Computer Science Algorithms Research Articles

The Logic and Application of Greedy Algorithms

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.

Efficiency of large integer multiplication algorithms: A comparative study of traditional methods and Karatsuba's algorithm

The large integer multiplication is the basis of many computer science algorithms, ranging from cryptography to complex calculations in various scientific fields. Contemporary society excessively depends on complex computing tasks. Hence, the need for good algorithms is becoming increasingly apparent as well. This text gives the reader an in-depth knowledge of the multiplication algorithms of large integers by contrasting traditional algorithms with the new Algorithm developed by Karatsuba. This research methodology involves a comparative analysis of the components using an advanced analysis framework that primarily focuses on execution times, efficiency metrics, and resource utilization. Incontrovertibly, the experimental results confirm the Karatsuba algorithm's undoubted hastiness compared to the conventional approaches. This study extends our grasp of the evolution of algorithms in computational optimization, enabling people to get unique and relevant findings that will benefit numerous areas where large integer multiplications are involved. In addition to these findings, the study also highlights the importance of algorithm selection in ensuring computational efficiency and accuracy in large integer multiplications across various applications.

ALGORITHM VISUALIZER

Everybody has a different way of learning new information, this is called your learning style. Our learning style is how we best learn information. One of the most popular learning styles of 21st century is visual learning. “Algorithm Visualizer” is an online visual learning tool which acts as a catalyst in learning complex computer science algorithms. It provides an interactive experience for a learner to visualize the end-to-end implementation of an algorithm. The application contributes in solving the burden of learning two of the most popularly known algorithms, searching and sorting. Key Words: Algorithm visualizer, Sorting, Searching, Algorithm.

Sky-Sorter: A Processing-in-Memory Architecture for Large-Scale Sorting

Sorting is one of the most important algorithms in computer science. Conventional CPUs, GPUs, FPGAs, and ASICs running sorting are fundamentally bottlenecked by the off-chip memory bandwidth, because of their von-Neumann architecture. Processing-near-memory (PNM) designs integrate a CPU, a GPU or an ASIC upon an HBM for sorting, but their sorting throughput are still limited by the HBM bandwidth and capacity. In this paper, we propose a skyrmion racetrack memory (SRM) -based PIM accelerator, <i>Sky-Sorter</i> , to enhance the sorting performance of large-scale datasets. Sky-Sorter implements samplesort which involves four steps, sampling, splitting marker sorting, partitioning, and bucket sorting. An SRM-based random number generator (TRNG) is used in Sky-Sorter to randomly sample records from the dataset. Sky-Sorter divides the large dataset into many buckets based on sampled splitting markers by our proposed SRM-based partitioner. Its partitioning throughput matches the off-chip memory bandwidth. We further designed an SRM-based sorting unit (SU) to sort all records of a bucket without introducing extra CMOS logic. Our SU uses the fast in-cell insertion characteristics of SRMs to implement and perform insertsort within a bucket. Sky-Sorter employs SUs to sort all buckets simultaneously by fully utilizing large internal array bandwidth. Compared to state-of-the-art accelerators, Sky-Sorter improves the throughput per Watt by <inline-formula><tex-math notation="LaTeX">$\sim 4\times$</tex-math></inline-formula> .

Applications of Mathematical in Computer Science

Abstract: Mathematics (The QUEEN’s mother of all Sciences), is the foundation of Computer Science. Mathematics can be perceived in our garden or park from symmetry of leaves, flowers, fruits etc. and by so many examples of Geometry and symmetry can be seen in nature. Scientists and researchers cannot ideally accomplish their work without the inclusion of mathematics. Mathematics is sociable for analytical skills needed in Computer Disciplines like; Concepts of binary number system, Boolean algebra, Calculus, Discrete mathematics, linear algebra, number theory, and graph theory are the most applicable to the subject of computer science with the accessional emergence of new concepts like machine learning, artificial intelligence, virtual reality and augmented reality make the future of mathematics grow endless. Mathematics has been an important intellectual preoccupation of man for a long time. Computer Science as a formal discipline is about seven decades young. Is the almost spontaneous use of computing? In this article, this paper convey to the frontage the many close connections and parallels between the Mother and daughter sciences. The paper underscores the strong interplay and interactions by looking at some exciting contemporary results from number theory and combinatorial mathematics and algorithms of computer science.

Convex Geometry and its Applications

The geometry of convex domains in Euclidean space plays a central role in several branches of mathematics: functional and harmonic analysis, the theory of PDE, linear programming and, increasingly, in the study of algorithms in computer science. The purpose of this meeting was to bring together researchers from the analytic, geometric and probabilistic groups who have contributed to these developments.

Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator.

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.

The feasibility and accuracy of machine learning in improving safety and efficiency of thrombolysis for patients with stroke: Literature review and proposed improvements.

In the treatment of ischemic stroke, timely and efficient recanalization of occluded brain arteries can successfully salvage the ischemic brain. Thrombolysis is the first-line treatment for ischemic stroke. Machine learning models have the potential to select patients who could benefit the most from thrombolysis. In this study, we identified 29 related previous machine learning models, reviewed the models on the accuracy and feasibility, and proposed corresponding improvements. Regarding accuracy, lack of long-term outcome, treatment option consideration, and advanced radiological features were found in many previous studies in terms of model conceptualization. Regarding interpretability, most of the previous models chose restrictive models for high interpretability and did not mention processing time consideration. In the future, model conceptualization could be improved based on comprehensive neurological domain knowledge and feasibility needs to be achieved by elaborate computer science algorithms to increase the interpretability of flexible algorithms and shorten the processing time of the pipeline interpreting medical images.

THE DESIGN OF EVOLUTIONARY ALGORITHMS: A COMPUTER SCIENCE PERSPECTIVE ON THE COMPATIBILITY OF EVOLUTION AND DESIGN

AbstractThe effectiveness of evolutionary algorithms is one of the issues discussed in The Compatibility of Evolution and Design, where it is argued that such algorithms are only effective when stringent preconditions are met. This article considers this issue from the perspective of computer science. It explores the properties of problems that can be effectively solved by evolutionary algorithms, and the extent to which such algorithms need to be carefully adjusted. Although there are important differences between the study of evolutionary algorithms in computer science and the study of biological evolution, it is argued that the experience of computer science can help to strengthen the claim that evolutionary mechanisms generally require a considerable degree of fine‐tuning.

Review on String-Matching Algorithm

String-matching algorithm is one of the most researched algorithms in computer science which has become an important factor in many technologies. This field aims at utilizing the least time and resources to find desired sequence of character in complex data content. The most classical and famous string-search algorithms are Knuth-Morris-Pratt (KMP) algorithm and Boyer-Moore (DM) algorithm. These two algorithms provide efficient heuristic jump rules by prefix or suffix. Bitap algorithm was the first to introduce bit-parallelism into string-matching field. Backward Non-Deterministic DAWG Matching (BNDM) algorithm is a modern practical algorithm that is an outstanding combination of theoretical research and practical application. Those meaningful algorithms play a guiding role in future research in string-search algorithm to improve the average performance of the algorithm and reduce resource consumption.

Toward an IoT-Based Software-Defined Plumbing Network System With Fault Tolerance

in this paper we will see the application of computer science algorithms to the plumbing system. We propose a fault tolerant tap water system which is impossible without Internet of things and algorithms . We will show that the problem is a mutual exclusion group problem and we propose an adapted algorithm version from the literature as a solution . Coupling algorithms with the configurable plumbing network we believe that this will open new field of research on IoT we called it software defined plumbing Network where components that have been traditionally implemented in hardware (e.g. water mixers, spring faucets ,flow sensors, etc.) are instead implemented by means of software . This way we can solve other problem like instantaneous hot water,automatic cleaning of the water heater..etc since due to computer algorithms the systems can be easily smart, extensible and adaptive.

Position Paper: Dijkstra's Algorithm versus Uniform Cost Search or a Case Against Dijkstra's Algorithm

Dijkstra's single-source shortest-path algorithm (DA) is one of the well-known, fundamental algorithms in computer science and related fields. DA is commonly taught in undergraduate courses. Uniform-cost search (UCS) is a simple version of the best-first search scheme which is logically equivalent to DA. In this paper I compare the two algorithms and show their similarities and differences. I claim that UCS is superior to DA in almost all aspects. It is easier to understand and implement. Its time and memory needs are also smaller. The reason that DA is taught in universities and classes around the world is probably only historical. I encourage people to stop using and teaching DA, and focus on UCS only.

Discovering the evolution of artificial intelligence in cancer research using dynamic topic modeling

The rapid growth of healthcare data in recent years calls for more advanced and efficient analytic techniques. Artificial intelligence facilitates finding insightful patterns in massive high-dimensional data. Considering the latest movements towards using machine learning and deep learning techniques in the medical domain, in this study, we focused on the publications in which researchers employed artificial intelligence techniques for cancer diagnosis and treatment. Using dynamic topic modeling and natural language processing techniques, we analyzed the contents and trends of more than 12,000 scientific publications within the period of 2000 to 2018, extracted from two different sources, i.e., Elsevier’s Scopus and PubMed. While drawing the landscape of cancer research, our results also shed light on the evolution of artificial intelligence techniques and algorithms used for cancer diagnosis and treatment. Our findings confirm that modern computer science algorithms are being widely applied to extract patterns from large-scale medical images to cure different types of cancer with a special focus on deep learning techniques in recent years.

Utilizing machine learning algorithms in computer science for drug repurposing

Utilizing machine learning algorithms in computer science for drug repurposing

Secure consensus with distributed detection via two-hop communication

In this paper, we consider a multi-agent resilient consensus problem, where some of the nodes may behave maliciously. The approach is to equip all nodes with a scheme to detect neighboring nodes when they behave in an abnormal fashion. To this end, the nodes exchange not only their own states but also information regarding their neighbor nodes. Such two-hop communication has long been studied in fault-tolerant algorithms in computer science. We propose two distributed schemes for detection of malicious nodes and resilient consensus with different requirements on resources for communication and the structures of the networks. In particular, the detection schemes become effective under certain connectivity properties in the network so that the non-malicious nodes can share enough information about their neighbors. It is shown that the requirements are however less stringent than those for conventional algorithms. A numerical example is presented to demonstrate the performance of the proposed methods in wireless sensor networks.

Determining the Activity Series with the Fewest Experiments Using Sorting Algorithms

Determining the activity series of a collection of elements is a classic pedagogical experiment, in which pairs of elements are reacted to determine the relative rank ordering of their reactivity. Determining the optimal sequence of pairwise experiments that minimizes the total number of experiments corresponds to well-known comparison sorting algorithms in computer science. We describe relevant algorithms (insertion sort, binary insertion sort, merge sort, and merge insertion sort) and their application to the activity series problem and discuss ways that this connection can contribute to the introductory chemistry and computer science curricula. In addition to pedagogical interest, this illustrates a simple form of artificial intelligence for chemical experiment planning.

From recursive to dynamic: An algorithm for dealing with a problem

One of the problems in data science is related to data management, especially big data. It is indirectly related to algorithms in computer science. Therefore, the meeting between big data and the appropriate algorithm to produce a good output requires characterization and explanation. This paper reveals one of them, by involving the recursive and dynamic principles of programming, and by comparison giving choices as answers to problems.

A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset.

Coronavirus, also known as COVID-19, has spread to several countries around the world. It was announced as a pandemic disease by The World Health Organization (WHO) in 2020 for its devastating impact on humans. With the advancements in computer science algorithms, the detection of this type of virus in the early stages is urgently needed for the fast recovery of patients. In this paper, a study of neutrosophic set significance on deep transfer learning models will be presented. The study will be conducted over a limited COVID-19 x-ray. The study relies on neutrosophic set and theory to convert the medical images from the grayscale spatial domain to the neutrosophic domain. The neutrosophic domain consists of three types of images, and they are the True (T) images, the Indeterminacy (I) images, and the Falsity (F) images. The dataset used in this research has been collected from different sources. The dataset is classified into four classes {COVID-19, normal, pneumonia bacterial, and pneumonia virus}. This study aims to review the effect of neutrosophic sets on deep transfer learning models. The selected deep learning models in this study are Alexnet, Googlenet, and Restnet18. Those models are selected as they have a small number of layers on their architectures. To test the performance of the conversion to the neutrosophic domain, more than 36 trials have been conducted and recorded. A combination of training and testing strategies by splitting the dataset into (90–10%, 80–20%, 70–30) is included in the experiments. Four domains of images are tested, and they are, the original domain, the True (T) domain, the Indeterminacy (I) domain, and the Falsity (F) domain. The four domains with the different training and testing strategies were tested using the selected deep transfer models. According to the experimental results, the Indeterminacy (I) neutrosophic domain achieves the highest accuracy possible with 87.1% in the testing accuracy and performance metrics such as Precision, Recall, and F1 Score. The study concludes that using the neutrosophic set with deep learning models may be an encouraging transition to achieve better testing accuracy, especially with limited COVID-19 datasets.

Current Perspective of Symbiotic Organisms Search Technique in Cloud Computing Environment: A Review

Nature-inspired algorithms in computer science and engineering are algorithms that take their inspiration from living things and imitate their actions in order to construct functional models. The SOS algorithm (symbiotic organisms search) is a new promising metaheuristic algorithm. It is based on the symbiotic relationship that exists between different species in an ecosystem. Organisms develop symbiotic bonds like mutualism, commensalism, and parasitism to survive in their environment. Standard SOS has since been modified several times, either by hybridization or as better versions of the original algorithm. Most of these modifications came from engineering construction works and other discipline like medicine and finance. However, little improvement on the standard SOS has been noticed on its application in cloud computing environment, especially cloud task scheduling. As a result, this paper provides an overview of SOS applications in task scheduling problem and suggest a new enhanced method for better performance of the technique in terms of fast convergence speed.

Algorithm, Machine Learning and Artificial Intelligence

We discuss the concepts of algorithm, machine learning and artificial intelligence. We start by noting that different definitions of algorithm may serve different epistemic purposes, and by highlighting the specificities of computer science algorithms together with a high-level overview of their different types. We continue by discussing machine learning as a discipline and commenting on its algorithms, with a focus on artificial neural networks due to their role in the deep learning revolution. We discuss the epistemic and ethical challenges arising from the widely spread use of machine learning algorithms in the applications. Then, we discuss the artifacts of the artificial intelligence domain, clarifying their relation with machine learning algorithms. Finally, we provide the reader with comments on the emergence of the “trustworthy AI” paradigm, highlighting some of the theoretical challenges affecting the discussions on trust in human-AI interactions.