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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Hossain, Mohammad Alamgir Amin, Sherif Tawfik Eishah, Hassan Abu Hamdan, Alfadil Ahmed Metwally, Abdelnasser Abdelwakil Limkar, Suresh Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Computer Science College of Engineering and Computer Science Jazan University Jazan Saudi Arabia Department of Artificial Intelligence and Data Science AISSMS Institute of Information Technology Maharashtra Pune India

ISBN: (纸本)9789819765805

Cardiovascular diseases (CVD) are a prominent contributor to illness and death on a global scale, underscoring the need for precise predictive models to facilitate timely intervention. The present study investigates the utilization of deep learning methodologies, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM), in the context of predictive modeling of cardiovascular diseases. This study examines the efficacy of three well-known optimization techniques, namely Adam Optimization, RMSprop, and Stochastic Gradient Descent (SGD), within the framework of these neural network architectures. Among the various models based on Convolutional Neural Networks (CNNs), Stochastic Gradient Descent (SGD) has been identified as the optimizer that produces the most favorable outcomes for predicting CVD. The utilization of this optimization technique demonstrated exceptional efficacy in the training of the deep neural network, resulting in superior levels of accuracy, sensitivity, and specificity. On the other hand, it was observed that LSTM-based models exhibited the greatest improvement when utilizing RMSprop optimization. The utilization of RMSprop has been found to have a positive impact on the effectiveness of sequence modeling, resulting in enhanced predictive capabilities for assessing the risk of cardiovascular disease. The efficacy of this technique was demonstrated in its ability to capture temporal dependencies within the dataset, consequently enhancing the predictive capability of the model. The results of this study emphasize the importance of carefully choosing neural network architectures and optimization techniques when constructing predictive models for cardiovascular disease. Customizing the selection of neural network architecture and optimization algorithm according to the unique attributes of the dataset can substantially augment the precision and dependability of CVD risk evaluations. This, in turn, can ultimately lead t

关键词： Risk assessment

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If CLIP Could Talk: Understanding Vision-Language Model Representations Through Their Preferred Concept Descriptions

If CLIP Could Talk: Understanding Vision-Language Model Repr...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Esfandiarpoor, Reza Menghini, Cristina Bach, Stephen H. Department of Computer Science Brown University United States Data Science Institute Brown University United States

ISBN: (纸本)9798891761643

Recent works often assume that Vision-Language Model (VLM) representations are based on visual attributes like shape. However, it is unclear to what extent VLMs prioritize this information to represent concepts. We propose Extract and Explore (EX2), a novel approach to characterize textual features that are important for VLMs. EX2 uses reinforcement learning to align a large language model with VLM preferences and generates descriptions that incorporate features that are important for the VLM. Then, we inspect the descriptions to identify features that contribute to VLM representations. Using EX2, we find that spurious descriptions have a major role in VLM representations despite providing no helpful information, e.g., Click to enlarge photo of CONCEPT. More importantly, among informative descriptions, VLMs rely significantly on non-visual attributes like habitat (e.g., North America) to represent visual concepts. Also, our analysis reveals that different VLMs prioritize different attributes in their representations. Overall, we show that VLMs do not simply match images to scene descriptions and that non-visual or even spurious descriptions significantly influence their representations. Code: https://***/BatsResearch/ex2. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Review on Blockchain Technology in Health Care Systems 6th

A Review on Blockchain Technology in Health Care Systems

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6th International Conference on Communications and Cyber-Physical Engineering, ICCCE 2023

作者： Rajendar, Janga Kishore Kumar, Botcha Verma, Toran Lavanya, Mangalampalli Sasha Sai Lakshmi Department of Computer Science Engineering - Data Science CMR Engineering College Kandlakoya Telangana Hyderabad501401 India

ISBN: (纸本)9789819971367

Blockchain is a distributed database that multiple parties can maintain and share. This new technology is expected to greatly impact the healthcare industry. It can help address various issues related to patient care. Through the use of blockchain technology, data can be moved in real-time without exposing it to various risks such as theft and forgery. This paper aims to introduce the general idea of the Blockchain and its applications in the healthcare industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blockchain

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Fitness Sharing Chaotic Particle Swarm Optimization (FSCPSO): A Metaheuristic Approach for Allocating Dynamic Virtual Machine (VM) in Fog Computing Architecture

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computers, Materials & Continua 2024年第8期80卷 2557-2578页

作者： Prasanna Kumar Kannughatta Ranganna Siddesh Gaddadevara Matt Chin-Ling Chen Ananda Babu Jayachandra Yong-Yuan Deng Department of Computer Science&Engineering Ramaiah Institute of TechnologyBangalore560054India Department of Artificial Intelligence&Data Science Ramaiah Institute of TechnologyBangalore560054India School of Information Engineering Changchun Sci-Tech UniversityChangchun130600China Department of Computer Science and Information Engineering Chaoyang University of TechnologyTaichung41349Taiwan Department of Information Science and Engineering Malnad College of EngineeringHassan573202India

In recent decades,fog computing has played a vital role in executing parallel computational tasks,specifically,scientific workflow *** cloud data centers,fog computing takes more time to run workflow ***,it is essential to develop effective models for Virtual Machine(VM)allocation and task scheduling in fog computing *** task scheduling,VM migration,and allocation,altogether optimize the use of computational resources across different fog *** process ensures that the tasks are executed with minimal energy consumption,which reduces the chances of resource *** this manuscript,the proposed framework comprises two phases:(i)effective task scheduling using a fractional selectivity approach and(ii)VM allocation by proposing an algorithm by the name of Fitness Sharing Chaotic Particle Swarm Optimization(FSCPSO).The proposed FSCPSO algorithm integrates the concepts of chaos theory and fitness sharing that effectively balance both global exploration and local *** balance enables the use of a wide range of solutions that leads to minimal total cost and makespan,in comparison to other traditional optimization *** FSCPSO algorithm’s performance is analyzed using six evaluation measures namely,Load Balancing Level(LBL),Average Resource Utilization(ARU),total cost,makespan,energy consumption,and response *** relation to the conventional optimization algorithms,the FSCPSO algorithm achieves a higher LBL of 39.12%,ARU of 58.15%,a minimal total cost of 1175,and a makespan of 85.87 ms,particularly when evaluated for 50 tasks.

关键词： Fog computing fractional selectivity approach particle swarm optimization algorithm task scheduling virtual machine allocation

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Comparison of the SOLA model and ARIMA for Cloud Computing Resources Allocations 4

Comparison of the SOLA model and ARIMA for Cloud Computing R...

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4th International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2024

作者： Prince Sekwatlakwatla, Sello Malele, Vusumuzi North-West University Unit for Data Science and Computing Department of Computer Science and Information Systems South Africa

ISBN: (纸本)9798350387988

Cloud computing solutions are becoming more and more popular as a way for organizations to improve productivity, save costs, and simplify procedures. The advantage of cloud services is that they enable users to store data remotely and access on-demand applications and services from a shared pool of configurable computing resources. Cloud computing resource allocation faces challenges in load balancing, effective resource management, and compliance with regulatory and legal requirements, requiring complex tools and techniques for optimal use. Most organizations are moving to cloud services and encouraging their clients to access services online. However, effective resource allocation is critical for improving performance and lowering costs in this area. Due to unpredictable network traffic in cloud computing, resource allocation is challenging, which causes customers to complain about application timeouts, delayed system times, and higher bandwidth use during peak hours. This entails allocating resources to various users and programs, such as memory, processing power, storage, and network bandwidth. In this regard, this study compares the ensemble method, which is stepwise Gaussian Linear Autoregressive (SGLA) and the individual method, which is autoregressive integrated moving average (ARIMA). The results show SGLA prediction accuracy increased with an average of 97.9%, and ARIMA prediction showed an accuracy of 71.5%. In this regard the ensemble method performed better than individual methods using the same datasets. The study recommends the ensemble method for the prediction and allocation of resources in cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Distributionally Robust data Valuation 41

Distributionally Robust Data Valuation

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41st International Conference on Machine Learning, ICML 2024

作者： Lin, Xiaoqiang Xu, Xinyi Wu, Zhaoxuan Ng, See-Kiong Low, Bryan Kian Hsiang Department of Computer Science National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

data valuation quantifies the contribution of each data point to the performance of a machine learning model. Existing works typically define the value of data by its improvement of the validation performance of the trained model. However, this approach can be impractical to apply in collaborative machine learning and data marketplace since it is difficult for the parties/buyers to agree on a common validation dataset or determine the exact validation distribution a priori. To address this, we propose a distributionally robust data valuation approach to perform data valuation without known/fixed validation distributions. Our approach defines the value of data by its improvement of the distributionally robust generalization error (DRGE), thus providing a worst-case performance guarantee without a known/fixed validation distribution. However, since computing DRGE directly is infeasible, we propose using model deviation as a proxy for the marginal improvement of DRGE (for kernel regression and neural networks) to compute data values. Furthermore, we identify a notion of uniqueness where low uniqueness characterizes low-value data. We empirically demonstrate that our approach outperforms existing data valuation approaches in data selection and data removal tasks on real-world datasets (e.g., housing price prediction, diabetes hospitalization prediction). Copyright 2024 by the author(s)

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CyberGuardian: An Interactive Assistant for Cybersecurity Specialists Using Large Language Models

CyberGuardian: An Interactive Assistant for Cybersecurity Sp...

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19th International Conference on Software Technologies, ICSOFT 2024

作者： Paduraru, Ciprian Patilea, Catalina Camelia Stefanescu, Alin Department of Computer Science University of Bucharest Romania Institute for Logic and Data Science Romania

ISBN: (纸本)9789897587061

Cybersecurity plays an important role in protecting people and critical infrastructure. Sectors such as energy, defense and healthcare are increasingly at risk from cyber threats. To address these challenges, dedicated Security Operations Centers (SOCs) continuously monitor threats and respond to critical issues. Our research focuses on the use of Large Language Models (LLMs) to optimize the tasks of SOCs and to support security professionals. In this work, we propose a framework, which we call CyberGuardian, whose main goal is to provide a chatbot application along with a set of tools to support SOC analysts in real-time cybersecurity tasks. We use state-of-the-art LLM techniques and start from a Llama 2 model, then fine-tune the base model using a new dataset containing mainly cybersecurity topics. The CyberGuardian framework has a plugin architecture that integrates processes such as Retrieval Augmented Generation (RAG), safeguard methods for interaction between human user and chatbot, integration with tools to manage tasks such as database interactions, code generation and execution, and plotting graphs just by specifying the task in a natural language. The work, along with the dataset we collected and reusable code to update or customize, is made available to the cybersecurity community as open source. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Critical infrastructures

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Promo-Zen: A Natural Language Processing Based Promotional Text Message Management System 6

Promo-Zen: A Natural Language Processing Based Promotional T...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Udugampola, Chamath Weerasingha, Nethmi NSBM Green University Department of Computer and Data Science Homagama Sri Lanka

ISBN: (纸本)9798331517878

Promotional Short Message Service (SMS) messages, which provide frequent updates about deals and discounts to consumers, are crucial in developing countries like Sri Lanka, as they help alleviate financial pressure during economic crises. However, poor practices by the brands, SMS technology limitations and human memory limitations challenge the ability of consumers to make effective use of this facility. As a solution, this project aimed to develop an Android application to organize and enhance the readability of promotional messages, while also providing mechanisms to prevent users from forgetting promotions. These objectives were achieved by creating a Kotlin app that displays promotional information in a date-sorted weekly dashboard, offers deal-expiration reminders, and optimizes message content for readability. The system utilizes a novel workflow/pipeline combining a Natural Language Processing (NLP) model with Named Entity Recognition (NER) capabilities, Regular Expressions and custom logic, to understand the context of and extract the crucial information from the promotional messages. User-acceptance testing carried out demonstrates positive results, reducing traditional access times to promotions of 8-10 seconds to approximately 2 seconds through this application. Additionally, user-testers reported multiple occurrences of the reminders feature ensuring that promotions weren't forgotten before use. © 2024 IEEE.

关键词： Acceptance tests

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Plant Fettle Detector: Unifying Fruit Quality Tracking and Plant Disease Detection through Advanced Image Analysis 3

Plant Fettle Detector: Unifying Fruit Quality Tracking and P...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Nair, Sruthi Bajpai, Khushi Fadnavis, Sakshi Jain, Shruti Sheikh, Zeenat Shri Ramdeobaba College of Engineering and Management Department of Computer Science and Engineering - Data Science Nagpur44013 India

ISBN: (纸本)9798350381931

India being an agricultural country, food quality tracking is a major challenge faced by common farmers across the country. This research presents an innovative integration of Convolutional Neural Networks (CNNs) to address key challenges in agriculture - fruit quality tracking and plant disease detection. The unified system comprises the Fruit Quality Tracker and the Plant Disease Detector, leveraging advanced image analysis for precision agriculture. The Fruit Quality Tracker uses computer vision to assess ripeness and defects in real-time, empowering farmers with informed decisions. Simultaneously, the Plant Disease Detector swiftly identifies and classifies diseases, mitigating economic losses. The research explores architectural nuances, development methodologies, and deployment on Google Cloud Platform. Testing shows high accuracy rates of 98.5% for fruit quality and 98.83% for plant disease detection. The system's transformative potential in agriculture is highlighted, bridging the gap between technology and sustainable food production systems. © 2024 IEEE.

关键词： Image analysis

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Risk Identification of data science Projects: A Literature Review

Risk Identification of Data Science Projects: A Literature R...

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2023 Learning. Knowledge. data. Analytics, LWDA 2023

作者： Holtkemper, Maike Potanin, Maria Oberst, Alexander Beecks, Christian University of Hagen Department of Mathematics and Computer Science Chair of Data Science Germany

While cost, time, and resources are considered to have a high impact on data science projects, risks are the key for the successful implementation of a project. The correct handling of risks has been proven to increase a project’s chances of success. Therefore, awareness of existing and emerging risks as well as their assessment play a major role in data science project management. In 2021, 87 percent of data science projects fail and are thus not implemented successfully. The path of successful implementation of data science projects in companies is more complex and uncertain than for conventional projects, in particular the identification and availability of the necessary skills in a company before and during the project. Regarding software engineering projects, the analysis and evaluation of potential risks is well-known, but for data science projects potential risks have not yet been analyzed at a larger scale. To identify the potential reasons for failure of data science projects, an in-depth understanding of potential risks and measures for mitigation these risks is inevitable. In this paper, we conduct a systematic literature review on risks of data science projects and present the top fifteen risks as first findings. Furthermore, we compare the identified risks to the major risks of software engineering projects to highlight similarities and differences between these two disciplines. The findings of our literature review can be used in guiding the development of future risk assessment systems for complex data science projects. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Risk assessment

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