annappa Shubham Dodia Dept. of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

In the United States of America (USA), every year 150,000 patients are registered with a secondary brain tumor that is not generated in the brain. This necessitates the need for early brain tumor detection, which in turn will help patients to live longer. For clinical evaluation and treatment, precise segmentation of brain tumors in MRI images is required. This process can be aided by machine learning and efficient image processing, but manual imaging can be time-consuming. In this study, we aim to develop an 3D automated segmentation algorithm with a novel loss function. A 3D attention UNET CNN model was trained using the novel loss function, which was calculated by taking the weighted average of dice loss and focal loss to overcome the class imbalance. Results show the enhancement in the segmentation performance of attention UNET model with an average increase of 5% in the Dice coefficient for all three classes. However, the model’s performance was not as strong for enhanced and core tumors. Further research may be needed to optimize performance in these areas.

关键词： Solid modeling Image segmentation Three-dimensional displays Machine learning algorithms Magnetic resonance imaging Manuals Brain modeling

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Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts

Towards Adoption of Green Blockchain with Emphasis on Blockc...

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Blockchain and Distributed Systems Security (ICBDS), IEEE International Conference on

作者： Shraddha Phansalkar Deepa Mishra Neha Chaube Rahul Sonkamble Dept. of Computer Science & Engineering MIT Art Design and Technology University Pune Maharashtra

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms

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Advancing Healthcare with GPGPU and Energy Efficient NoC Architectures: A Comprehensive Survey

Advancing Healthcare with GPGPU and Energy Efficient NoC Arc...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Panthagani Vijaya Babu Sonal Yadav Vijay Holimath Dept. of Computer Science & Engineering ViBram Silicon Hub National Institute of Technology Raipur India VividSparks IT Solutions Bangalore India

ISBN: (数字)9798331530259

ISBN: (纸本)9798331530266

General-purpose graphics Processing Units (GPGPU) have emerged as a transformative technology in healthcare and medical fields, harnessing their powerful parallel processing capabilities to handle complex computational challenges. Initially developed for graphics rendering, GPUs are now employed in high-performance tasks such as medical imaging, genomic analysis, drug discovery, and real-time patient monitoring, where vast data volumes and intensive computations are prevalent. GPGPU technology enhances processing efficiency, reduces latency, and supports faster, more accurate diagnostics and treatment planning. Complementing this advancement, Networks-On-Chip (NoC) designs, introduced in the early 2000s, have become a standard communication backbone for high-end CPUs and Systems-on-Chip (SoCs). Their low communication latency, high throughput, and energy efficiency make NoCs ideal for addressing the growing demands of GPU-based systems. However, achieving these performance objectives requires minimizing power dissipation, energy consumption, and costs. This research provides a comprehensive survey of NoC design models for multi-GPU systems, focusing on their role in energy-efficient and scalable architectures. It also highlights the impact of GPGPU in revolutionizing healthcare by meeting modern medical applications' computational and efficiency requirements.

关键词： Surveys Scalability Memory management graphics processing units Medical services Throughput Energy efficiency Real-time systems Computational efficiency Standards

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Custom 8-bit floating point value format for reducing shared memory bank conflict in approximate nearest neighbor search

arXiv

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arXiv 2023年

作者： Ootomo, Hiroyuki Naruse, Akira Dept. of Computer Science Tokyo Institute of Technology Tokyo Japan NVIDIA Tokyo Japan

The k-nearest neighbor search is used in various applications such as machine learning, computer vision, database search, and information retrieval. While the computational cost of the exact nearest neighbor search is enormous, an approximate nearest neighbor search (ANNS) has been attracting much attention. IVFPQ is one of the ANNS methods. Although we can leverage the high bandwidth and low latency of shared memory to compute the search phase of the IVFPQ on NVIDIA GPUs, the throughput can degrade due to shared memory bank conflict. To reduce the bank conflict and improve the search throughput, we propose a custom 8-bit floating point value format. This format doesn’t have a sign bit and can be converted from/to FP32 with a few instructions. We use this format for IVFPQ on GPUs and achieved better performance without significant recall loss compared to FP32 and FP16. Copyright © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Securing Medical Records Using Private Blockchain technology

Securing Medical Records Using Private Blockchain Technology

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Basil Alothman Mariam Alenezi Danah Alhamdan Mariam Almutairat Zahraa Almousa Chibli Joumaa Murad Khan Dept. of Computer Science and Engineering Kuwait College of Science and Technology Kuwait-City Kuwait

The term “electronic medical records” (EMRs) refers to information that is highly confidential and saved electronically. This information is associated with the diagnosis and treatment of a patient, and it must be routinely distributed among the concerned people such as doctors and nurses. The disclosure of private medical information or its alteration during an operation makes it extremely difficult for participants to exchange their medical files with one another. The most effective strategy for overcoming these obstacles is to implement an electronic medical records system that is based on blockchain technology. Within the scope of this study, we have addressed how the technology of blockchain might contribute to improved healthcare data management. Through the application of blockchain technology, the purpose of this research is to establish a connection between the health ministries and departments and both public and private hospitals in order to simplify the process of gaining access to one's medical records and transferring those records while maintaining patient confidentiality and improving safety.

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Multivariate Imputation by N Neighbour Mean and Chained Equation for Time Series Missing Data

Multivariate Imputation by N Neighbour Mean and Chained Equa...

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Industrial Electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Risham Kumar Pansari Muktesh Gupta Rajesh Wadhvani Dept. of Computer Science and Engineering Maulana Azad National Institute of Technology Bhopal India

Missing or incomplete data introduces numerous challenges during data analysis. If there exists any incomplete data then algorithms used for analysis can produce incorrect results. Data with missing or incomplete values may have a negative effect on computation. Thereby leading to an incorrect result. Certain algorithms are incapable of handling missing data properly, while other methods produce an efficient output to calculate the missing values. Because different machine learning algorithms become less effective when given incomplete data, it is extremely important to manage missing data properly. It could be possible that the data set might contain some missing values for various features due to the following reasons data were maintained in the file, data got corrupted, etc. This paper proposed a method that makes use of N neighbors of the data that is missing and makes use of the machine learning model to tune the computed value so as to impute a correct value for the missing data when the missing data is spread across different features. The proposed model makes use of the spatial characteristics of the time series data in order to predict missing data with the least error. Through experimentation, the proposed model demonstrated a high degree of accuracy in predicting missing values, even when the gaps spanned multiple feature columns.

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haitao Yuan Shen Wang Yaofei Ma Jing Bi Jinhong Yang Jia Zhang MengChu Zhou School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization.

关键词： Energy consumption Federated learning Collaboration Simulated annealing Programming Genetics Servers Resource management Particle swarm optimization Optimization

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