Innovations In Computer Science Research Articles

Emerging Trends In Artificial Intelligence Learning Methods: Deep Learning And Machine Learning Innovation In Computer Science

Emerging Trends In Artificial Intelligence Learning Methods: Deep Learning And Machine Learning Innovation In Computer Science

Participatory Design Thinking: A User-Centered Approach to Computer Science Innovation

Traditional design thinking, while valuable, has limitations in user involvement. This paper proposes Participatory Design Thinking (PDT), a novel approach that addresses these limitations by placing users at the core of the entire design process in computer science (CS) projects (2). In this study, the shortcoming of traditional design thinking in user centricity was discussed and core principles and benefits of PDT are presented. Then, the practical implementation of PDT in CS, including user selection, training, and integration throughout the design stages were investigated. Finally, the potential impact of PDT on CS innovation and propose directions for future research were explored.

Analysis of Innovation Paths Big Data Mining to Big Data Algorithm of Business Administration

In the process of enterprise management, there are some problems such as poor accuracy and long selection time of computer science innovation path, which seriously affect the effective selection of computer science path innovation. Based on a big data mining method, this paper analyzes the path innovation of computer science from three dimensions, constructs the path set of path innovation by least dichotomy, and obtains the optimal innovation path by derivation. Then, the maximum likelihood theory is used to calculate the innovation path and compared it with the previous path innovation methods, comparing the accuracy and calculation time of different innovation paths. MATLAB simulation results show that the big data mining method can improve the accuracy and comprehensiveness of innovation path selection, reaching more than 90%, and control the selection time of the innovation path within 25 seconds, and the overall result is better than the previous path innovation methods. Therefore, the big data mining method can improve the accuracy of computer science innovation path selection and meet the needs of computer science path innovation in business administration. However, in the research of big data mining methods, this paper ignores the analysis of multi-path innovation, which leads to insufficient research depth. In the future, it will further analyze multi-path innovation.

Main trends in educational development with the help of computer sciences

The article is devoted to the topical issue of searching for methods of learning development using technologies based on computer science. Technology plays an important role in society and has naturally integrated into the education sector. The catalyst for the widespread introduction of new forms of education was restrictions on conducting classical forms of classes during the COVID-19 pandemic. The pandemic has forced the global academic community to turn to new learning methods, including distance and online learning. It should be noted that COVID-19 has spurred digitization and innovation in computer science. Thanks to the application of telecommunication and information technologies compatible with various means of supporting learning based on intelligent online platforms, it has become possible, on the one hand, to acquire high-quality education via the Internet, and, on the other hand, to change the methods and forms of education. Based on the analysis of this situation, the contradiction between the emergence of new trends caused by online learning in the world of education and organizational, moral and psychological problems in the world of education is determined. To solve this contradiction, the task of finding means of introducing new technologies based on computer science in the world of education to solve organizational, moral and psychological problems in education arises. With the aim of timely and effective implementation of new trends in the world of education, it is necessary to define and propose modern information technologies based on the theory of computer science that will be able to solve organizational, moral and psychological problems in education. Solving such a task based on the methods and technologies of computer science is timely and relevant. The article considers: technical trends in teaching and learning, namely: gamification, blockchain, artificial intelligence; the growing trend of the importance of teaching interpersonal communication skills with the help of information technologies, namely: entrepreneurship, public speaking and leadership qualities; the trend of introducing nanolearning in order to increase the concentration of attention in less time; the trend of strengthening the role of the teacher as a coach or tutor; the trend of using information technologies for lifelong learning. It is shown how the introduction of new information technologies can solve organizational, moral and psychological problems in education. Proposed forms and methods of introducing new information technologies in educational institutions.

Hydrological Drought Forecasting Using a Deep Transformer Model

Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: MSE=0.11, MAE=0.21, RSME=0.31, and R2=0.92 for the Chattahoochee station, and MSE=0.06, MAE=0.19, RSME=0.23, and R2=0.93 for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere.

Penerapan Teknologi Metaverse pada Bank Syari’ah

Computer Science innovation plays a major role in everyday life-changing and enriching human interaction, communication, and social transactions. Currently, the fourth wave of computational innovation is underway relating to spatial (mapping), immersive technologies such as Virtual Reality (VR), and Augmented Reality (AR). Metaverse is a new interface that is expected to be used to perform all kinds of human-computer interactions. Bank BRI and Bank Mandiri are conventional banks that are starting to enter the metaverse ecosystem. Of the features that have been provided by BSI, there are no features related to the metaverse. From the search that the author did from various sources,untill now there has been no Islamic bank in Indonesia that has begun to penetrate the world of the metaverse. In this article, the focus of the discussion is on the metaverse application that can be used for virtual branch offices of Islamic banks. With the virtual branch in Metaverse, of course, it is hoped that it can be a new, fun journey for customers while at the same time being able to reach a wider community to carry out various digital transaction services as well as better customer service in the future. Spatial is one of the most worthy metaverse applications for the banking world, including Islamic banks. Spatial application provides features that can serve customers in the metaverse realm at a low cost. Spatial empowers users to take advantage of beautiful privacy spaces to share engaging content, build close-knit communities, and drive meaningful sales of creative works and products. Keywords: Augmented Reality, Metaverse, Islamic banking, Virtual Branch, Spatial.

Impact of Social Media on Geopolitics and Economic Growth: Mitigating the Risks by Developing Artificial Intelligence and Cognitive Computing Tools.

Social media is one of the most revolutionary innovations in computer science that facilitates connecting people in the world to share information, ideas, and thoughts. In recent years, social media has demonstrated tremendous growth, which has affected individuals, businesses, communities, and economies. The focus of the present study is to identify the impact of social media on geopolitics and economic growth. The study is based on a systematic review of previous literature on the subject. It has been revealed through the findings that social media impacts geopolitics by decreasing the level of censorship and increasing the spread of news or information, while it also enables the politicians to influence individuals over online social networks through the great level of access. On the other hand, it has been identified that social media has both positive and negative impacts on geopolitics and economic growth. Social media is able to unite diverse groups and individuals spread across the planet dedicated to specific issues. The formation of communities and the ability of social media to unite groups show how social media could contribute positively to geopolitics and economic growth. But it decreases the productivity level of the individuals; on the other hand, it does contain the potential to create a participatory economy, which can be beneficial for a particular country. Some argue that social sharing has encouraged people to use computers and mobile phones to express their concerns on social issues without actually having to engage actively with campaigns in real life. Their support is limited to pressing the “Like” button or sharing content. This study performs a thorough study selection exercise and a quality assessment to ensure that the present study is valuable to academia and the relevant stakeholders, especially the experts of computer science who can develop the smartest artificial intelligence and cognitive computing tools that can help mitigate those risks of social media for the geopolitically volatile, uncertain, complex, and ambiguous world and ensure smooth economic growth.

Towards an AI-Driven Marketplace for Small Businesses During COVID-19.

With the introduction of new COVID-19 variants such as Delta and Omicron, small businesses have been tasked with navigating a constantly changing business environment. Furthermore, due to supply chain issues, shortages of various critical products negatively affect businesses of all sizes and industries. However, continued innovation in Computer Science, specifically in sub-fields of Artificial Intelligence (AI), such as natural language processing (NLP), has created significant value for businesses through helpful data-driven features. To this end, we propose a platform utilizing AI-driven tools to help build an effective business-to-business (B2B) platform. The proposed platform aims to automate much of the market research which goes into selecting products and platform users during times of distress while still providing an intuitive e-commerce interface. There are three primary novel components to this platform. The first of these components is the Buyer’s Club (BC), which allows customers to pool resources to purchase bulk orders at a reduced cost. The second component is an automated system utilizing Natural Language Processing (NLP) to detect trending disaster news topics. Disaster topic detection can be applied to inform buyers and suppliers on adapting to changing market conditions and has been shown to match closely with Google Trends data. The third component is a regulation matching system, using a custom data set to help inform customers when purchasing products. Such guidance is necessary to comply with a regulatory environment that will be irregular for the foreseeable future.

Influence of coronary stenosis location on diagnostic performance of machine learning-based fractional flow reserve from CT angiography

BackgroundCompared with invasive fractional flow reserve (FFR), coronary CT angiography (cCTA) is limited in detecting hemodynamically relevant lesions. cCTA-based FFR (CT-FFR) is an approach to overcome this insufficiency by use of computational fluid dynamics. Applying recent innovations in computer science, a machine learning (ML) method for CT-FFR derivation was introduced and showed improved diagnostic performance compared to cCTA alone. We sought to investigate the influence of stenosis location in the coronary artery system on the performance of ML-CT-FFR in a large, multicenter cohort. MethodsThree hundred and thirty patients (75.2% male, median age 63 years) with 502 coronary artery stenoses were included in this substudy of the MACHINE (Machine Learning Based CT Angiography Derived FFR: A Multi-Center Registry) registry. Correlation of ML-CT-FFR with the invasive reference standard FFR was assessed and pooled diagnostic performance of ML-CT-FFR and cCTA was determined separately for the following stenosis locations: RCA, LAD, LCX, proximal, middle, and distal vessel segments. ResultsML-CT-FFR correlated well with invasive FFR across the different stenosis locations. Per-lesion analysis revealed improved diagnostic accuracy of ML-CT-FFR compared with conventional cCTA for stenoses in the RCA (71.8% [95% confidence interval, 63.0%–79.5%] vs. 54.8% [45.7%–63.8%]), LAD (79.3 [73.9–84.0] vs. 59.6 [53.5–65.6]), LCX (84.1 [76.0–90.3] vs. 63.7 [54.1–72.6]), proximal (81.5 [74.6–87.1] vs. 63.8 [55.9–71.2]), middle (81.2 [75.7–85.9] vs. 59.4 [53.0–65.6]) and distal stenosis location (67.4 [57.0–76.6] vs. 51.6 [41.1–62.0]). ConclusionIn a multicenter cohort with high disease prevalence, ML-CT-FFR offered improved diagnostic performance over cCTA for detecting hemodynamically relevant stenoses regardless of their location.

The impasse of competitive intelligence today is not a failure. A special issue for papers at the ICI 2020 Conference

The impasse of competitive intelligence today is not a failure. A special issue for papers at the ICI 2020 Conference

Facial recognition using two-dimensional principal component analysis and k-nearest neighbor: a case analysis of facial images

Science and Technology Innovation in Computer Science, especially in the facial image is increasingly needed in the Industrial Era of 4.0. The problem: How to use the working pattern of the Two-Dimensional Principal Component Analysis (2DPCA) method that integrated with K-Nearest Neighbors (KNN) in the Facial Image Recognition for various purposes? The purpose of this study, to analyze the Facial Image Recognition based on the method of Two-Dimensional Principal Component Analysis (2DPCA) which is integrated with KNN. This research uses the method of Two-Dimensional Principal Component Analysis (2DPCA) for the feature extraction process and the KNN classification method is applied to perform the data classification process so that the desired accuracy value is obtained. Research subjects use the image database from UCI repository, consists of 190 black and white face images of people taken with varying pose (straight, left, right, up), expression (neutral, happy, sad, angry), and size. The result of this study is the performance analysis of Facial Image Recognition based on the method of Two-Dimensional Principal Component Analysis (2DPCA) that is integrated with k-Nearest Neighbors (KNN).

The Research of Science and Technology Innovation in Computer Science through the Analysis of the Turing Award

The Turing Award is recognized as the highest distinction in computerscience and the Nobel Prize of computing. According to the Turing Award winners statistical data from 1966 to 2009, this paper analyses some characters of their research activity, such as achievement, angle of innovation, research field, age, the winner s interest and etc. The conclusion is that the Turing Award emphasizes on primitive theoretical innovation, interdisciplinary and it embodies research advantage accumulation and teamwork. At the same time it shows that most winners performed their award winner work when young or at middle age. At last, the paper provides some advice about science and technology innovation for computer research.

Periodic Table of Cryptocurrencies: Blockchain Categorization

The tech is already complicated enough, and if the terminologies aren't understood or clarified, it will further compound the complexities in this nascent industry. Everyone needs to know about this revolutionary tech, and as participants of this beautiful ecosystem it is our responsibility to simplify things as best as we can to the masses. The blockchain, the underlying technology, is the biggest innovation in computer science—the idea of a distributed database where trust is established through mass collaboration and clever code rather than through a powerful institution that does the authentication and the settlement. The biggest problems, though, have to do with clear categorization. I propose a theory of cryptocurrency periodic table policy in Altcoin markets with focus on supply rigidities identified with aggregate supply in cryptocurrency markets. The distribution of Altcoin across markets affects aggregate demand and output, however, these effects are not internalized in private financial decisions.

Developing a geographically weighted complex systems model using open-source data to highlight locations vulnerable to becoming terrorist safe-havens

ABSTRACTAdaptability and invisibility are hallmarks of modern terrorism, and keeping pace with its dynamic nature presents a serious challenge. Innovations in computer science have incorporated applied mathematics to develop a library of models that reflect a variety of approaches to counterterrorism including models designed to forecast attack locations. Although these can protect individual structures or locations, they risk changing the target without reducing the threat. This research developed ageospatial information system-based complex systems model using geographically weighted regression that incorporates a variety of quantitative, qualitative and geospatial variables that differ interms of scale, weight and type. Variable selection was based on scholarly research discoveries from specializations such as political science, geography, international relations, history and statistics to determine those variables that might impact an extremist’s selection of a safe-haven. Publically available data sets provided values that populated the variables for the model. The results display as highlighted areas most attractive to extremists. This prototype is intended to serve as a foundation for future geographically weighted models that incorporate variables from different academic disciplines. It is hoped this model will improve security through prevention by providing insights into safe-haven selection and identifying questions that should be asked.

Systems biology

The unequal race between technological and theoretical innovation in Computer Science has a mirror image in Systems Biology where the balance is even more biased toward the experimental side. The aim of Systems Biology is to produce a specification of natural systems, when artificial systems are usually already equipped with one 1 . As a consequence, Systems Biology is almost exclusively an experimental science where most of the innovation effort is geared toward the conception of devices able to extract more facts from biological systems. It has resulted in a modern trend of Systems Biology where high-throughput experiments are now producing big data and a massive inflation of publication volume (Figure 1). The lack of comprehensive integration of biological knowledge is hidden by the success stories of synthetic design using bio material: the initial aim of understanding how the basic components of the cell conspire in a complex and changing environment is modestly replaced by the science of hijacking cellular and genetic components in order to implement intelligently designed tasks. In turn these bio-devices enable biologists to set up more experiments and accumulate more data. This life cycle between technological innovation and data accumulation is encouraged by high impact journals in biology, which tend to give more credit to technological breakthroughs than (disputable) progress in understanding the ways of the cell.

Applying Big Data Analytic Methods In The Design of Prediction Algorithms For Efficacy And Adverse Effects of Psychiatric Medications

Psychiatric disorders are complex and heterogenic brain related diseases which are still not fully understood. Indeed, while other fields of medicine have seen significant advances in diagnosis and prognosis over the past decade, the field of psychiatry has been relatively stagnant. In contrast to this lack of understanding, a variety of medications are available today for people who suffer from psychiatric disorders. For example, there are dozens of currently available medications for depression, which according to the World Health Organization is the leading cause of disability at the western world.The fact that pharmacological reagents, such as antidepressant medications, affect behavior suggests that the phenotypic behavior is the end result of biological processes which occur within the brain and body of the organism. These aspects are still not fully elucidated due to fact that many genetic alterations and environmental factors are probably involved in depression and impact the efficacy of antidepressants. Considering all possible factors and variants is very challenging. Furthermore, while some patients respond to specific medications with minor adverse effects, others either do not respond or experience adverse effects, suggesting that there are fundamental differences between patients diagnosed with the same disorder. Therefore, in order to predict efficacy and adverse effects of an antidepressant treatment, analysis of many genetic factors is needed. Additional elements should be considered as well; for example, the patient's childhood development and current life status (demographic factors), and medical history (clinical factors).Innovations in computer science now allow for the collection and sharing of large amount of data, and the advancement of next generation sequencing technologies facilitates accumulation of genetic information. Taking advantage of these advances, we combine machine learning algorithms with clinical knowledge to design successful prediction algorithms for efficacy and adverse effects of common antidepressant medications. These prediction algorithms are validated using large databases obtained from the NIH. It is important to note however, that machine learning algorithms are not designed for the analysis of genetic data, as the number of tested subjects using these algorithms has to be several scales higher than the number of tested features. Since the human’s genome is comprised of over 3 billion nucleotides, each considered as a genetic feature, even if whole genome sequence of all the world’s population (7-8 billion people) were available, it still wouldn’t be enough to apply machine learning algorithms.At Taliaz we developed a pipe-line of algorithms and procedures which allows the use of machine learning algorithms on genetic data combined with clinical and demographic information. Using this pipeline, we have succeeded in designing prediction algorithms for the efficacy and adverse effects of current antidepressants, with up to 77% accuracy (76% specificity and 79% sensitivity). In my talk I will describe the features pre-scoring pipeline designed by Taliaz, and provide examples of prediction algorithms that we have designed using this pipeline.

Clustering under Perturbation Resilience

Motivated by the fact that distances between data points in many real-world clustering instances are often based on heuristic measures, Bilu and Linial [Proceedings of the Symposium on Innovations in Computer Science, 2010] proposed analyzing objective based clustering problems under the assumption that the optimum clustering to the objective is preserved under small multiplicative perturbations to distances between points. The hope is that by exploiting the structure in such instances, one can overcome worst case hardness results. In this paper, we provide several results within this framework. For center-based objectives, we present an algorithm that can optimally cluster instances resilient to perturbations of factor $(1 + \sqrt{2})$, solving an open problem of Awasthi, Blum, and Sheffet [Proceedings of the IEEE Annual Symposium on Foundations of Computer Science, 2010]. For $k$-median, a center-based objective of special interest, we additionally give algorithms for a more relaxed assumption in which we allow the optimal solution to change in a small $\epsilon$ fraction of the points after perturbation. We give the first bounds known for $k$-median under this more realistic and more general assumption. We also provide positive results for min-sum clustering, which is typically a harder objective than center-based objectives from an approximability standpoint. Our algorithms are based on new linkage criteria that may be of independent interest. Additionally, we give sublinear-time algorithms, showing algorithms that can return an implicit clustering from access to only a small random sample.

Collaborating and sharing data in epilepsy research.

Technological advances are dramatically advancing translational research in Epilepsy. Neurophysiology, imaging, and metadata are now recorded digitally in most centers, enabling quantitative analysis. Basic and translational research opportunities to use these data are exploding, but academic and funding cultures prevent this potential from being realized. Research on epileptogenic networks, antiepileptic devices, and biomarkers could progress rapidly if collaborative efforts to digest this "big neuro data" could be organized. Higher temporal and spatial resolution data are driving the need for novel multidimensional visualization and analysis tools. Crowd-sourced science, the same that drives innovation in computer science, could easily be mobilized for these tasks, were it not for competition for funding, attribution, and lack of standard data formats and platforms. As these efforts mature, there is a great opportunity to advance Epilepsy research through data sharing and increase collaboration between the international research community.

Rationale and Strategy for a 21st Century Scientific Computing Architecture: The Case for using Commercial Symmetric Multiprocessors as Supercomputers

Rationale and Strategy for a 21st Century Scientific Computing Architecture: The Case for using Commercial Symmetric Multiprocessors as Supercomputers