Computer Research Articles

Analysis of Inpatient Registration Information System on The Timeliness of Inpatient Services at Hamba Regional Hospital

This research aims to evaluate the inpatient registration information system at RSUD Haji Abdoel Madjid Batoe. This research is a descriptive qualitative research with the research respondents being inpatient registration admission officers at RSUD Haji Abdoel Madjid Batoe. This research is a qualitative research using the scrum method. Findings from interviews with admissions officers showed several factors affecting the timeliness of registration, including human resource shortages, delays in procurement of materials, suboptimal procedures, slow computer systems, and limited funding. The analysis resulted in recommendations for improvement, additional staff, improved technological equipment, material and fund management, and consistent implementation of SOPs. In addition, regular training and evaluation for staff is required to ensure that procedures are followed correctly and efficiently.

The Application of Microcomputer Systems in Automatic Control Systems

With the continuous advancement and innovation of computer system technologies, the performance of microcomputers has improved significantly. The enhanced computing power of microcomputer hardware provides greater flexibility for the development of various intelligent optimization algorithms. Integrating microcomputers into automatic control systems effectively addresses the systems demands for signal stability, sensitivity, response speed, and manufacturing cost, thereby significantly advancing the practical application of automatic control systems. This paper comprehensively explores the current state and challenges of applying microcomputers in automatic control systems, while providing an in-depth analysis of development trends in this field. It systematically explains the specific improvements microcomputers bring to control systems and how to select appropriate algorithms based on different types of automatic control systems. Moreover, the review highlights the impact of hardware performance limitations of microcomputers on algorithm implementation and discusses potential solutions and directions for future technological advancements. It also examines successful applications and existing issues in representative automatic control systems employing microcomputers. Finally, the study anticipates the development trends of intelligent optimization algorithms for microcomputers in automatic control systems, as well as the emerging challenges and research directions

What Quantum Can Learn from Classical Computer Engineering

Quantum computing represents a paradigm shift requiring reconceptualization of algorithms, architectures, and software. Although much is new, there is much that quantum computing can learn from traditional classical computer engineering. In this Special Issue, we focus on examples of quantum research inspired by classical computer engineering, ranging from quantum machine learning and image processing to the extension of the “multi-core” concept to quantum computing. Hardware/software co-design is difficult when communities have their unique jargon and disjoint problem domains. For parallel programming on classical computers, an approach for communicating application developer needs emerged around identifying key bottlenecks that limited software. It was difficult to guess features of future applications, but experienced developers knew features of a computer system that would limit software performance. For each feature, a small kernel was developed with the idea that if the kernel ran well, a system would be an effective system for application developers. This set of kernels was called the Parallel Research Kernels . They have been useful to guide design of parallel computers. We hypothesize that an analogous tool for quantum computing, called Quantum Research Kernels , may aid hardware/software co-design of quantum computing systems. In this article, we motivate the Quantum Research Kernels and provide working examples.

Patients' attitudes toward artificial intelligence (AI) in cancer care: A scoping review protocol.

Artificial intelligence broadly refers to computer systems that simulate intelligent behaviour with minimal human intervention. Emphasizing patient-centered care, research has explored patients' perspectives on artificial intelligence in medical care, indicating general acceptance of the technology but also concerns about supervision. However, these views have not been systematically examined from the perspective of patients with cancer, whose opinions may differ given the distinct psychosocial toll of the disease. This protocol describes a scoping review aimed at summarizing the existing literature on the attitudes of patients with cancer toward the use of artificial intelligence in their medical care. The primary goal is to identify knowledge gaps and highlight opportunities for future research. This scoping review protocol will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA-ScR). The electronic databases MEDLINE (OVID), EMBASE, PsycINFO, and CINAHL will be searched for peer-reviewed primary research articles published in academic journals. We will have two independent reviewers screen the articles retrieved from the literature search and select relevant studies based on our inclusion criteria, with a third reviewer resolving any disagreements. We will then compile the data from the included articles into a narrative summary and discuss the implications for clinical practice and future research. To our knowledge, this will be the first scoping review to map the existing literature on the attitudes of patients with cancer regarding artificial intelligence in their medical care.

Unveiling and Evaluating Vulnerabilities in Branch Predictors via a Three-Step Modeling Methodology

With the emergence and proliferation of microarchitectural attacks targeting branch predictors, the once-established security boundary in computer systems and architectures is facing unprecedented challenges. This paper introduces an innovative branch predictor modeling methodology that abstractly characterizes 19 states and 53 operations of branch predictors, aiming to assist hardware designers in addressing overlooked security concerns during the microarchitecture design phase. Building upon this modeling discipline, we develop a symbolic execution-based framework to analyze and derive potential vulnerabilities in branch predictors. This framework finally yields 156 valid three-step attack patterns against branch predictors, including 89 novel variants not discovered in previous work. Subsequently, we extend the framework to automatically generate a benchmark suite for assessing the practical feasibility of derived attacks in real-world scenarios. Evaluation across five commercial Intel processors underscores the substantial threat posed by branch predictor attacks, with 130 of the 156 derived attacks proving viable on at least one processor. Finally, we theoretically model and evaluate 12 secure designs related to branch predictors. The evaluation results demonstrate that existing secure branch predictors can offer better security guarantees than secure speculation schemes, indicating that secure branch predictor designs are promising solutions to maintain the confidentiality and integrity of computer systems.

Optimized Design and Applications of Arithmetic Logic Units: Addressing Power Efficiency and Performance in Diverse Computing Applications

In modern computer systems, the Arithmetic Logic Unit (ALU) is the core component of the central processing Unit (CPU) and performs basic tasks such as arithmetic operations, logic operations, and data transmission. With the development of information technology, the demand for computing is growing, the requirements for computing accuracy, speed and energy consumption are becoming more stringent. The design and optimization of ALU is directly related to the overall performance and energy efficiency of the system, so it becomes the focus of research and the key to technological breakthroughs. This paper focuses on summarizing and exploring the application scenarios, development, and optimization prospects of ALUs. This study reviews various application contexts, including embedded systems, quantum computing, and high-performance processors, highlighting how tailored ALU designs can meet the demands of each. In the field of optimization strategies, Gate Diffusion Input (GDI) technology, reversible logic, and Single Electron Transistor (SET) technology were discussed as a way to reducing power consumption and improving processing speeds.

Understanding the Differences in an AI-Based Creativity Support Tool Between Creativity Types in Fashion Design

As the perspective on creativity shifts to “how it is expressed,” research has aimed to categorize it by problem-solving style. Since harnessing individual creative traits can positively impact creative performance, there has been an emphasis on designing computer systems that are tailored to personal problem-solving behaviors. AI-CST has opened up the potential to facilitate such customization. In this work, we consider two types of creativity—adaptors and innovators—based on problem-solving styles, and investigate AI-CST designs that both types could flexibly use according to the fashion design process. We identified two main AI-CST functions—determining design direction and receiving design inspiration—of the fashion design process, and developed CoCoStyle to map these functions. Through a user study with 30 fashion professionals (15 adaptors and 15 innovators), we found significant differences between the two groups from survey responses, system usage logs, and interviews. Based on the results, we discuss the theoretical and practical implications of AI and AI-CST where creativity is essential.

Real-time human–computer interface based on eye gaze estimation from low-quality webcam images: integration of convolutional neural networks, calibration, and transfer learning

Abstract Eye gaze estimation represents a well-established research domain within computer vision. It has a wide range of practical applications in numerous fields, including human–computer interaction (HCI) for cursor control, health care, and virtual reality, enhancing its suitability for adoption throughout the scientific community. Different methods have been used for eye gaze estimation, such as model based, feature based, and appearance based. The appearance-based method is mainly used because it directly estimates an individual’s gaze direction from images/videos rather than depending on specific features or geometric models. This article developed an appearance-based, real-time generic eye gaze system for HCI to control the cursor through the eye using the convolutional neural network (CNN), calibration, and transfer learning. The study employed low-quality eye images captured from a conventional desktop webcam, enabling the proposed methodology to be implemented on any computer system equipped with a similar web camera without the need for supplementary hardware. Initially, the labeled dataset of both eyes is collected using the webcam. Then, a CNN model is trained by inputting left and right eye images to predict the gaze coordinate as output. We applied the calibration and transfer learning approach to the trained models to make a generic model for new users. In real-time use, the first step is calibration, where the user’s eye images are captured for various screen coordinates, and transfer learning is employed to fine-tune the pre-trained model according to the user’s eyes. Then, the fine-tuned model is used for eye gaze prediction to control the cursor. The system’s performance is evaluated using a test group of multiple users, and it demonstrated an average visual angle accuracy of 2.08 degrees before calibration, which notably improved to 1.81 degrees after the calibration process.

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Data-Driven Technologies for Energy Optimization in Smart Buildings: A Scoping Review

Psychological Analysis and Career Decision-Making of College Students Based on Cognitive Algorithms on Distributed Platforms

This research uses cognitive analytics on a distributed computer system to investigate how students’ decision-making behaviors relate to their personality traits. The goal of the research is to improve our knowledge of the variables affecting students’ professional choices and to offer a solid, expandable framework for individualized career counseling. The investigation examines a range of psychological characteristics, such as personality characteristics, cognitive capacities, passions, and socioeconomic status, by utilizing cognitive algorithms. Massive databases may be processed more easily thanks to the distributed system, which also ensures effective computing and analyses in real time. The approach entails gathering information from a heterogeneous student body, then analyzing it and extracting features to find meaningful trends. Neural networks and combination techniques are examples of sophisticated algorithms for machine learning that are used to create models of prediction that help with career suggestions. The results show how well cognitive computers capture the intricate interactions between variables that influence career choices. The flexibility of the distributed platform guarantees the system’s suitability for big colleges and universities, offering students individualized career counseling on a broad scale. This study offers a fresh method for comprehending and assisting students in their professional choices, which makes a significant contribution to the fields of data mining for education and career planning.

A comprehensive review of computational diagnostic techniques for lymphedema.

Lymphedema is localized swelling due to lymphatic system dysfunction, often affecting arms and legs due to fluid accumulation. It occurs in 20% to 94% of patients within 2 to 5 years after breast cancer treatment, with around 20% of women developing breast cancer-related lymphedema (BCRL). This condition involves the accumulation of protein-rich fluid in interstitial spaces, leading to symptoms like swelling, pain, and reduced mobility that significantly impact quality of life. The early diagnosis of lymphedema helps mitigate the risk of deterioration and prevent its progression to more severe stages. Healthcare providers can reduce risks through exercise prescriptions and self-manual lymphatic drainage techniques. Lymphedema diagnosis currently relies on physical examinations and limb volume measurements, but challenges arise from a lack of standardized criteria and difficulties in detecting early stages. Recent advancements in computational imaging and decision support systems have improved diagnostic accuracy through enhanced image reconstruction and real-time data analysis. The aim of this comprehensive review is to provide an in-depth overview of the research landscape in computational diagnostic techniques for lymphedema. The computational techniques primarily include imaging-based, electrical, and machine learning approaches, which utilize advanced algorithms and data analysis. These modalities were compared based on various parameters to choose the most suitable techniques for their applications. Lymphedema detection faces challenges like subtle symptoms and inconsistent diagnostics. The research identifies Bioimpedance Spectroscopy (BIS), Kinect sensor and Machine Learning integration as the promising modalities for early lymphedema detection. BIS can effectively identify lymphedema as early as four months post-surgery with sensitivity of 44.1% and specificity of 95.4% in diagnosing lymphedema whereas in Machine learning, Artificial Neural Network (ANN) achieved an impressive average cross-validation accuracy of 93.75%, with sensitivity at 95.65% and specificity at 91.03%. Machine learning and imaging can be integrated into clinical practice to enhance diagnostic accuracy and accessibility.

Flo: A Semantic Foundation for Progressive Stream Processing

Streaming systems are present throughout modern applications, processing continuous data in real-time. Existing streaming languages have a variety of semantic models and guarantees that are often incompatible. Yet all these languages are considered "streaming"---what do they have in common? In this paper, we identify two general yet precise semantic properties: streaming progress and eager execution. Together, they ensure that streaming outputs are deterministic and kept fresh with respect to streaming inputs. We formally define these properties in the context of Flo, a parameterized streaming language that abstracts over dataflow operators and the underlying structure of streams. It leverages a lightweight type system to distinguish bounded streams, which allow operators to block on termination, from unbounded ones. Furthermore, Flo provides constructs for dataflow composition and nested graphs with cycles. To demonstrate the generality of our properties, we show how key ideas from representative streaming and incremental computation systems---Flink, LVars, and DBSP---have semantics that can be modeled in Flo and guarantees that map to our properties.

Unifying Compositional Verification and Certified Compilation with a Three-Dimensional Refinement Algebra

Formal verification is a gold standard for building reliable computer systems. Certified systems in particular come with a formal specification, and a proof of correctness which can easily be checked by a third party. Unfortunately, verifying large-scale, heterogeneous systems remains out of reach of current techniques. Addressing this challenge will require the use of compositional methods capable of accommodating and interfacing a range of program verification and certified compilation techniques. In principle, compositional semantics could play a role in enabling this kind of flexibility, but in practice existing tools tend to rely on simple and specialized operational models which are difficult to interface with one another. To tackle this issue, we present a compositional semantics framework which can accommodate a broad range of verification techniques. Its core is a three-dimensional algebra of refinement which operates across program modules, levels of abstraction, and components of the system's state. Our framework is mechanized in the Coq proof assistant and we showcase its capabilities with multiple use cases.

Converso: Chat App with Live Video Sharing and AI Chat Bot

Internet communication is now an integrated part of daily life with lots of communications related applications in computer systems. The proposed mobile communication application, named Converso, is an Android chatting application for real-time text chat with additional features like the live streaming and an artificial intelligence-based chatbot. Java is used in developing the back end of the application while XML has been used for the design aspect of the application. The application makes use of Firebase as the data storage as well as data synchronization solution. In this paper, the author describing the process of devising of Converso and its purpose of offering cost effective rapid communication channel for business promotions and education get Total: The paper focuses on describing the creation of Converso and indicating the main purpose of the creation of the service. Furthermore, it emphasizes the measures taken to safeguard user security within the context of existing trends of mobile communicating technologies.

Enhanced Coyote Optimization Algorithm for Task Scheduling in Computer Systems Using Modified DNN

Enhanced Coyote Optimization Algorithm for Task Scheduling in Computer Systems Using Modified DNN

An efficient smart phone application for wheat crop diseases detection using advanced machine learning.

Globally, agriculture holds significant importance for human food, economic activities, and employment opportunities. Wheat stands out as the most cultivated crop in the farming sector; however, its annual production faces considerable challenges from various diseases. Timely and accurate identification of these wheat plant diseases is crucial to mitigate damage and enhance overall yield. Pakistan stands among the leading crop producers due to favorable weather and rich soil for production. However, traditional agricultural practices persist, and there is insufficient emphasis on leveraging technology. A significant challenge faced by the agriculture sector, particularly in countries like Pakistan, is the untimely and inefficient diagnosis of crop diseases. Existing methods for disease identification often result in inaccuracies and inefficiencies, leading to reduced productivity. This study proposes an efficient application for wheat crop disease diagnosis, adaptable for both mobile devices and computer systems as the primary decision-making engine. The application utilizes sophisticated machine learning techniques, including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, combined with feature extraction methods such as Count Vectorization (CV) and Term Frequency-Inverse Document Frequency (TF-IDF). These advanced methods collectively achieve up to 99% accuracy in diagnosing 14 key wheat diseases, representing a significant improvement over traditional approaches. The application provides a practical decision-making tool for farmers and agricultural experts in Pakistan, offering precise disease diagnostics and management recommendations. By integrating these cutting-edge techniques, the system advances agricultural technology, enhancing disease detection and supporting increased wheat production, thus contributing valuable innovations to both the field of machine learning and agricultural practices.

Optimized design of digital animation based on virtual reality technology

Background: The production of moving images using computer systems is referred to as digital animation, or laptop-generated imagery (LGI). By using virtual landscapes and devices to create immersive settings, virtual reality (VR) improves this experience. The application of virtual reality (VR) technology and optimization algorithms to digital animation character technology is investigated in this study.Aim: The study aims to enhance the shape, motion, and interplay of animated characters using an upgraded Penguin Search Optimization (UPSO) algorithm, while also employing VR for motion design and scene communication.Methods: The research employs motion captures facts, scenario descriptions, and the character models to optimize the shape, actions, and interactions of animation characters. To improve those components, the UPSO set of rules is applied, and the efficiency of the optimized animation in realistic virtual worlds is accessed via VR generation.Results: The adoption of the UPSO algorithm resulted in a significant improvement in the realism of character actions (95%), character quality (92%), and overall product satisfaction (93%). The UPSO algorithm also achieved an accuracy of 97.03%.Conclusion: This study contributes to the advancement of animation by combining optimization algorithms with VR technology, resulting in a more immersive and appealing visual experience for users

DITTO: A Visual Digital Twin for Interventions and Temporal Treatment Outcomes in Head and Neck Cancer.

Digital twin models are of high interest to Head and Neck Cancer (HNC) oncologists, who have to navigate a series of complex treatment decisions that weigh the efficacy of tumor control against toxicity and mortality risks. Evaluating individual risk profiles necessitates a deeper understanding of the interplay between different factors such as patient health, spatial tumor location and spread, and risk of subsequent toxicities that can not be adequately captured through simple heuristics. To support clinicians in better understanding tradeoffs when deciding on treatment courses, we developed DITTO, a digital-twin and visual computing system that allows clinicians to analyze detailed risk profiles for each patient, and decide on a treatment plan. DITTO relies on a sequential Deep Reinforcement Learning digital twin (DT) to deliver personalized risk of both long-term and short-term disease outcome and toxicity risk for HNC patients. Based on a participatory collaborative design alongside oncologists, we also implement several visual explainability methods to promote clinical trust and encourage healthy skepticism when using our system. We evaluate the efficacy of DITTO through quantitative evaluation of performance and case studies with qualitative feedback. Finally, we discuss design lessons for developing clinical visual XAI applications for clinical end users.

Low-energy consuming, optically and electrically stimulated artificial synapse based on lead-free metal halide perovskite (Cs3Cu2I5) for neuromorphic applications

Computational systems inspired by the human brain are being researched to minimize high power consumption and memory instability in traditional computers. Metal halide perovskites, having high light absorption and tunable...

Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems

Real Relative Encoding Genetic Algorithm for Workflow Scheduling in Heterogeneous Distributed Computing Systems