International Journal on Recent and Innovation Trends in Computing and Communication
Not a member yet
8613 research outputs found
Sort by
Prototype Using Ultrasonic Sensor to Assist Visually Impaired Individual in the Philippines
Get PDFPeople who are visually impaired deals with number of difficulties when exercising the most basic things in daily life. Some are frequently rely on others, which makes them less confident in an unusual setting and some use tools like white cane or simple sticks, but for some cirsumstances these could put their lives at risk while traveling. Since the Philippines has limited access to technological resources that could reduce the risk, the researchers came up with the idea of using an ultrasonic sensor to detect obstacles that are above or in front of the user. The study aims to develop a functional prototype that helps visually impaired individual and determine how well the functional prototype responds to the needs of the blind individual. Utilizing a problem-based approach to problem solving, the Design Thinking Methodology was the methodology employed in this study. As a result, using different sound and vibration frequencies the prototype can identify minimum distances of 120 cm, 80 cm, and 30 cm as an output. The study concludes that a successful tool could be a design prototype that uses sensor-equipped sticks to assist blind individuals in moving around and increasing their awareness of their surroundings. However, the scope of this study is limited as it is unable to determine the material of the obstruction or whether a hole exists in front of the user because the tool used in this work can only detect barriers above and in front of the user
A Novel Architecture of Software Testing based on SDN Hypervisor Technique for Big Data
Get PDFThere is a lack of network standard skills in present networking landscape. There is an increase in data plane granularity, data plane separation and simplifies the network devices, even networking industry has experienced a renewal with Software-Defined Networking (SDN). The device performance is improve by the linearly protocol by using SDN controller. The SDN-based software testing architecture is the basics of hypervisor approach. The application layer is initially combined with network updates, security and Quality of service. Software Defined Network (SDN) is a main feature. By using data plane communication protocol, the protocol communication is simplified. The physical switch controls the network data plane and virtual switch. The performance and efficiency are the accurate results that are achieved. Therefore, processing, storage, acquisition of big data and transmission are highly possible by SDN. The operation and design of SDN has big data impact. Hence, this method shows better results interms of accuracy, efficiency, computational time and security
Numerical Simulation and Assessment of Hyper Parameter Tuned Machine Learning Based Malware Detection System
Get PDFIn the realm of cybersecurity, the detection and mitigation of malware remain paramount challenges due to the constant evolution and sophistication of malicious software. This study presents a comprehensive numerical simulation and assessment of a hyperparameter-tuned machine learning (ML) system designed for the detection of malware. By employing a variety of ML algorithms, including decision trees, support vector machines, and neural networks, this research focuses on optimizing each model's hyperparameters to enhance detection accuracy. The methodology involves a rigorous simulation environment where numerous malware signatures and behaviors are analyzed to test the efficacy of the ML models. Hyperparameter tuning is achieved through advanced techniques such as grid search and randomized search, ensuring that each model operates at its optimal capacity. The results demonstrate a significant improvement in detection rates compared to traditional, non-tuned systems, with the tuned models achieving higher precision and recall metrics. This paper not only highlights the critical role of hyperparameter optimization in malware detection systems but also sets a benchmark for future research in employing machine learning to combat increasingly complex cybersecurity threats. The findings underscore the potential of hyperparameter-tuned ML models as robust tools in the ongoing battle against malware.
Causal Inference Methods for Understanding Attribution in Marketing Analytics Pipelines
Get PDFIn strategic decision-making, the limits of conventional predictive analytics have become more apparent as businesses negotiate more complex, data-rich settings. Because predictive models often just reveal correlations rather than the fundamental causes of change, they expose organisations to misunderstandings and inefficient responses. A revolutionary development is provided by causal machine learning models, which separate cause-and-effect correlations from big, multidimensional datasets. By simulating the possible effects of business decisions prior to execution, these models help decision-makers close the gap between insight and consequence. In these kinds of campaigns, many channels often provide ads to specific individuals. The industry is very interested in "attribution," which is the process of allocating conversion credit to the different channels. Marketing researchers have a plethora of options to better forecast and maybe explain customer behaviour because to the massive amount of data. In this work, a causally justified approach to conversion attribution in online advertising campaigns is presented. However, as this article will argue, academics studying marketing should not hastily forsake methodological and cognitive processes that have been honed over centuries of scientific and philosophical contemplation. By combining the literature from many hard sciences, we talk about the importance of machine learning in causal inference as well as current issues with data management and measurement in the age of digital data
Functional Differential Inclusion of Fractional Order in Banach Algebras
Get PDFIn this paper, the existence of the solution for fractional order neutral functional differential inclusion in Banach algebras is proved. existence the extremal solution for fractional order neutral functional differential inclusion in Banach algebras is established under certain monotonicity conditions
Study of Product-Based Learning Approach in Designing Electronics Systems Curricula
Get PDFProduct-Based Learning (PBL) is currently acknowledged as one of the most effective approaches in the development of curriculum in the electronics systems education. This methodology alters the dynamics where instead of conventional teacher-centered classroom lectures, the organization approaches learning in an applications-centered way. In Electronics systems, knowledge is constructed through affecting and being affected by the context and PBL enables the students to come up with new electronic products, to implement them, and to modify them through practicing. Thus, lesson-by-lesson adoption of theoretical section and its immediate application in solving practical problems helps the students gain a better hold on the base, including topic like circuit design, signal processing and system integration. It not only develops specific technical knowledge but also problem-solving skills and critical thinking along with team-work as the designers turn into engineers to solve the project problems. The goal of this paper is to introduce a product-based learning approach that enables engineering students to develop their skills in electronics systems design. Using real-world projects, students can gain hands-on experience and improve their problem-solving capabilities. The study also explores the various advantages of this approach, such as its ability to retain students' knowledge and improve their engagement
Transforming Personalized Education through AI-Enhanced Ontology Modelling in Dynamic Adaptive Learning Systems
Get PDFThis study explores the impact of integrating advanced AI technologies—AI-enhanced ontology modelling, reinforcement learning (RL), and natural language processing (NLP)—on educational systems. The AI-enhanced ontology modelling demonstrated substantial improvements with manual workload reduction increasing from 20% to 70%, adaptability of learning paths rising from 20% to 70%, and precision and accuracy improving from 20% to 70%. RL algorithms achieved 85% accuracy in predicting optimal learning modules, leading to a 20% improvement in student performance, a 30% increase in task completion rates, and a 15% rise in student engagement. Additionally, NLP techniques resulted in a 50% reduction in quiz creation time and a 95% relevance rate for quizzes, contributing to a 25% improvement in student performance. The dynamic adjustment of quiz difficulty based on real-time performance further enhanced learning outcomes and engagement. These findings highlight the significant benefits of AI technologies in enhancing educational efficiency, personalization, and effectiveness, demonstrating notable gains in content creation, learning experience optimization, and student performance
The Future of HCM: Moving to the Cloud
Get PDFThe landscape of Human Capital Management (HCM) is undergoing a significant transformation as organizations increasingly migrate their HCM systems to the cloud. Cloud-based HCM solutions offer unparalleled scalability, flexibility, and cost-efficiency, enabling businesses to streamline HR processes, enhance data accessibility, and leverage advanced analytics. This paper explores the future of HCM in the context of cloud migration, highlighting key drivers, benefits, and challenges associated with this shift. Through a comprehensive literature review and analysis of case studies from diverse industries, the study demonstrates how cloud-based HCM systems facilitate real-time data management, support remote and hybrid work models, and integrate seamlessly with other enterprise systems. Additionally, the research addresses critical concerns such as data security, privacy, and change management, proposing best practices for successful cloud adoption. The findings underscore the pivotal role of cloud computing in shaping the future of HCM, offering strategic insights for organizations aiming to optimize their human capital and maintain a competitive edge in a dynamic business environment
Metric Fuzziness and Eigenvalue Theory
No full textThis article is dedicated to the exploration of fuzzy eigenvalues and fuzzy eigenvectors within the context of a fuzzy metric space. To facilitate this discussion, we introduce a specific metric for this space. Furthermore, we provide comprehensive definitions for fuzzy eigenvalues and fuzzy eigenvectors, focusing on their application to fuzzy square matrices. In the course of our exploration, we establish a series of theorems pertaining to fuzzy eigenvalues and eigenvectors within a fuzzy metric space. To enhance understanding, we illustrate these theorems with practical examples
Leveraging AI and ML Tools in the Utility Industry for Disruption Avoidance and Disaster Recovery
Get PDFThe utility industry is facing increasing disruptions due to climate change, aging infrastructure, and cyber threats. To enhance operational resilience and disaster recovery, utilities leverage Artificial Intelligence (AI) and Machine Learning (ML) technologies. These tools enable predictive maintenance, anomaly detection, and real-time decision-making, allowing for the anticipation of failures and a faster crisis response. Case studies from companies such as Avangrid and PRASA highlight the practical benefits of AI and ML in reducing outages, optimizing resource allocation, and minimizing recovery time. As the utility sector continues to adopt these advanced technologies, the potential for cost savings, improved customer satisfaction, and enhanced service reliability has become more apparent. AI and ML are key to ensuring a more resilient and efficient future for utilities, particularly in the face of increasing environmental and cyber threats