International Journal of Computer (IJC) <p>The <a title="International Journal of Computer (IJC) home page" href="" target="_blank" rel="noopener"><strong>International Journal of Computer (IJC)</strong></a> is an open access International Journal for scientists and researchers to publish their scientific papers in Computer Science related fields. <a title="International Journal of Computer (IJC)" href="" target="_blank" rel="noopener">IJC</a> plays its role as a refereed international journal to publish research results conducted by researchers.</p> <p>This journal accepts scientific papers for publication after passing the journal's double peer review process (within 4 weeks). For detailed information about the journal kindly check <a title="About the Journal" href="">About the Journal</a> page. </p> <p>All <a title="International Journal of Computer (IJC)" href="" target="_blank" rel="noopener">IJC</a> published papers in Computer Science will be available for scientific readers for free; no fees are required to download published papers in this international journal.</p> <p> </p> en-US <p style="text-align: justify;">Authors who submit papers with this journal agree to the <a title="Copyright Notice" href="" target="_blank" rel="noopener">following terms</a>.&nbsp;</p> (Prof. Feras Fares) (Technical Support) Fri, 05 Jan 2024 07:34:34 +0000 OJS 60 Analytics in SAP S/4 HANA of SD/MM/LE: A New Technology That is Faster and More Reliable <p>Operational analytics is all about answering business questions while doing business and supporting business users across the organization, from shop floor users to management and executives. Therefore, business transactions and analytics must co-exist together in a single platform to empower business users to drive insights, make decisions, and complete business processes in a single application and using a single source of facts without toggling between multiple applications. Traditionally transactional systems and analytics were maintained separately to improve throughput of the transactional system and that certainly introduced latency in decision making. However, with innovation in the SAP HANA platform, SAP S/4HANA embedded analytics enables business users, business analysts, and management to perform real-time analytics on live transactional data. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics. This paper reviews technical architecture and key components of SAP S/4HANA embedded analytics.</p> Narayan Mahajan Copyright (c) 2024 Narayan Mahajan Sun, 08 Jan 2023 00:00:00 +0000 Enhancing Wireless Charging Systems through Dynamic Power Management with the Innovative Power Control Algorithm <p>Abstract— The Innovative Power Control Algorithm (IPCA) represents a significant theoretical advancement in the domain of wireless charging, addressing the inefficiencies and rigidity of traditional static power management systems. Rooted in dynamic power management principles, IPCA leverages real-time data analytics and adaptive feedback mechanisms to optimize power delivery, ensuring efficiency and adaptability across varying operational conditions. This paper delineates the theoretical framework of IPCA, elucidating its algorithmic structure, mathematical modeling, and simulated performance outcomes. Through comprehensive simulations, IPCA demonstrates a potential increase in charging efficiency and adaptability when compared to conventional methods. The theoretical implications of IPCA extend to diverse application scenarios, including consumer electronics, electric vehicles, and industrial automation, promising significant enhancements in wireless charging systems. Despite its theoretical nature, this research lays a robust groundwork for future empirical studies, aiming to validate and realize the practical deployment of IPCA in real- world wireless charging systems.</p> Aarav Mittal, Richard Huang Copyright (c) 2024 Aarav Mittal, Richard Huang Mon, 26 Feb 2024 00:00:00 +0000 Hybrid Software Classifier of Internet Applications <p>Traffic classification refers to the processes used to categorize traffic based on features in the traffic and in accordance with specific classification objectives. In general, there are numerous forests, fields, and aims in the classification of internet traffic. Internet service providers must be aware of the types of traffic that are sent across their network Classifying Internet applications that are carried by computer networks is the specific goal, create a hybrid software classifier that can be applied to the classification of Internet traffic. Internet service providers (ISPs) attempt to boost bandwidth as a result of rising Internet traffic, but at the same time, more bandwidth is needed for Internet applications. Problems arise from the exponential growth of new internet applications using unregistered ports. The new programs may also contain a lot of viruses and dangerous code. In recent years, traffic classification has drawn more and more attention. With the help of direct and passive observation of the individual packets or stream of packets moving over the network, it seeks to provide the capability of automatically identifying the program that created a specific stream of packets. Data mining (DM) is a method for sifting through enormous databases in search of fresh, obscured, and practical information patterns. The knowledge discovery process includes the DM idea. This study employs a variety of techniques and functions as a hybrid classification. The foundation of many essential network monitoring and controlling jobs, such as billing, quality of service, security, and trend analyzers, is the classification of networks flows by their application type. Identification of Internet traffic is a crucial tool for network management. It enables operators to more accurately forecast upcoming traffic patterns and demand, and it enables security staff to spot unusual conduct.</p> Fatima Aljwari, Hamza Ibrahim, Faraah ALnashri, Aisha ALkamisi, Reem ALkhaldi Copyright (c) 2024 Fatima Aljwari, Hamza Ibrahim, Faraah ALnashri, Aisha ALkamisi, Reem ALkhaldi Mon, 26 Feb 2024 00:00:00 +0000 The Mega Healthcare Data Breaches in the United States (2009 – 2023): A Comparative Document Analysis <p>This paper presents a comprehensive analysis of the predominant healthcare data breaches in the United States from October 2009 to September 2023, utilizing a mixed-methods approach centered on seven publicly available breach reports. It aims to identify patterns, common factors, and measures to enhance cybersecurity within the sector. Through comparative document analysis, the study examines the nature, causes, and repercussions of these breaches, recognizing external attacks, internal errors, and software vulnerabilities as critical weaknesses. The consequences range from financial and reputational damage to erosion of patient trust. The findings stress the necessity for improved preventive strategies, bolstering of security practices, employee training, vendor oversight, and effective incident response mechanisms. The paper also offers insights into the legal and ethical implications of breaches. It suggests robust cybersecurity measures, including the adoption of emerging technologies like blockchain and AI/ML to deter threats. The recommendations guide healthcare organizations toward establishing robust protections for sensitive health data, ensuring regulatory compliance, and facilitating continuity of trust and care. The paper serves as a call to action for ongoing study into the multidimensional impact of data compromises in healthcare. </p> Abiola Adedeji Adebanjo Copyright (c) 2024 Abiola Adedeji Adebanjo Sat, 27 Jan 2024 00:00:00 +0000 Utilizing NLP Sentiment Analysis Approach to Categorize Amazon Reviews against an Extended Testing Set <p>Sentiment analysis, also known as opinion mining, is a pivotal aspect of natural language processing (NLP). This method entails discerning the polarity of textual information and determining whether it conveys positive or negative sentiments. In one of the domains, e-commerce, sentiment analysis assumes paramount significance. It offers businesses a nuanced understanding of their brand and product sentiment as reflected in customer reviews, facilitating market comprehension and strategic decision-making. This study primarily focused on analyzing the Amazon food reviews dataset, augmenting the original dataset with newly generated data, and subsequently conducting data preprocessing tasks, encompassing text cleansing, removing stop words, lemmatization, and stemming. Subsequently, machine learning models were constructed, trained, and evaluated using NLP feature extraction techniques to address the sentiment analysis challenge and investigate the impact of increased data volume on model performance. Among the diverse methodologies employed for extracting features from textual data samples, this research integrated term frequency-inverse document frequency (TF-IDF), Word to Vector (W2V), and Bag of Words (BoW) techniques in the feature extraction phase. Furthermore, three distinct machine learning models, namely Logistic Regression, Decision Tree, and Random Forest, were designed, implemented, and assessed. The models' performance was scrutinized following hyperparameter optimization to determine the most effective approach. The outcomes revealed that the performance of the models was consistent, yielding accuracy rates ranging from 85% to 89% on the testing dataset. Nevertheless, the Logistic Regression model, employing BoW features, demonstrated superior performance compared to the other models. Following optimization of the logistic regression model, a remarkable accuracy of 89% was attained on the testing dataset by operating the BoW extracted features.</p> Arman Sarraf Copyright (c) 2024 Arman Sarraf Fri, 05 Apr 2024 00:00:00 +0000 Wearable Sensors for Posture and Movement in Patient Handling: A Scoping Review <p>Nurses experience work-related musculoskeletal disorders (WMSDs) such as lower back pain due to awkward postures or movements during patient handling. Monitoring and education for patient handling are necessary to prevent these WMSDs. Recently, measurement methods for patient handling using wearable sensors have been developed to implement these interventions at various sites. However, the status of these measurement methods has not been comprehensively summarized. The purpose of this study is to summarize the status of measurement methods for patient handling using wearable sensors. Peer-reviewed papers published between January 2013 and November 2023 that included measurements of patient handling using wearable sensors were selected from Google Scholar. Measured patient handlings, postures, and movements were summarized. The type, number, and placement of sensors were also investigated. Furthermore, the applied data processing techniques were also summarized. Inertial sensors and insole pressure sensors were applied for measurement methods. Current methods can measure trunk angle, arm movement, and foot placement during several motions such as patient transfer. In addition, load and correctness of patient handling motion are recognized by a wearable sensor-based system using machine learning techniques. These results indicate that current methods can provide effective kinematic values during patient handling to prevent WMSDs. On the other hand, there were also limitations due to number of sensors. Future studies should develop simpler measurement methods using fewer sensors.</p> Kodai Kitagawa Copyright (c) 2024 Kodai Kitagawa Tue, 20 Feb 2024 00:00:00 +0000 Application of Text Summarization on Text-Based Generative Adversarial Networks <p><strong> </strong>In this project, we wish to convert long textual inputs into summarised text chunks and generate images describing the summarized text. This project aims to cultivate a model that can generate true-to-life images from summarized textual input using GAN. GANs aim to estimate and recreate the possible spread of real-world data samples and produce new pictures based on this distribution. This project offers an automated summarised text-to-image synthesis for creating images from written descriptions. The written descriptions serve as the GAN generator's conditional intake. The first step in this synthesis is the use of Natural Language Processing to bring out keywords for summarizing. BART transformers are employed. This is then fed to the GAN network consisting of a generator and discriminator. This project used a pre-trained DALL-E mini model as the GAN architecture.</p> Muhammad Alli-Balogun Copyright (c) 2024 Muhammad Alli-Balogun Sat, 27 Jan 2024 00:00:00 +0000