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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Salam Al-E'mari Yousef Sanjalawe Budoor Allehyani Information Security Department Faculty of Information Technology University of Petra (UoP) Amman Jordan Information Technology Department King Abdullah II School for Information Technology The University of Jordan (UJ) Amman Jordan Software Engineering Department College of Computing Umm Al-Qura University (UQU) Makkah Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Managing resources efficiently is critical in modern cloud computing environments due to the growing complexity and increasing demand for cloud services. Load balancing, a key aspect of cloud resource management, ensures optimal distribution of workloads across servers to improve system performance, minimize response times, and increase resource utilization. However, many existing load-balancing algorithms suffer from challenges such as premature convergence, high computational complexity, and inefficiency in handling dynamic and heterogeneous workloads. To address these challenges, this research proposes a Hybrid Bio-Inspired Approach that combines the Salp Swarm Algorithm (SSA) and Elephant Herding Optimization (EHO). The motivation behind this research is to develop a scalable and adaptable load-balancing mechanism capable of improving performance and handling fluctuating workloads effectively. The main objective of this research is to enhance cloud load balancing by leveraging the unique strengths of SSA and EHO, aiming for better exploration and exploitation balance, improved resource utilization, and minimized response times. The proposed hybrid approach integrates SSA's efficient search capabilities with EHO's natural clustering and resource optimization features, creating a robust framework for load balancing in cloud systems. The methodology is implemented using the CloudSim simulator, a widely used platform for modeling and simulating cloud environments. The performance of the proposed hybrid algorithm is evaluated against traditional and other bio-inspired techniques. Results from the simulations demonstrate that the hybrid SSA-EHO approach outperforms existing algorithms in terms of load distribution, response time, scalability, and resource utilization, highlighting its potential as an efficient solution for cloud resource management.

关键词： Cloud computing Energy consumption Heuristic algorithms Scalability Clustering algorithms Load management Resource management Time factors Optimization Load modeling

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Towards Efficient Workflow Scheduling Over Yarn Cluster Using Deep Reinforcement Learning

Towards Efficient Workflow Scheduling Over Yarn Cluster Usin...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jianguo Xue Ting Wang Puyu Cai MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China Department of Computer Science and Engineering Michigan State University USA

Hadoop Yarn is an open-source cluster manager responsible for resource management and job scheduling. However, data-driven applications are typically organized into workflows that consist of a series of jobs with dependencies. Yarn does not manage users' workflows and only considers the current job rather than the entire workflow when scheduling. In practice, multiple workflows share the same Yarn cluster and are pre-assigned separate Yarn resource queues to avoid mutual interference. However, this coarse-grained resource division can sometimes result in low resource utilization and increased pending time of jobs on the Yarn queue. For instance, one resource queue may have exhausted its quota while still having pending jobs, while other queues may have available resources but cannot begin executing any jobs due to unfulfilled data dependencies. To address this problem, we propose a deep reinforcement learning-based workflow scheduling scheme that takes into account job dependencies, job priorities, and dynamic resource usage. The proposed approach can intelligently identify and utilize free windows of different resource queues. Our simulation results demonstrate that the proposed DRL-based workflow scheduling scheme can significantly reduce the average job latency compared to existing approaches.

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Modelling IoT Application Requirements for Benchmarking IoT Middleware Platforms 2021

Modelling IoT Application Requirements for Benchmarking IoT ...

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23rd International Conference on Information Integration and Web Intelligence, iiWAS 2021

作者： Mondal, Shalmoly Hassani, Alireza Jayaraman, Prem Prakash Haghighi, Pari Delir Georgakopoulos, Dimitrios Faculty of Science Engineering and Technology Swinburne University of Technology Australia School of Info Technology Deakin University Australia Department of Computer Science and Software Engineering Swinburne University of Technology Australia Department of Human Centred Computing Monash University Australia

ISBN: (纸本)9781450395564

The significant advances in the Internet of Things (IoT) have led to IoT applications being widely used in various scenarios ranging from smart city, smart farming, to Industrial IoT (IIoT) solutions. With the explosion of IoT application development, IoT middleware platforms are increasingly being used for hosting such IoT applications. This has given rise to the need for developing benchmarking solutions to analyze and test the performance of different middleware platforms that host these IoT applications. To develop such benchmarks, there are a number of key components that are needed. One of these components is an IoT dataset. To generate such datasets, representing IoT application requirements in a general and formal way is important. In this paper, we propose a framework to model the IoT Applications Requirements and enable Data Generation(ARDG-IoT). The framework supports a formal way to capture IoT application requirements and use these requirements to generate IoT data that can be used to create benchmarks for different IoT middleware platforms. ARDG-IoT consists of our proposed model, IoTSySML, which captures the application requirements, and an IoT data simulator tool, which is used to generate IoT data. We present an evaluation of the framework using a real world Industrial IoT application case study. © 2021 ACM.

关键词： Benchmarking

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Analysis of Web Content Quality Information on the Koseeker Website Using the Web Content Audit Method and ParseHub Tools 8

Analysis of Web Content Quality Information on the Koseeker ...

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8th International Conference on Information and Communication Technology, ICoICT 2020

作者： Abiantoro, Deandra Kusumo, Dana Sulistyo School of Computing Telkom University Department of Informatics Bandung Indonesia Department of Software Engineering School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9781728161426

ISBN: (纸本)9781728161426

Content Quality is one of the most critical dimensions of Website Quality. There are seven indicators to check the quality of the content. Content that has low quality can be determined with these indicators. To facilitate quality checking of the website content, the authors used a content audit method. However, there are currently not many standards that can be used for content auditing. So in this study, the authors proposed to use the website auditing framework and combine it with content quality indicators. With these methods, the authors can assess content by following the indicators of content quality. Complete information about the website was needed to conduct an audit. The authors used web scraping using Parsehub to get all these data. In this research, the case taken for quality checking was Koseeker Website. After conducting the content audit in Koseeker, the results showed that Koseeker has a quality that was not yet maximum on three of seven indicators, namely, Timely, Relevant, and Authority. After doing the test, it can be seen that the proposed web content audit can assess the quality of Koseeker website content. © 2020 IEEE.

关键词： Websites

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Exploring Deep Learning and Grad-CAM for Speech-Based Detection of Mild Traumatic Brain Injury

Exploring Deep Learning and Grad-CAM for Speech-Based Detect...

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IEEE International Conference on Big Data

作者： Fredy Rojas Samaneh Madanian John Michael Templeton Christian Poellabauer Sandra L. Schneider Department of Computer Science and Software Engineering Auckland University of Technology (AUT) Auckland New Zealand Department of Computer Science and Engineering University of South Florida Tampa FL USA School of Computing and Information Sciences Florida International University Miami FL USA Department of Communicative Sciences & Disorders Saint Mary’s College Notre Dame IN USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Mild traumatic brain injury (mTBI) is challenging to diagnose due to its subtle and transient symptoms, making noninvasive diagnostic tools crucial for early detection. This study explores the use of a custom ResNet deep learning model combined with the Grad-CAM interpretability technique for mTBI detection via speech analysis. Speech data were transformed into Mel-spectrograms and fed into the model for binary classification between concussed and control individuals. The Grad-CAM method provided insights into which frequency regions of the Mel-spectrogram were most important for the model's predictions, with higher-frequency regions identified as significant for the model in detecting mTBI. Using Monte Carlo Cross-Validation (MCCV), we evaluated 50 different subject train-test split configurations to gain insights into the model's performance stability and variability. This analysis can assess the model's ability to learn consistent patterns within the dataset and suggest potential generalization tendencies. The variability observed in the performance metrics distribution underscores the importance of robust evaluation methods, particularly when working with small datasets. The combination of deep learning, robust evaluation technique and interpretability in this study contributes to the development of clinically viable, speech-based tools for mTBI detection, with potential applications in sports and healthcare settings.

关键词： Deep learning Analytical models Medical services Predictive models Brain modeling Data models Stability analysis Transient analysis Brain injuries Sports

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Application of Explicit Group Sparse Projection in Optimizing 5G Network Resource Allocation and Topology Design

Application of Explicit Group Sparse Projection in Optimizin...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Kodanda Rami Reddy Manukonda Hemanth Swamy R. Sankar Chidambaranathan C. M. Haribabu.k Saranya. Department of Consulting IBM Senior Software Engineer Motorola Solutions Department of EEE Chennai Institute of Technology Chennai Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE MLR Institute of Technology Hyderabad Department of AI & DS Dhanalakshmi College of Engineering Chennai

ISBN: (数字)9798350354218

ISBN: (纸本)9798350354225

The growing adoption of 5G networks in diverse domains like IoT, smart cities, healthcare, and industrial automation necessitates advanced optimization techniques. While existing approaches have shown promise, they are often limited by computational intensity, lack of adaptability, and specific focus on geometric constraints. This paper presents EGSP-5G, a novel methodology employing Explicit Group Sparse Projection (EGSP) for 5G network resource allocation and topology design. Its capability to achieve targeted sparsity levels allows for efficient utilization of network resources, thereby making it a versatile and powerful tool for various optimization tasks. Remarkably, EGSP-5G achieved a resource utilization rate of 90.02% and a throughput of 110.32 in networks comprising 1000 nodes. It stands as a robust and versatile solution for optimizing 5G networks across diverse application domains.

关键词： Technological innovation Automation 5G mobile communication Network topology Smart cities Medical services Throughput Topology Resource management Optimization

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Neural Networks and Deep Learning: Innovations and Applications in Soft computing

Neural Networks and Deep Learning: Innovations and Applicati...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Jahagirdar Imran Abdulkadar Chinnem Rama Mohan P. Vijaya Bharati Shamik Palit Gunda Srinivasa Rao Pankaj Mudholkar Computer Application Savitribai Phule Pune University Department of Computer Science and Engineering Narayana Engineering College Nellore Andhra Pradesh India Department of CSE Vignans Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of Computing Science and Software Engineering University of Stirling RAK Campus UAE School of Management and Commerce Mallareddy University Kompally Faculty of Computer Applications Marwadi University Rajkot Gujarat India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing.

关键词： Deep learning Technological innovation Data privacy Privacy Natural languages Process control Real-time systems Informatics Biological neural networks Robots

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Analysis of the Generator and Consistency of General Web Page Layout Structure Using Matching Algorithm Based on Set Difference 8

Analysis of the Generator and Consistency of General Web Pag...

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8th International Conference on Information and Communication Technology, ICoICT 2020

作者： Ilhamiati, Agniya Noor Kusumo, Dana Sulistyo Sardi, Indra Lukmana Telkom University Department of Informatics School of Computing Bandung Indonesia Telkom University Department of Software Engineering School of Computing Bandung Indonesia

ISBN: (纸本)9781728161426

According to the website quality of the organization dimension and website usability, the consistency of the layout of the web page is how the general layout elements format is common to all pages. Consistency can make it easier for users in terms of navigation. In this study, researchers used a case study on the East Kalimantan High Prosecutors' website (http://***/). The inequality location of the layout makes inconsistency. In this research, web page layout structure data was collected using a Chrome Extension called HTML Tree Generator by adding additional functions according to semantic elements, so that automatically downloads HTML elements in the Document Object Model (DOM). Then, the general layout structure generator only took the semantic elements from the most common layout as its input. Matching Algorithm used to solve the problem of inconsistency in general layout structure, by looking for differences of structure using set difference. Combination functions are used to determine the comparison of each page. Set difference produce 'false' if there are one or more differences. Research on the consistency of web page layout structure from case studies yields a consistency score of 92.1%. This score indicates an inconsistency in the web page layout. However, after fixed inconsistent pages, the recalculation score can produce a consistent web page layout. © 2020 IEEE.

关键词： Websites

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B2A, A Straight-Through Approach from Basics to Advanced Level: Case Study for Python Course 11

B2A, A Straight-Through Approach from Basics to Advanced Lev...

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11th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2024

作者： Shazmeen, Abbasi Dakić, Pavle Zoltan, Alwahab Dhulfiqar Department of Programming Languages and Compilers Faculty of Informatics Eötvös Loránd University 1/C Pázmány Péter st. BudapestH-1117 Hungary Faculty of Informatics and Computing Singidunum University Danijelova 32 Belgrade Serbia Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 Slovakia

In this study, we present a straightforward teaching strategy for teaching the Python programming language. Our method emphasizes a clear progression from fundamental to sophisticated concepts. Beginning with a thorough explanation of core Python ideas, we show a beginner-level script that dissects each line of code and displays the results. One distinguishing feature of our system is that students use ChatGPT to optimize code, which encourages them to improve the core program with fewer lines. After getting the updated solutions, students carefully compare the original and optimized programs to discover improvements and potential flaws. This study underlines the effectiveness of the Straight-Through Approach, which focuses on improving Python understanding while also developing critical thinking and code optimization skills. Our findings show that this strategy is a simple and effective way for teachers to assist students in growing from basic to advanced Python abilities, resulting in a dynamic and successful learning environment. The proposed technique will teach students how to use AI-driven modules correctly, such as ChatGPT. © 2024 Copyright for this paper by its authors.

关键词： Python

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An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

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Computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

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