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Jumpstarting and Evolving a Software Quality Management Initiative: From Pre-Web to DevOps 16

Jumpstarting and Evolving a Software Quality Management Init...

引用

16th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2024

作者： Cusick, James J. Ritsumeikan University Dept. of Electronic and Computer Engineering Shiga Kusatsu Japan

ISBN: (纸本)9798350377903

Management of technology development requires adequate measurements. This paper discusses a broad effort to define, deploy, and adopt software quality measures, especially software reliability in an industrial setting. The industrial projects described here began before the emergence of the Web and continue to the current day. The approach and results of these quality metrics support the technical management of a variety of large-scale software platforms, their operations, and the improvement of quality across numerous iterations of development. Additionally, a comparison of classical customer oriented and dynamic software metrics with those commonly applied within Agile and DevOps environments is discussed in the context of the evolution of technology management and measurements. These metrics include Software Reliability, Test Effectiveness, and Code Coverage. Management decisioning using these metrics is traced through several real-world industrial projects across differing companies. Conclusions are linked to current trends in managing Software Development such as ITIL, Agile methods, and DevOps. © 2024 IEEE.

关键词： Software testing

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Unraveling Patterns in Healthcare Fraud through Comprehensive Analysis 18

Unraveling Patterns in Healthcare Fraud through Comprehensiv...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chirchi, Khushi E. Kavya, B. Dayananda Sagar University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9789380544519

This study addresses healthcare provider fraud in Medicare, employing advanced machine learning models on a diverse dataset to predict potential fraud. The goal is to contribute insights for effective fraud detection and mitigate its impact on overall healthcare costs. Utilizing Logistic Regression, Random Forest, and addressing class imbalance through SMOTE, we discern patterns in provider behavior. Evaluation metrics, including confusion matrices, accuracy, sensitivity, specificity, Kappa values, AUC, and F1-scores, comprehensively assess each model's performance. Findings highlight distinct behavior patterns and underscore SMOTE's effectiveness in mitigating class imbalance challenges. Comparative analysis discusses algorithm strengths and weaknesses, offering insights for real-world implementation, impacting fraud prevention strategies for insurance companies, healthcare providers, and beneficiaries. In summary, this research makes a noteworthy contribution to the detection of healthcare fraud, offering insights for effective strategies. The incorporation of SMOTE enhances model robustness, leading to improved fraud detection capabilities and, consequently, reducing the impact of fraud on healthcare costs. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： ANN AUC GNN Kappa values Logistic Regression ML NB Random Forest SMOTE SVM

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Bio-Inspired Tree Seed Algorithm Enhanced Discrete Wavelet Technique for Memory Control 4

Bio-Inspired Tree Seed Algorithm Enhanced Discrete Wavelet T...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： More, Sachin D Ingle, Dayanand Bharati Vidyapeeth College of Engineering University of Mumbai Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798331523923

The widely recognized classical image compression techniques are renowned for their excellent performance, minimal computational complexity, and the ability to generate an embedded bitstream that can be decoded at varying bit rates, resulting in progressively improved image quality as more bits are received. It requires a significant amount of computer memory as it stores the generated sets and the pixel locations in linked lists. The multiresolution feature of transform does not result in the creation of a resolution-scalable bitstream that would allow the image to be decoded at various resolutions. The enormous memory issue with the conventional methods is resolved by the Tree Seed Algorithm (TSA) an Enhanced Discrete Wavelet Transform methodology which requires just one list of fixed size with average of 2.25 bits/pixel and size of 1/4 of the image. The primary contribution of this study lies in enhancing image quality and resolution thereby optimizing memory usage. The TSA Optimized Discrete Wavelet Transform approach is recommended in this study to decrease complexity and improve processing speed. Using the unique attributes of the algorithm, it is particularly well-suited to meet the diverse skill levels and resolution preferences of contemporary Internet users. © 2025 IEEE.

关键词： Decoding

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Advancing Medical Diagnostics: Performance Analysis of YOLO Algorithms for Automated Platelet Detection

Advancing Medical Diagnostics: Performance Analysis of YOLO ...

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2024 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2024

作者： Dubey, Soumya Jhang, Kyoungson Chungnam National University Dept. of Computer Engineering Daejeon Korea Republic of

ISBN: (纸本)9798331530839

The significance of health automation, particularly post-COVID-19, emphasizes the critical role of advanced diagnostic technologies. Conventional methods for platelet detection in blood-smear images are often slow and inconsistent, impacting medical response efficiency. This study explores the application of the YOLO (You Only Look Once) algorithm, specifically versions 3, 5, 7, and 8, to improve platelet detection accuracy and efficiency. Through meticulous data preparation and fine-tuning on a curated dataset, we compared the performance of these YOLO versions in terms of accuracy, precision, recall, and computational efficiency. YOLOv5 emerged as the most accurate and reliable model, achieving the highest mean Average Precision (mAP) and superior precision and recall metrics, despite slightly slower inference times compared to YOLOv8. YOLOv8, with the fastest inference times and excellent detection capabilities, is also highly effective for real-time applications. The findings underscore the advancements and tradeoffs among different YOLO versions, with YOLOv5 and YOLOv8 showing significant potential for enhancing automated platelet detection in clinical diagnostics. © 2024 IEEE.

关键词： COVID-19

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Machine Learning based Malware Detection in Android: A Practical Approach 3

Machine Learning based Malware Detection in Android: A Pract...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Tyagi, Toshi Gautam, Madhu Kiet Group of Institutions Dept. of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350384598

Today's society heavily depends on online communities, which host a wide variety of social networks and public and private services. These demands led to the creation of Android, the world's most popular mobile operating system. The wide range of features and capabilities offered by freely installable third-party apps is largely responsible for this success. While focusing on the positive aspects of technology use, it is vital to keep in mind the potential negative consequences like privacy breaches, data theft, and malware infections that could compromise personal information and device security, especially if negligence or overuse of technology is present. This article discusses a systematic review of ML-based Android malware detection techniques. It provides a critical analysis of a number of carefully selected articles, highlighting their methods and conclusions. To support this study, we also present our own analysis and findings, for which an evaluation of diverse ML models with 123,453 applications and 5,560 malware samples from the DREBIN dataset was done to classify apps as malicious and benign. Preprocessing was done on the dataset to address missing values and perform label encoding. To cope with the issue of class imbalance, we used SMOTE an efficient ML technique that handles imbalance issues efficiently. We have applied five different ML algorithms, The results show that among all the techniques, Decision Tree (DT), Support Vector Machines (SVM) with various kernels (linear, polynomial, RBF), Sequential Neural Network (NN), K-Nearest Neighbours (KNN), and Logistic Regression (LR), the sequential neural network model outperformed other methods with 99% accuracy. Additionally, the process identifies areas for future research and improvement in ML-based Android malware detection techniques. © 2024 IEEE.

关键词： Android malware

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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State of the Art and Innovations needed for Direct Radiating Arrays to place Very High Throughput Satellite Communications into Technology Reach 5

State of the Art and Innovations needed for Direct Radiating...

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5th IEEE International Conference on Communications, Information, Electronic and Energy systems, CIEES 2024

作者： Kaifas, Theodoros N. F. Democritus University of Thrace Dept. of Electrical and Computer Engineering Xanthi Greece

ISBN: (纸本)9798350352863

The current work provides the roadmap towards the advent of Very High Throughput Satellite Communications. The key enabler will be the envisioned and presented in detail placement of the Direct Radiating Arrays vision into the reach of the technology. Such an endeavor entails the proper choice of the multi-element electrically large array's geometry as well as the choice on the element radiator. Those choices should support and implement the overlapping concept in hardware. At the same time, they should leave room for advanced array synthesis techniques to unfold their full potential. Those issues are treated in detail, and proper emphasis is given both on the state of the art as well as to the innovations needed to increase the current relative technological readiness level. © 2024 IEEE.

关键词： Satellite communication systems

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Direction-Selective Parallel Module Structure for Cascaded Bridge and Modular Multilevel Converters with Minimum Transistor Count 50

Direction-Selective Parallel Module Structure for Cascaded B...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Zhang, Jinshui Goetz, Stefan M. Dept. Electrical & Computer Engineering Duke University Durham United States

ISBN: (纸本)9781665464543

Modular multilevel converters have developed into a door opener for a variety of energy applications. Enabling parallel connectivity between modules can offer several advantages to systems, including sensorless balancing, module-to-module energy exchange, reduced impedance, low-frequency operation down to dc output, and a larger effective capacitance. Existing module circuits, however, require many individual switches or offer only very limited parallel functionalities. We propose a new module topology, which we name the direction-selective parallel structure, which requires only a minimum of transistors. A combination of only four active switches and four diodes enables bidirectional equilibration, bipolar module output, and inter-module switched-capacitor features. The topology is highly attractive for existing converters working with cascaded H-bridge elements as the addition of only four diodes enables the key features of more complicated module topologies, particularly their superior balancing, such as sensorless estimator-free balancing, and module-to-module energy exchange. The diodes furthermore well match the insulated gate bipolar transistors implemented in most larger systems. We elaborate on the working principle of the direction-selective parallel module and demonstrate its operation. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Harmonizing the Musical Metaverse: unveiling needs, tools, and challenges from experts’ point of view

Harmonizing the Musical Metaverse: unveiling needs, tools, a...

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International Conference on New Interfaces for Musical Expression, NIME 2024

作者： Boem, Alberto Tomasetti, Matteo Turchet, Luca Dept. of Information Engineering and Computer Science University of Trento Trento Italy

The Musical Metaverse (MM) represents an innovative frontier for the field of New Interfaces for Musical Expression (NIME). The MM holds the potential to redefine areas such as musical composition and performance via immersive environments based on technologically mediated social interactions. Despite substantial research on single-user immersive systems, the intersection of NIME and the MM remains largely unexplored. In this paper, we systematically explore this domain by examining previous and current approaches, alongside conducting interviews with eleven experts who have created multi-user immersive musical environments and authored publications on this topic. The goal is to map such an uncharted territory by collecting valuable insights and leveraging the perspective of experts to provide an understanding of the potentials and challenges inherent in creating immersive social environments for musical activities. Our results reveal that existing multi-user immersive environments make use of diverse implementation approaches but face challenges due to the absence of standardized technology stacks, particularly in networking and data synchronization. © 2024, International Conference on New Interfaces for Musical Expression. All rights reserved.

关键词： Music

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A High Throughput Bloom Filter Based TCAM Architecture for Fast NIDS

A High Throughput Bloom Filter Based TCAM Architecture for F...

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2024 International Conference on Smart Electronics and Communication systems, ISENSE 2024

作者： Shaik, Sameera Noor Mahammad, S.K. IIITDM Kancheepuram Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331528126

One of the computationally most intensive tasks of an Network Intrusion Detection System (NIDS) is string/pattern matching where the payload of incoming packet is compared against predefined rule-set in order to detect any intrusion or threat. Although, various software algorithms and hardware architectures have been proposed to accelerate the performance of NIDS, wire-speed matching of these patterns still remains as a challenge. TCAMs are popularly used in many networking applications as they can perform the pattern matching in single clock cycle, but they always suffer with high power dissipation and delay due to their complex circuitry. Hence, most of the research focuses on efficient utilization of TCAM to cope up with high-speed links. In this paper, a high-throughput TCAM based architecture employed with Bloom filters is proposed. When a Bloom filter is deployed with TCAM, the number of patterns to be matched on TCAM reduces significantly since most of the patterns are eliminated in the prefiltering stage. There exist various Bloom filters such as Standard Bloom filter [1], Cuckoo filters [2], Counting Bloom filters [3] and Compressed filters [4]. Our proposed variant of Bloom filter outperforms all the existing filters in terms of speed while using less memory. We performed experiments on widely used open-source Snort NIDS and the proposed approach is able to achieve a throughput of 128Gbps for version 3.0c and 77.5Gbps for version 2.9.17. © 2024 IEEE.

关键词： Open source software

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