Asian Journal of Research in Computer Science
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Enhancing Compressed Sensing with Graph Structural Constraints: A Novel Approach to Active Learning in Measurement Matrices
No full textCompressed sensing on the graph, signals can be approximated by the graph and with the nodes containing information, so compressed sensing can collect information distributed on nodes or links. Also, compressed sensing on the graph becomes important due to the high cost of examining parameters one by one and the unavailability of information on some of them directly in the graph. In this article, by using the idea of active learning and random walking, a method has been introduced to improve the construction of the measurement matrix in the field of the graph, so that information from the graph that is used in the construction of the measurement matrix (assuming that the measurement matrix is underdetermined and non-horizontal) is introduced by the random walk method. They may be missed, identified, and, after observation, inserted into the measurement matrix, resulting in a stronger recovery of the original signal. To test this method, firstly, from the data set containing five hundred and ninety as the initial signal, the measurement matrix is constructed with two random walking methods and the proposed method, and the output vector is obtained from it, then the initial thin signal is received with two recovery algorithms, convex optimization and model is recovered and finally calculates the amount of error and the degree of similarity of the four recovered signals compared to the original signal and from their comparison, it is clear that the recovery of the thin signal from the matrix made by the proposed method and the recovery with the convex optimization algorithm has the highest The degree of similarity and the lowest amount of error with the original signal is compared to the other three recovered signals
AI and Digital Economies: A Comparative Analysis of South and Southeast Asia and Africa
No full textThis study aims to analyze the adoption of Artificial Intelligence (AI) in South and Southeast Asia and Africa, focusing on how AI technologies shape the digital economies in these regions. The objective is to assess AI\u27s role in key sectors such as energy, manufacturing, healthcare, and finance, and examine its impact on policy formulation, public service delivery, and governance. The paper employs a comparative literature review methodology. Data is gathered from secondary sources, including academic papers and reports, to analyze AI infrastructure, policy frameworks, sectoral impacts, and barriers to adoption. The study identifies similarities and differences in AI integration across the regions. The study reveals that South and Southeast Asia have made greater advancements in AI adoption, driven by stronger infrastructure and government policies, while Africa faces challenges due to infrastructure deficits and limited AI expertise. However, both regions show potential for leveraging AI to drive economic growth, particularly in underserved sectors like agriculture and financial inclusion. Additionally, the research highlights the critical role of AI governance, with a strong need for both regions to develop robust regulatory frameworks to ensure responsible and ethical AI implementation. The study is limited by its reliance on secondary data, which restricts deeper insight into on-the-ground challenges. Future research should incorporate field studies and interviews with AI practitioners for a more comprehensive understanding of AI’s impact. This paper contributes to the limited comparative research on AI adoption between South and Southeast Asia and Africa. It offers insights for policymakers and industry leaders on how AI can be harnessed for inclusive economic growth and transformation in these regions
Automation and AI in Precision Agriculture: Innovations for Enhanced Crop Management and Sustainability
No full textPrecision agriculture is one of the ways to achieve food security and sustainability through better resource-use optimization and crop productivity dealing with the challenges posed by the growing population and addressing environmental concerns. The study offers an in-depth look at the most recent developments in artificial intelligence (AI) and automation in precision agriculture (PA), with a particular emphasis on important technologies such as drones, autonomous tractors, AI-driven irrigation systems, and predictive analytics for crop management. The accuracy of crop monitoring and health assessments has increased by 30–50 percent as a result of AI-powered solutions, which have improved resource-based decision-making. Systems for precision irrigation and fertilization have increased crop yields by 5–15 percent when using 25–40 percent less water and 30-40 percent less fertilizer, respectively. Robotic harvesters and sprayers are examples of automation technologies that have reduced labor expenses by 20–40 percent and increased operational efficiency by 35 percent. Additionally, AI-based prediction models have reduced pest damage by 20–25 percent and reached an accuracy of 85–90 percent for crop yield forecasts and pest control. Despite these developments, issues of scalability, affordability for small farms, and data privacy still exist, which can hinder technology adoption among farmers. The evaluation follows by outlining ideas for future research, such as 5G, blockchain, and AI integration with cloud and edge computing. These technologies could improve decision-making and transparency in precision agriculture by enabling real-time data transmission, secure data management, and enhanced traceability, thus addressing current limitations and fostering trust among stakeholders
Factorization Algorithm for Semi-primes and the Cryptanalysis of Rivest-Shamir-Adleman (RSA) Cryptography
No full textThis paper introduces a new factoring algorithm called Anorld’s Factorization Algorithm that utilizes semi-prime numbers and their implications for the cryptanalysis of the Rivest-Shamir-Adleman (RSA) cryptosystem. While using the concepts of number theory and algorithmic design, we advance a novel approach that notably enhances the efficiency of factoring large semi-prime numbers compared to other algorithms that have been developed earlier. In our approach, we propose a three-step algorithm that factorizes relatively large semi-primes in polynomial time. We have introduced factorization up to 12-digit semi-prime using Wolfram|Alpha, a mathematical software suitable for exploring polynomials. Additionally, we have discussed the implications of the new algorithm for the security of RSA-based cryptosystems. In conclusion, our research work emphasizes the important role of factoring algorithms in the cryptanalysis of RSA cryptosystems and proposes a novel approach that bolsters the efficiency and effectiveness of semi-prime factorization, thereby informing the development of more powerful cryptographic protocols
Ensemble Learning Based Prediction for Cyber Harassment Observations on Tweets
No full textNow a days social media plays crucial role in allowing individuals to express their views. Based on their views find the information of different keywords/statements like sadness, happiness, teasing, harassment and abuse. Online abuse, a novel form of pestering, have become increasingly predominant in online groups in modern civilization. Detecting harassment is indeed a significant challenge. Several studies provide information on cyber harassment, but none of them offer a solid remedy. Several studies provide information on cyber harassment, but none of them offer a solid remedy. Due to this reason, multiple models can be practiced to recognize and block harassment-related communications. We have utilized ensemble machine learning models to predict accurate results. The twitter dataset used for our research. We observe two models getting accuracy for RF+DT is 92% and SVM+LR is 93%. It is similar accuracy in individual models. So, there is no difference between Ensemble or individual model accuracy rate
Health Monitoring of Li-ion Batteries: State-of-the-Art Techniques and Emerging Trends in Engineering and Energy Storage
No full textHealth monitoring of Li-ion batteries is crucial for ensuring their safe and reliable operation in various engineering and energy storage applications. This paper provides a comprehensive review of state-of-the-art techniques and emerging trends in health monitoring for Li-ion batteries. The abstract highlights the key aspects of the study, including the significance of health monitoring, the current state-of-the-art techniques, and the emerging trends shaping the future of battery health monitoring. The abstract emphasizes the importance of health monitoring in detecting and diagnosing battery degradation, identifying potential failure modes, and optimizing battery performance. It discusses the challenges associated with traditional battery health monitoring methods, such as limited accuracy, high cost, and complexity, and highlights the need for advanced monitoring techniques to address these limitations. Furthermore, the abstract outlines the current state-of-the-art techniques for battery health monitoring, including electrochemical impedance spectroscopy (EIS), voltage and temperature monitoring, and internal resistance measurement. It discusses the advantages and limitations of each technique and highlights recent advancements in sensor technology, data analytics, and machine learning algorithms for enhancing the accuracy and reliability of battery health monitoring. Moreover, the abstract explores emerging trends in health monitoring, such as the integration of wireless sensors, real-time monitoring systems, and cloud-based data analytics platforms. It discusses the potential benefits of these trends, including improved accessibility, scalability, and cost-effectiveness of battery health monitoring solutions. Overall, the abstract provides a comprehensive overview of the current state-of-the-art techniques and emerging trends in health monitoring for Li-ion batteries, highlighting the importance of continuous innovation in this field to ensure the safe and efficient operation of battery systems in engineering and energy storage applications
Optimizing Software Development Processes in Cloud Computing Environments Using Agile Methodologies and DevOps Practices
No full textThis exploration paper investigates the enhancement of programming improvement processes inside distributed computing conditions by coordinating Spry philosophies and DevOps rehearses. Distributed computing offers adaptable, on-request assets that altogether upgrade programming improvement and arrangement productivity. Nonetheless, expanding these advantages requires versatile strategies and constant incorporation and conveyance (CI/Album) rehearses. The review analyzes how Deft standards, with their emphasis on iterative turn of events and coordinated effort persistent criticism. Through contextual analyses and experimental examination, the exploration features best practices for executing Light-footed and DevOps in cloud-based projects. The discoveries give noteworthy bits of knowledge to programming improvement groups planning to use cloud advances to their maximum capacity
Distance-Distributed Energy Efficient Clustering (D-DEEC) Routing Protocol for Wireless Sensor Network
No full textA wireless sensor network (WSN) composed of many tiny devices that rely on energy efficient routing protocols for extension of their lifetime. Many cluster-based routing protocols have been proposed based on heterogeneity in recent times. Indeed, these protocols are aiming at achieving energy efficiency, throughputs and better lifetime of the networks. However, two important factors that could have helped some of these protocols to achieve the above-mentioned aims are really missing. Factors such as the distance and average distance between nodes and the Base Station (BS) in selecting a cluster head needed to be considered. These were the major challenges that were identified in the Distributed Energy Efficient Clustering (DEEC) protocol after careful study. As a result, the throughputs and the lifetime of the scheme were affected. In this paper, a reviewed hierarchy-based heterogeneous routing protocol called Distance-DEEC (D-DEEC) is proposed to enhance the DEEC protocol. The new algorithm took into account the residual energy, distance of the individual nodes and average distance of all the nodes from the BS in selecting the Cluster Heads (CHs). This has allowed the protocol to select a cluster head that has high residual energy, is closer to the BS and at the same time not too far from its neighbours. The scheme also employed the sleep and awake approach to reduce energy dissipation. The technique allows the Base BS to calculate the maximum energy of distant nodes and determines when such nodes can transmit their report based on a given threshold energy, Eth . The performance of the proposed algorithm was evaluated using MatLab R2018a and the outcomes showed that, D-DEEC protocol outperformed TDEEC in terms of energy consumption, throughputs and the network lifetime.
Identifying the Learning Style of Students Using Machine Learning Techniques: An Approach of Felder Silverman Learning Style Model (FSLSM)
No full textIdentification of the learning style of the students in the teaching and learning environment plays a significant importance in improving both teaching and learning perspectives. The intension of the research was to investigate about applying the Machine Learning Techniques for identification of the Learning style of the students in online learning environment based on the Felder Silverman Learning Style (FSLSM) identification model. The significance of this experiment is that the proposed methodology considers the combination of access frequency (f) of course materials and total time (T) students spent on each course activity. For data collection process, it was designed reusable plugin for the Moodle for time tracking. Real-time dataset was prepared using three course modules designed according to the features of FSLSM model and the features for analyzing the students according to the FSLSM, it was selected seven criteria, and the features were validated using Pearson Correlation Coefficient method. These course modules were enrolled 150 students per each module. Once the data set was prepared, the data set was preprocessed and applied Five Supervised Classification Machine learning algorithms as Decision Tree, Logistic Regression, Random Forest, Support Vector Machine and K-Nearest Neighbors algorithm. The models were evaluated using Accuracy, Precision, Recall and F1 values. Over the five algorithms the Decision Tree classifier algorithm performed with best average accuracy with 93.5% for Input, 86% for Perception, 89.5 for Processing and 94% for Understanding dimension. The models were validated using the K-fold Cross validation and Standard Deviation values. Mean Squared Error, Bias and Variance values were considered the evaluation of underfitting or overfitting context of the model. To parameter optimization, the Grid Search Methodology was applied to find the best combination of criterion for the model. Finally, an application was developed for Identifying the Learning Style of the Students using the designed Machine learning model. The Consistency of the ML Model based on the Decision Tree classifier algorithm were evaluated using the results generated through developed application and the results suggested that consistency for taught machine learning algorithms is often between 85% to 95%, which is an acceptable range. The results generated by the application for identification of the learning style suggested the combination of learning style for particular students sample as Global-Mild, Visual- Strong, Sensing- Moderate and Reflective-Strong. Identification of these combination of learning style assist for teachers by giving an insight which components of the learning contents should be improved in course designing process
An Evaluation of Digital Addressing System Implementation in Ghana
No full textPurpose: The research reviews the implementation of Ghana’s Digital Addressing System. Nearly six years into implementation and without an outward appearance of the meeting of the announced goals of the project, the research sets out to find out the likelihood of success or failure of the project and suggest remedial measures.
Relevance: The implementation of digital addressing in Ghana holds and demonstrates promise for benefits relative to transportation, courier services, taxation, property ownership and title, among others. For policy makers, the findings of this work give direct pointers to what needs to be done to keep the concept useful. For researchers, the work has only opened the doors for further academic and technical introspection into digital addressing and its nuances especially in Ghana.
Methods: Using a variety of quantitative and qualitative techniques the study assessed the implementation of the policy employing interviews, surveys, workshops and group discussion. It hinged on the Design-Reality Gap (DRG) model to identify the issues of interest with Ghana\u27s digital addressing systems.
Findings: We found out that the attempts, although well intended, fall short in several best practice aspects. The research found it difficult to confidently showcase any immediate successes arising out of the implementation of the Ghana Post GPS App and System and proceeds to point out specific areas in need of attention if Ghana’s Digital Addressing efforts are to meet the presumptive goals.
Conclusion: The Digital Addressing System might be a partial failure unless action is taken to close design-reality gaps