Combines Computer Science Research Articles

The future of medicine: an outline attempt using state-of-the-art business and scientific trends.

Currently, there is a lot of discussion about the future of medicine. From research and development to regulatory approval and access to patients until the withdrawal of a medicinal product from the market, there have been many challenges and a lot of barriers to overcome. In parallel, the business environment changes rapidly. So, the big question is how the pharma ecosystem will evolve in the future. The current literature about the latest business and scientific evolutions and trends was reviewed. In the business environment, vast changes have taken place via the development of the internet as well as the Internet of Things. A new approach to production has emerged in a frame called Creative Commons; producer and consumer may be gradually identified in the context of the same process. As technology rapidly evolves, it is dominated by Artificial Intelligence (AI), its subset, Machine Learning, and the use of Big Data and Real-World Data (RWD) to produce Real-World Evidence (RWE). Nanotechnology is an inter-science field that gives new opportunities for the manufacturing of devices and products that have dimensions of a billionth of a meter. Artificial Neural Networks and Deep Learning (DL) are mimicking the use of the human brain, combining computer science with new theoretical foundations for complex systems. The implementation of these evolutions has already been initiated in the medicinal products' lifecycle, including screening of drug candidates, clinical trials, pharmacovigilance (PV), marketing authorization, manufacturing, and the supply chain. This has emerged as a new ecosystem which features characteristics such as free online tools and free data available online. Personalized medicine is a breakthrough field where tailor-made therapeutic solutions can be provided customized to the genome of each patient. Various interactions take place as the pharma ecosystem and technology rapidly evolve. This can lead to better, safer, and more effective treatments that are developed faster and with a more solid, data-driven and evidence-concrete approach, which will drive the benefit for the patient.

Exploring the Convergence of Visual Computing Across Machine Learning, Entertainment, Design, Medicine, Education and Beyond

Visual computing is an interdisciplinary field combining computer science, mathematics, physics, and cognitive sciences to develop, analyze, and produce visual information. It aims to enhance activity organization and interaction through visual elements, including images, videos, flowcharts, and icons. This field addresses tangible components, like touchscreens and interactive whiteboards, and intangible components, such as data patterns and algorithms, visually represented for better comprehension and decision-making. Key aspects of visual computing include the visual computer environment, which facilitates interaction through images and text, and visual applications that manage extensive image data, including videos and 3D scenes. Beyond its association with videos and 3D design, visual computing influences computer graphics and data visualization. Modern software's ability to generate detailed images and process visual data has driven advancements in computer vision and scientific analysis, linking visual computing with disciplines like machine learning, human-computer interaction, and data science.

Security Control of Cyber-Physical Systems under Cyber Attacks: A Survey.

Cyber-physical systems (CPSs), which combine computer science, control systems, and physical elements, have become essential in modern industrial and societal contexts. However, their extensive integration presents increasing security challenges, particularly due to recurring cyber attacks. Therefore, it is crucial to explore CPS security control. In this review, we systematically examine the prevalent cyber attacks affecting CPSs, such as denial of service, false data injection, and replay attacks, explaining their impacts on CPSs' operation and integrity, as well as summarizing classic attack detection methods. Regarding CPSs' security control approaches, we comprehensively outline protective strategies and technologies, including event-triggered control, switching control, predictive control, and optimal control. These approaches aim to effectively counter various cyber threats and strengthen CPSs' security and resilience. Lastly, we anticipate future advancements in CPS security control, envisioning strategies to address emerging cyber risks and innovations in intelligent security control techniques.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations.

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery. This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them. Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties. The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare. Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.

Analysis of quantum gates in quantum circuits

Although classical computing has brought great convenience to mankinds lives, classical computing is facing some problems. Combining computer science, information theory, and quantum mechanics, quantum information has been developed in many fields. Quantum gates can operate quantum bits, the carrier of quantum information, while quantum circuits are composed of quantum gates and quantum bits, showing the logic of operations. This study summarizes the fundamental concepts of quantum informatics: qubits, quantum circuits, and quantum gates where it is proved that single-bit quantum gates and CNOT gates are universal quantum gates. In addition, this research also demonstrates the current progress of quantum gates in the examination of fidelity of quantum gates, improvement of fidelity of quantum gates, and teleportation of quantum gates. Meanwhile, according to the current development status, the limits and prospects of quantum information are discussed. These results shed light on helping readers understand the basic concepts of quantum information, thus understand the future applications and developments in quantum information.

A Survey of Detection and Mitigation for Fake Images on Social Media Platforms

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

The Role of Algorithms in Everyday Life

Abstract: This paper sheds light on how algorithms have become a part of our lives impacting various aspects. It explores their influence on shaping social media content, driving search engines, recommendation systems and financial decision making. The increasing significance of algorithms in interactions raises concerns about transparency, fairness and ethical considerations. Furthermore it delves into their effects on healthcare, education and employment while highlighting both the benefits and drawbacks they bring. The concept of accountability is introduced to ensure design and deployment of algorithms that uphold societal values and prevent discrimination. By taking an approach that combines computer science, social sciences and ethics this paper emphasizes the importance of comprehending the role played by algorithms. Society must understand their mechanisms and implications in order to harness their potential while safeguarding against consequences. This investigation underscores the necessity for discussions and regulations that strike a balance between innovation and protecting rights, social equality and human autonomy in this era driven by algorithms. This synthesis of topics serves as a foundation for research in this field.

Artificial Intelligence in Prehospital Emergency Health Care

What Is the Issue? Health care systems in Canada, including prehospital emergency health care services, are facing staffing shortages and increased patient volumes, and are looking for creative solutions to ensure the provision of high-quality care. What Is the Technology? Artificial intelligence (AI) combines computer science and data to mimic human thought processes, problem solving, and responses. AI relies on statistical models, algorithms, data analysis, and machine learning to make predictions based on past behaviours or results (predictive AI) or to learn from past data to generate new content (generative AI). Chatbots, such as ChatGPT, are examples of generative AI. What Is the Potential Impact? In health care settings, AI can be used to automate some clinical and administrative processes to create efficiencies and reduce burden on health care providers and health administrators. In a prehospital setting, this can include AI used at the dispatch stage (e.g., analyzing the conversation and prompting a dispatcher with additional questions, or translating speech to help a dispatcher understand a caller in real time) or in an ambulance (e.g., providing traffic analysis or information to support optimal patient management). What Else Do We Keed to Know? The use of AI in prehospital emergency health care is still in the early stages of development and implementation. Early implementation of AI programs such as those to detect out of hospital cardiac events and to triage emergency calls during peak times are underway in some countries. More real-world clinical trials and research are required before widespread implementation will take place.

Differentially expressed protein-coding genes in ankylosing spondylitis: Emerging insights into pathogenesis and therapeutic approaches.

Ankylosing spondylitis (AS) is a chronic, progressive inflammatory rheumatic disease affecting the spine, axial skeleton, and sacroiliac joints. Pathogenesis of AS encompasses enthesitis, synovitis, and osteoproliferation, leading to the formation of syndesmophytes, ankylosis, and spinal rigidity. Bioinformatics, an interdisciplinary field combining computer science, mathematics, and biology, enables the analysis of complex biological data for investigating AS pathogenesis. This review summarizes differentially expressed protein-coding genes in peripheral blood or local tissues of AS patients compared with healthy controls and comprehensively reviews currently available therapeutic agents. The objective is to enhance the understanding of AS pathogenesis, inform diagnosis, identify novel therapeutic targets, and facilitate personalized medicine. This review contributes to a deeper understanding of AS pathogenesis and provides a foundation for developing innovative therapeutic approaches.

LANGUAGE ATTITUDE AND SENTIMENT ANALYSES IN GETTING THE INSIGHTS TOWARDS COVID-19’S OMICRON VARIANT NEWS

Omicron variant has been massively reported on Indonesian mass media following the spread of other previous variants during Covid-19 pandemic. This research combines computer science and linguistics to analyze the news on the variant. It implemented quantitative research using computational algorithm by collecting the titles of the news from Indonesian mainstream online mass media. Sentiment Analysis (SA) was applied to obtain the sentiments, opinion, and subjectivities of the texts along with topic modeling in classifying the topics. The words in the headline news titles were used as the data and grabbed by Python programming language. A criterion-based sampling was employed in to select the relevant data and to formulate the criteria in the research methodology. The results were filtered to ‘Omicron’ keyword for SA processing by the Azure Text Sentiment Analysis tool. The results of SA, as computational research, was then confirmed with Attitude Analysis (AA) from the perspective of Systemic Functional Linguistics. AA classified the words into affect, judgment, and appreciation as the attitude construed in English text. This research provides SA as the insights of Omicron issue. The presence of AA extracts the words into bipolar senses of human’s meaning interpretation. AA is important to straighten SA findings. SA has contextual meaning problem and requires study on its words classified in ‘neutral’ which are then confidently directed into positive or negative meanings by AA. It is found that there are different dynamics by SA and AA findings as they reflect particular meanings. Besides their difference, SA is useful for managing overload data into fast policy making whereas AA makes sure the acceptable meanings to people. In this case AA corrects the bias occurring from SA.

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists.

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. We hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

PROPERTIES OF DISCRETE-TIME CONDITIONAL LINEAR CYCLOSTATIONARY RANDOM PROCESSES IN THE PROBLEMS OF ENERGY INFORMATICS

Modern challenges in the energy industry require comprehensive research in the field of energy informatics, which combines computer science, control systems, and energy management systems within a single methodology. An important area of energy informatics is the study of problems of systems and processes modeling in energy, including energy loads and consumption. Linear and conditional linear random processes (CLRP) are mathematical models of signals represented as the sum of a large number of random impulses occurring at random times. The energy consumption, vibration signals of energy objects, etc. can be modeled using this approach. A variant of the CLRP model with discrete time, taking into account the cyclic properties of energy consumption, has been investigated in the paper. The goal is to justify the conditions for the discrete-time CLRP to be a periodically correlated random process, as well as a cyclostationary process. It has been shown that the corresponding conditions depend on the periodicity of the probability distributions of the kernel and the generating white noise of the CLRP representation. To achieve the goal, the properties of mathematical expectation and covariance function of CLRP, as well as the method of characteristic functions, have been used. The paper proves that the discrete-time CLRP is a periodically correlated random sequence if the generating white noise has periodic mathematical expectation and variance, and the kernel is a periodically correlated random field. Based on the analysis of the multivariate characteristic function, it has been proven that the discrete-time CLRP is cyclostationary if the generating white noise is a cyclostationary process and the kernel is a cyclostationary random field. The properties of discrete-time conditional linear cyclostationary random processes are important for mathematical modeling, simulation, statistical analysis, and forecasting of energy consumption. Keywords: mathematical model, energy informatics, conditional linear random process, cyclostationary process, white noise, characteristic function.

Medical robotic engineering selection based on square root neutrosophic normal interval-valued sets and their aggregated operators

<abstract><p>We introduce the concepts of multiple attribute decision-making (MADM) using square root neutrosophic normal interval-valued sets (SRNSNIVS). The square root neutrosophic (SRNS), interval-valued NS, and neutrosophic normal interval-valued (NSNIV) sets are extensions of SRNSNIVS. A historical analysis of several aggregating operations is presented in this article. In this article, we discuss a novel idea for the square root NSNIV weighted averaging (SRNSNIVWA), NSNIV weighted geometric (SRNSNIVWG), generalized SRNSNIV weighted averaging (GSRNSNIVWA), and generalized SRNSNIV weighted geometric (GSRNSNIVWG). Examples are provided for the use of Euclidean distances and Hamming distances. Various algebraic operations will be applied to these sets in this communication. This results in more accurate models and is closed to an integer $ \Delta $. A medical robotics system is described as combining computer science and machine tool technology. There are five types of robotics such as Pharma robotics, Robotic-assisted biopsy, Antibacterial nano-materials, AI diagnostics, and AI epidemiology. A robotics system should be selected based on four criteria, including robot controller features, affordable off-line programming software, safety codes, and the manufacturer's experience and reputation. Using expert judgments and criteria, we will be able to decide which options are the most appropriate. Several of the proposed and current models are also compared in order to demonstrate the reliability and usefulness of the models under study. Additionally, the findings of the study are fascinating and intriguing.</p></abstract>

Application of Machine Learning in Intelligent Medical Image Diagnosis and Construction of Intelligent Service Process.

The introduction of digital technology in the healthcare industry is marked by ongoing difficulties with implementation and use. Slow progress has been made in unifying different healthcare systems, and much of the globe still lacks a fully integrated healthcare system. As a result, it is critical and advantageous for healthcare providers to comprehend the fundamental ideas of AI in order to design and deliver their own AI-powered technology. AI is commonly defined as the capacity of machines to mimic human cognitive functions. It can tackle jobs with equivalent or superior performance to humans by combining computer science, algorithms, machine learning, and data science. The healthcare system is a dynamic and evolving environment, and medical experts are constantly confronted with new issues, shifting duties, and frequent interruptions. Because of this variation, illness diagnosis frequently becomes a secondary concern for healthcare professionals. Furthermore, clinical interpretation of medical information is a cognitively demanding endeavor. This applies not just to seasoned experts, but also to individuals with varying or limited skills, such as young assistant doctors. In this paper, we proposed the comparative analysis of various state-of-the-art methods of deep learning for medical imaging diagnosis and evaluated various important characteristics. The methodology is to evaluate various important factors such as interpretability, visualization, semantic data, and quantification of logical relationships in medical data. Furthermore, the glaucoma diagnosis system is discussed in detail via qualitative and quantitative approaches. Finally, the applications and future prospects were also discussed.

A goal-oriented Design Method of CO2 Power Cycle (CPC) System

The CO2 power cycle (CPC) system is an efficient and environmentally friendly method for waste heat recovery (WHR). However, the traditional design and optimization process of a CPC system is very complex and time-consuming. This paper proposes a novel goal-oriented design method based on machine-learning methods for quickly designing an optimized CPC system with given performance indicators. And taking the design of the CO2 transcritical power cycle (CTPC) system for internal combustion engines (ICEs) as an example. Firstly, the net output power and the total cost of the system prediction models are trained by simulated data. Then the multi-objective optimization of the system is carried out by using the genetic algorithm coupled with the prediction models, and the optimization results are used to train a classification model. Finally, the given target indicators are input into the classification model for goal-oriented designing and getting the optimal configuration. The results of the goal-oriented design validation show that the goal-oriented design method can design the CTPC system well. And, once the classification model is trained, the CTPC system's future goal-oriented design process only needs to be calculated once, significantly reducing design time. In conclusion, the goal-oriented design method based on machine-learning proposed is a novel and promising method. This is a technology that combines computer science and energy science and can provide users with a quick and reliable CPC system design method.

Evaluation of word embedding models to extract and predict surgical data in breast cancer

BackgroundDecisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making.ResultsWe applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input.ConclusionsThe proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.

The ABCD of Kidney Allograft Pathology-The Beginning of the Beginning.

Artificial intelligence (AI) is a field that combines computer science and robust datasets to enable problem solving. Big data are information whose scale, diversity, and complexity require specific technology and analytical methods to manage and extract value and hidden knowledge from them.[1][1]

Arduino based Real-Time Noise and Air Pollution Monitoring Alert System

Climate change, noise, water and air pollution, as well as other environmental problems, are becoming increasingly serious due to the rapid growth of the human population, industrialization, infrastructure improvements, the development of cars, and the use of fossil fuels. It is crucial to keep an eye on these problems and offer strategies to solve them in order to guarantee a healthy life and a brighter future. A new area of study that can help with the monitoring of noise and air pollution is called "smart sensor networks," which combines computer science, wireless communication, and electronics. Here, we propose an IoT-based system for monitoring air quality and noise pollution that will enable us to keep an eye on and assess these factors in specific locations. The system monitors the air for the presence of dangerous gases or compounds using a variety of wind sensors, and it continuously updates this information on a compact controller. Additionally, our system keeps track of audio intensity and transmits this information to an online server using IoT. The microcontroller that interprets this data and sends it over the Internet communicates with the sensors. This enables the government to keep an eye on, manage, and take action against air pollution in diverse locations. Additionally, respected authorities may keep an eye on noise pollution around hospitals, schools, and other non-wooded places. If the system notices problems with air quality or noise, it will alert authorities so they can take appropriate measures to mitigate the problem.

Optimization of Urban Waste Transportation Route Based on Genetic Algorithm

Under the normal conditions of the new economy, people’s living standards have improved; the amount of urban domestic waste has increased suddenly; and the differentiation of waste has become more and more complicated. However, the garbage transportation method at this stage has been difficult to meet the needs of reality. Therefore, it is particularly urgent to reform the original and simple treatment methods and adopt the latest and more reasonable garbage transportation. The urban waste transportation route optimization described in this article is quite complicated in the specific problems faced in the optimization of the urban domestic waste transportation route. This paper proposes to combine computer science to study the system optimization model and combine information management and other disciplines to develop and apply research on the dynamic management software of urban waste logistics systems. In the modern service industry, logistics occupies an important position. The optimization of urban waste transportation routes based on genetic algorithms also promotes the development of high-end modern service industries and is an important task to promote urban development and maintained hygiene and improve the healthcare of the citizens. The optimization of urban waste transportation routes is conducive to creating a clean and tidy urban environment.

Artificial intelligence in orthopaedics surgery: transforming technological innovation in patient care and surgical training.

Artificial intelligence (AI) is an exciting field combining computer science with robust data sets to facilitate problem-solving. It has the potential to transform education, practice and delivery of healthcare especially in orthopaedics. This review article outlines some of the already used AI pathways as well as recent technological advances in orthopaedics. Additionally, this article further explains how potentially these two entities could be combined in the future to improve surgical education, training and ultimately patient care and outcomes.