Browse Items (11810 total)
Sort by:
-
Alpha-Bit: An Android App for Enhancing Pattern Recognition using CNN and Sequential Deep Learning
This research paper introduces Alpha-Bit, an Android application pioneering Optical Character Recognition (OCR) through cutting-edge deep learning models, including Convolutional Neural Networks (CNNs) and Sequential networks. With a core focus on enhancing educational accessibility and quality, Alpha-Bit specifically targets foundational elements of the English language - alphabets and numbers. Beyond conventional OCR applications, Alpha-Bit distinguishes itself by offering guided instruction and individual progress reports, providing a nuanced and tailored educational experience. Significantly, this work extends beyond technological innovation; Alpha-Bit's potential impact encompasses addressing educational inequalities, contributing to sustainability goals, and advancing the achievement of Sustainable Development Goal 4 (SDG 4). By democratizing education through innovative OCR technologies, Alpha-Bit emerges as a transformative force with the capacity to revolutionize learning experiences, making quality education universally accessible and empowering learners across diverse socio-economic backgrounds. 2024 ITU. -
Convolutional Neural Networks for Automated Detection and Classification of Plasmodium Species in Thin Blood Smear Images
There has been a continued transmission of malaria throughout the world due to protozoan parasites from the Plasmodium species. As for treatment and control, it is very important to make correct and more efficient diagnostic. In order to observe the efficiency of the proposed approach, This Research built a Convolutional Neural Network (CNN) model for Automated detection and classification on thin blood smear images of Plasmodium species. This model was built on a corpus of 27558 images, included five Plasmodium species. Our CNN model got an overall accuracy of 96% for the cheating detection with an F 1score of 0.94. In the detection of the presence of malaria parasites the test accuracy conducted was as follows: 8%. Species-specific classification accuracies were: P. falciparum (95.7%), P. vivax (94.9%), P. ovale (93.2%), P. malaria (92.8%) and P. Knowles (91, 5%). As for the model SL was found to have sensitivity of 97.3% And the specificity in this case is 9 6. 1 %. The proposed CNN-based approach provides a sound and fully automated solution for malarial parasite detection and species determination, which could lead to better diagnostic performances in day-to-day practices. 2024 IEEE. -
Reinforcement Learning for Language Grounding: Mapping Words to Actions in Human-Robot Interaction
Within the domain of human-robot communication, effective communication is paramount for seamless and smooth collaboration between humans and robots. A promising method for improving language grounding is reinforcement learning (RL), which enables robots to translate spoken commands into suitable behaviors. This paper presents a comprehensive review of recent advancements in RL techniques applied to the task of language grounding in human-robot interaction, focusing specifically on instruction following. Key challenges in this domain include the ambiguity of natural language, the complexity of action spaces, and the need for robust and interpretable models. Various RL algorithms and architectures tailored for language grounding tasks are discussed, highlighting their strengths and limitations. Furthermore, real-world applications and experimental results are examined, showcasing the effectiveness of RL-based approaches in enabling robots to understand and execute instructions from human users. Finally, promising directions for future research are identified, emphasizing the importance of addressing scalability, generalization, and adaptability in RL-based language grounding systems for human-robot interaction. 2024 IEEE. -
Smart Satellites: Unveiling the Power of Artificial Intelligence in Space Communication-A Study
The incorporation of Artificial Intelligence (AI) into space and satellite communication represents a paradigm shift in the way we explore, navigate, and communicate beyond our planet. This article is about the impact of AI on satellite operations, and the broader field of its communication to the earth. The article explores how AI enhances spacecraft autonomy, mitigates signal degradation, and improves the overall reliability of communication performance Satellite communication benefits from AI-driven advancements, in the areas of signal processing and optimization. Furthermore, examines the integration of AI in space-based challenges and opportunities associated with large-scale satellite networks. AI playing a crucial role in detecting and mitigating cyber security threats in space communication systems. This paper comes up with the perception into the future trends and potential advancements in AI applications for space and satellite communication. 2024 IEEE. -
Managing with Machines: A Comprehensive Assessment on the Use of Artificial Intelligence in Organizational Perspectives
This complete study, delves into the multifaceted impacts of artificial Intelligence (AI) inside organizational settings, highlighting its ability and demanding situations. The investigation spans numerous aspects along with AI-driven customer relationship management (CRM), employee productivity, and overall performance enhancement thru AI. By analyzing distinct AI applications and methodologies across different organizational functions, this studies presents insights into how AI can transform industries, decorate CRM, improve employee productiveness, and foster sustainable development. Despite the promising programs, the study also addresses the pitfalls and enormous hesitancy in AI adoption due to disasters in some high-profile AI projects. The paper underscores the significance of strategic AI integration, context-consciousness, and the want for organizational readiness to leverage AI's full capability whilst aligning with the Sustainable improvement goals (SDGs). 2024 IEEE. -
Enhancing Dimensional Geometry Casting using Computer Modeling
Sand casting method is used to produce many useful products for many applications. The aim of the study is to manufacture a product with excellent dimensional geometry is achieved in sand casting process at low cost. We would expect manuscripts to show how design and/or manufacturing problems have been solved using computer modeling, simulation and analysis. In this work, the important mechanical properties of hardness and surface roughness are investigated on Aluminum 6063 cast material with and without incorporating the copper tubes as a vent hole in sand casting process. Since copper has high thermal conductivity when compared to other metals, the heat transfer rate will be varying from existing system. The copper tubes have made different diameters of holes on outer surfaces with selective distance of intervals. The specific number of copper tubes with various diameters are designed by CATIA modeling software and analyzed with Taguchi Design of Experiment. Taguchi L9 orthogonal array is used proficiently in the optimal value of hardness and surface roughness. The results are revealed that the maximum hardness value of 104 BHN is attained for 10mm distance of holes made on copper tube with an angle of 90o degree. The minimum surface roughness of 2.11 micron is achieved for 20mm distance of holes made on copper tube with 45o of angle as a vent hole in sand casting process. 2024 E3S Web of Conferences -
Designing a Precision Seed Sowing Machine for Enhanced Crop Productivity
A seed sowing machine is a valuable agricultural device that facilitates the precise and efficient sowing of seeds in fields. When designing and optimizing such a machine, several crucial factors need consideration including seed size, seed rate, soil type, and field conditions. The primary objective is to achieve uniform seed distribution and optimal seed-to-soil contact, which can be accomplished by incorporating a seed metering mechanism to control the seed rate accurately. Versatility is another important aspect of the machine's design, as it should be able to handle different seed sizes, types, soil conditions, and field variations. To achieve this, utilizing advanced technologies such as sensors, automation, and precision farming techniques can significantly enhance the machine's performance and efficiency while also reducing costs and minimizing environmental impact. The optimization of a seed sowing machine plays a crucial role in ensuring successful crop production. By implementing cutting-edge technologies and precision farming techniques, farmers can increase their yields and decrease the amount of seed and fertilizer needed for a specific area. Ultimately, this leads to improved productivity, increased profitability, and a more sustainable approach to agriculture. 2024 E3S Web of Conferences -
Surface Roughness Analysis in AWJM for Enhanced Workpiece Quality
Abrasive Water Jet Machining is a distinctive manufacturing process that effectively removes material from a workpiece by employing a high-pressure stream of water combined with abrasive particles. The final quality of the machined surface is directly influenced by various process parameters, such as the traverse speed, hydraulic pressure, stand-off distance, abrasive flow rate, and the specific type of abrasive used. In recent times, extensive research has been undertaken to enhance the performance of AWJM, with a specific focus on critical performance measures like surface roughness. This paper presents the latest advancements in AWJM research, with particular attention given to enhancing performance measures, implementing process monitoring and control, and optimizing process variables for applications involving high-carbon steel. 2024 E3S Web of Conferences -
Mechanical Properties of FSW Joints Magnesium Alloy at Different Rotational Speeds
Magnesium (Mg) has become a focus in the transportation industry due to its potential in reducing fuel consumption and gas emissions while improving recyclability. Mg alloys are also known for their low neutron absorption, good resistant of carbon dioxide as well as thermal conductivity which makes them suitable for use in industrial equipment for nuclear energy. there has been an increasing interest in the research and development of Mg alloys. These are the lightest of all metallic structural materials and are approximately 33% lighter than aluminium (Al) and 75% lighter than ferrous (Fe) alloys and have excellent specific mechanical properties. In this work, FSW of AZ31B Alloy was examined at the various rotational speeds of 900 -1440 rpm, with fixed welding speed of 40mm/min and 2 tool tilt angle using an HSS tool. The mechanical properties were compared for the different rotational speeds. The quality of FSW joints is dependent on input value of heat and material flow rate, which are prejudiced by process parameters., higher rotation speeds may cause abnormal stirring, resulting in a tunnel defect at the weld nugget due to increased strain rate and turbulence. 2024 E3S Web of Conferences -
Stir Speed and Reinforcement Effects on Tensile Strength in Al-Based Composites
This study focuses on the preparation of Al-based hybrid composites using AA7475 as the main alloy reinforced with two materials, ZrO2 and SiC. The combination of stir-squeeze processing techniques was employed to create various specimens by varying four parameters: Stir-speed, Stir-time, reinforcements, and squeeze pressure. Taguchi design was utilized to generate specimens for analyzing their mechanical properties, specifically tensile strength, hardness, and porosity.The results indicated that the highest porosity (4.44%) was observed in the L16 test, with a combination of 700rpm stir speed, 25 mins stir time, 2wt% reinforcements, and 80MPa squeeze pressure. On the other hand, the lowest porosity (2.61%) was found in the L7 test, with 800rpm stir speed, 30 mins stir time, 2wt% reinforcements, and 100 MPa squeeze pressure.Regarding tensile strength (UTS), the maximum value (285.23MPa) was achieved in the L13 experiment, while the minimum value (187.58 MPa) was observed in the L1 experiment. This variation in UTS can be attributed to the applied load, the strengthening effect of the reinforcements, and the grain size of SiC. 2024 E3S Web of Conferences -
Integration of enterprise resource planning system as an effective technology for increasing business productivity
Enterprise Resource Planning (ERP) refers to a potential software, which organisations utilise for managing daily basis activities such as proper accounting, project management, compliance as well as procurement actions within organisational standards for achieving better business performance. This research focuses on understanding ways of ERP usage of businesses for enhancing potential procurement as well as accounting for assuring best performance achievement. Literature from different company reports and other sources has been implemented that brings out an understanding of productivity optimisation of organisations using ERP. It also focuses on illustrating different types of ERP along with assuring better data visibility aspects of the ERP usage for allowing consumers to view real time data while progressing with business relationships and enabling higher procurement standards. The research aims to investigate ways in which different types of ERP are used by organisations for assuring better accounting performance and procurement standards in their marketing environment. Hypothesis is a positive association between ERP utilisation and implementation in organisation and its accounting and procurement standards, achieving high performance in the competitive market. Methodology used in this research involves Exploratory research design with a probability sampling for bringing out best possible outcomes of the research. Sample sizes include secondary sources such as articles, journals and relevant company reports and databases for understanding ways in which ERP helps in attaining suitable accounting and procurement practices of businesses within organisational standards. Results as well as implications indicate an optimal relation of proper risk management through enhancing ERP and usage of most suitable ERP that assures best possible procurement and accounting practices for businesses to get competitive advantage in the market. 2024 Author(s). -
Restrained geodetic domination in the power of a graph
For a graph G = (V,E), S ? V(G) is a restrained geodetic dominating set, if S is a geodetic dominating (gd) set and never consists an isolated vertex. The least cardinality of such a set is known as the restrained geodetic domination (rgd) number. The power of a graph G is denoted as Gk and is obtained from G by making adjacency between the vertices provided the distance between those vertices must be at most k. In this study, we discussed geodetic number and rgd number of Gk. 2024 Author(s). -
Finding Real-Time Crime Detections during Video Surveillance by Live CCTV Streaming Using the Deep Learning Models
Nowadays, securing people in public places is an emerging social issue in the research of real-Time crime detection (RCD) by video surveillance, in which initial automatic recognition of suspicious objects is considered a prime problem in RCD. Dynamic live CCTV monitoring and finding real-Time crime activities by detecting suspicious objects is required to prevent unusual activities in public places. Continuous live CCTV video surveillance of objects and classification of suspicious activities are essential for real-Time crime detection. Deep training models have greatly succeeded in image and video classifications. Thus, this paper focuses on the use of trustworthy deep learning models to intelligently classify suspicious objects to detect real-Time crimes during live video surveillance by CCTV. In the experimental study, various convolutional neural network (CNN) models are trained using real-Time crime and non-crime videos. Three performance parameters, accuracy, loss, and computational time, are estimated for three variants of CNN models for the real-Time crime classifications. Three categories of videos, i.e., crime video (CV), non-crime video (NCV), and weapon-crime video (WCV), are used in the training of three deep models, CNN, 3D CNN, and Convolutional Long short-Term memory (ConvLSTM). The ConvLSTM scored higher accuracy, lower loss values, and runtime efficiency than CNN and 3D CNN when detecting real-Time crimes. 2024 ACM. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024. -
Machine Learning-Driven Energy Management for Electric Vehicles in Renewable Microgrids
The surge in demand for sustainable transportation has accelerated the adoption of electric vehicles (EVs). Despite their benefits, EVs face challenges such as limited driving range and frequent recharging needs. Addressing these issues, innovative energy optimization techniques have emerged, prominently featuring machine learning-driven solutions. This paper reviews work in the areas of Smart EV energy optimization systems that leverage machine learning to analyse historical driving data. By understanding driving patterns, road conditions, weather, and traffic, these systems can predict and optimize EV energy consumption, thereby minimizing waste and extending driving range. Concurrently, renewable microgrids present a promising avenue for bolstering power system security, reliability, and operation. Incorporating diverse renewable sources, these microgrids play a pivotal role in curbing greenhouse gas emissions and enhancing efficiency. The review also delves into machine learning-based energy management in renewable microgrids with a focus on reconfigurable structures. Advanced techniques, such as support vector machines, are employed to model and estimate the charging demand of hybrid electric vehicles (HEVs). Through strategic charging scenarios and innovative optimization methods, these approaches demonstrate significant improvements in microgrid operation costs and charging demand prediction accuracy. The Authors, published by EDP Sciences, 2024. -
Advancements in Solar-Powered UAV Design Leveraging Machine Learning: A Comprehensive Review
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques. We delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology. The Authors, published by EDP Sciences, 2024. -
A Review on Condition Monitoring of Wind Turbines Using Machine Learning Techniques
This document examines the most up-to-date research on the application of machine learning (ML) techniques in monitoring the conditions of wind turbines. The focus is on classification methods, which are used to identify different types of faults. The analysis revealed that the majority of the research utilizes Supervisory Control and Data Acquisition (SCADA) information, with neural networks, support vector machines, and decision trees being the most prevalent machine learning algorithms. The review also identifies several areas for future research, such as the development of more robust ML models that can handle noisy data and the use of ML methods for prognosis (predicting future faults). The Authors, published by EDP Sciences, 2024. -
Predicting Wind Energy: Machine Learning from Daily Wind Data
This paper offers a comprehensive review of the advancements in the realm of renewable energy, specifically focusing on solid oxide fuel cells and electrolysers for green hydrogen production. The review delves into the significance of wind energy as a pivotal renewable energy source and underscores the importance of precise forecasting for efficient energy management and distribution. The integration of machine learning-based approaches, such as Support Vector Regression and Random Forest Regression, has shown promising results in enhancing the accuracy of wind energy production forecasts. Furthermore, the paper explores the broader landscape of renewable energy generation forecasting, emphasizing the rising prominence of machine learning and deep learning techniques. As the penetration of renewable energy sources into the electricity grid intensifies, the need for accurate forecasting becomes paramount. Traditional methods, while valuable, have encountered limitations, paving the way for advanced algorithms capable of deciphering intricate data relationships. The review also touches upon the inherent challenges and prospective research avenues in the domain, including addressing uncertainties in renewable energy generation, ensuring data availability, and enhancing model interpretability. The overarching goal remains the seamless integration of renewable sources into the grid, propelling us towards a greener future. The Authors, published by EDP Sciences, 2024. -
Interactions between emotional and spiritual intelligence and their effects on employee performance
The association among worker behavior, spiritual intelligence, emotional intelligence, and system effectiveness is explained by this study. Understanding how others communicate and being aware of how one's own emotions affect others around you are all characteristics of emotional intelligence. Spiritual intelligence, which is a higher level of intelligence, reveals one's actual attributes and abilities. As company's most asset, the effectiveness of employee behavior has a significant impact on the company's ability to survive and thrive. In contrast to other facets of human conduct, employee conduct is distinguished by more formal behavior. This study aims to determine whether those with emotional and spiritual intelligence perform well at work. This research also aims to comprehend the behavior of emotionally intelligent and spiritually inclined people at work. Attempts are made in this study to ascertain whether higher levels of spiritual and emotional intelligence might boost the efficacy of these abilities. In this study, productivity at work is the dependent variable, whereas emotional intelligence and spiritual intelligence are independent variables. The parameters that can assess the variables were established using a literature review and a few common surveys. An organized survey that considers the variables is developed to gather information from the working class. To determine the link between the variables chosen for this study, the gathered data was analyzed using statistical approaches such as partial correlation and correlation. 2024 Author(s). -
Machine Learning Integration for Enhanced Solar Power Generation Forecasting
This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limitations of conventional machine learning algorithms that rely heavily on vast data. The PC-LSTM model showcases superior forecasting capabilities, especially with sparse data, outperforming standard LSTM and other traditional methods. Furthermore, the paper examines a comprehensive study from Morocco, comparing six machine learning algorithms for solar energy production forecasting. The study underscores the Artificial Neural Network (ANN) as the most effective predictive model, offering optimal parameters for real-world applications. Such advancements not only bolster the accuracy of solar energy forecasting but also pave the way for sustainable energy solutions, emphasizing the integration of these findings in practical applications like predictive maintenance of PV power plants. The Authors, published by EDP Sciences, 2024.