Volume 12 | Issue 4

 Abstract

Nowadays, demands for the renewable energy resources are increasing significantly. The most popular ones are wind energy and solar energy resource. But the wind energy has a lower installation cost compared to the solar energy. Wind energy is one of the most prominent and developed renewable energy resources. A power electronic interface is needed in order to connect a Wind Energy Conversion System (WECS) to the load. The output power of wind Energy system varies depend on the wind speed. Due the nonlinear characteristic of the wind turbine, it is a challenging task to maintain the maximum power output of the wind Turbine for all wind speed conditions. This can be overcomed by implementing MPPT (Maximum Power Point Tracking). MPPT plays a crucial role in enhancing the efficiency and performance of WECS. Furthermore, recent advancements in MPPT algorithms, including perturb and observe (P&O), incremental conductance, Cuckoo Search and Fuzzy logic control (FLC), are discussed in detail. The abstract explains the insights into future research directions aimed at further enhancing the MPPT efficiency and reliability in wind energy systems, thus contributing to the sustainable development of renewable energy sources.

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 Keywords

Matlab, Wind energy conversion system,Peturb and Observe, Incremental conductance, Cuckoo search algorithm,Fuzzy logic control

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 Abstract

Because of their cost-effectiveness and abundance of computing resources, enterprises from many industries now use cloud services for efficient data storage and administration. However, this technique raises worries regarding data security since sensitive information is handed to third-party cloud servers, which are subject to unauthorized access by internal workers or hostile hackers. To address these security concerns, encryption methods such as AES, RSA, and DES have been created, preserving data confidentiality by encrypting information prior to storage on the cloud. This suggested study proposes Elliptic Curve Cryptography (ECC) as an alternate encryption strategy for protecting data in cloud environments. Unlike older methods, ECC provides a lightweight solution for key creation and maintenance, requiring less computing time and resources. This study provides a detailed comparison of ECC and the widely used AES algorithm, with a focus on encryption time performance. Experimental results show that ECC beats AES, delivering quicker and more efficient encryption procedures, lowering cloud use costs. The findings of this study help to advance the area of cloud data security by providing a potential answer to enterprises seeking comprehensive protection for sensitive information in an ever-changing digital context.

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 Keywords

Cloud Computing ,Cryptography ,Elliptic curve cyptography algorithm,security

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 Abstract

According to the portfolio assessment technique if a teacher or mentor assessed every child with special needs (CWSN) through total learning management and provide diverse, expressive, action-oriented, collaborative, participatory, enjoyable learning experience to achieve the milestones of the student's learning progress, he can reach the expected level of learning, even if it is a child with special needs. The portfolio cognitive assessment technique is no doubt helpful to the teachers to achieve a comprehensive picture of all abilities of students (CWSN) i.e. level of understanding, level of application, skill ability, social commitment and outstanding performance. It is also in National Education Policy 2020, as the 360-degree holistic assessment technique.

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A Study of the Effects on the Achievement of CWSN (Children with Special Needs) of the Teaching Program Based on Portfolio Assessment Techniques

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In response to the increasing need for informative and expressive stock market forecasting, our research, "Enhancing Stock Market Forecasting: A Machine Learning Approach with Historical Data Analysis," aims to elevate predict the future stock values. Time collection forecasting has been extensively used to decide the future fees of inventory, and the analysis and modelling of finance time collection importantly manual investors' selections and trades. The proposed model carries sliding-window optimization and features a person-friendly graphical interface, providing a stand-alone application that indicates promise in predicting the complex patterns of especially non-linear time collection information, surpassing conventional models. Additionally, our model incorporates a 7-day prediction feature, allowing users to forecast stock prices for the upcoming week.

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 Keywords

Linear Regression, Long Short-Term Memory (LSTM), Random Forest, Arima Algorithm

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 Abstract

In metropolitan environments, traffic congestion caused by heavy-load vehicles poses serious obstacles to safe and effective transit. This paper brings a novel method for identifying heavy-load vehicles such as trucks and huge vans uses CCTV images and vehicle density analysis. The system uses the YOLO version 8 algorithm in combination with the programming framework Python and tools like PyTorch, OpenCV and Deep SORT to identify heavy-load automobiles in real-time and provide optimised routes to avoid traffic bottlenecks. By using YOLO algorithms and vehicle density analysis this system distributes heavy-load trucks systematically along the road networks, minimizing traffic congestion. The transportation management system also enhances overall traffic control by penalising transgressions and ensuring lane conformance. This creative approach has the potential to improve transportation efficiency and mitigate urban traffic congestion.

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 Keywords

Heavy load vehicles, Traffic congestion, YOLO algorithm, Route optimization, CCTV analysis, Urban transportation efficiency.

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 Abstract

Railway infrastructure is a vital part of global transportation networks, enabling the efficient movement of people and goods. However, the safety and reliability of railway tracks are crucial for maintaining these networks. The research proposes a Deep learning-based method for identifying and localizing railway track cracks, a major concern in railway maintenance. The method uses two advanced Deep learning models, YOLOv5 for object identification and Efficient Net for classification tasks. The diversified dataset allows the model to perform better in real world situations and learn and generalize faster. The system classifies and finds cracks with great accuracy. And plots the locations of discovered cracks using geospatial coordinates for better comprehension and visualization. Transfer learning approaches improve the model's resilience and adaptability to new and untested data. The system's performance is evaluated through extensive trails and comparisons, showing significant improvements in precision, effectiveness and dependability, underscoring its potential for improving railway track maintenance and security.

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Deep learning, YOLOv5, Efficient Net, Classification, Crack identification, Coordinate Mapping.

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 Abstract

Potholes can drastically impair driving and road performance. In 2018, 2019, and 2020, there were 2,015, 2,140, and 1,471 fatalities from road accidents caused by potholes, according to data from the Ministry of Road Transport and Highways (MoRTH). Potholes caused 4,775 incidents in 2019 and 3,564 accidents in 2020, respectively. Many researchers and transportation experts have directed their attention toward developing pothole maintenance techniques that work. Our requirement is for a pothole filling equipment that is long-lasting, economical, and requires minimal human labour. The objective of this project is to develop and construct a prototype for the Automatic Pothole Filling Robot, an automated road maintenance vehicle. Without assistance from an operator, it is capable of automatically locating and fixing potholes on road surfaces. A straightforward mechanical technique was created to find potholes. It assists in reducing the expenses and complexity, which up to now have been the primary disadvantages of autonomous vehicles used for road maintenance. The breadth and depth of the pothole are measured and detected using ultrasonic sensors. The pothole will be automatically filled by the robot

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NA

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 Abstract

The analysis of flow that has been suggested in this work addresses both its potential and the problems it tended to cause. We achieved the 94.52% accuracy using random forest algorithm.

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Random Forest, Crop Prediction System

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The main idea behind this project is to use the Arduino Mega microcontroller as the brains behind a complete car safety and monitoring system. It acts as the cornerstone, coordinating a wide range of operations meant to improve the security and safety of vehicles. The study focuses on two important areas: face recognition for car door control and automobile accident detection. The system's ability to detect the vehicle's closeness to designated speed limit zones and automatically reduce speed when necessary is enhanced with the addition of RSSI (Received Signal Strength Indicator) technology. A vibration sensor is used for accident detection, quickly detecting any impacts or accidents and initiating a sequence of emergency response procedures. In case of mishap, the GPS module activates, it quickly sends the exact location of the car via the GSM module, alerting and accelerating the rescue team's response.

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Accident detection, Arduino Mega microcontroller, face recognition, Received Signal Strength Indicator (RSSI), vibration sensor, GSM, GPS.

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 Abstract

This paper aims to provide an approach towards Hala Alyan's novel Salt Houses as a text that converges together the elements of living in Diaspora. However, the novel is a take on struggle between history and fiction, which creates the climax of resistance of the Palestinians living in or out of Palestine. Diaspora has been a long debated subject matter in the twentieth century as well as the twenty first century, major displacements due to war, forced exile of minorities and land confiscation. The peculiarity of a Palestinian family being dispersed into different parts of the world because of the illegal Israeli occupation on their homeland, while Alyan's storytelling captures the reader's mind to emotionally capture the atrocities they go through in their life and also her focus on portraying the family in the limelight to initiate the plight of the Palestinians in order to convey how Palestinians are denied justice

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 Keywords

displacement, exile, resistance, struggle, justice

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 Abstract

Abstract: - Every organization is interested in creating high performing workplaces where there is a pervasive performance work culture, people perform because they would like to perform better, and the organizational policies and practices help them in aligning their individual goals to organizational goals. High Performance Work Systems have been increasingly important in commercial rivalry in recent decades. This study seeks to evaluate how high-performance work systems contribute to organizational commitment when business environment change is the principal cause of external challenges. This study is useful for practitioners because it acknowledges the benefits of High-Performance Work Systems action for organizations and how it can be a source of organizational commitment for the organization. This document provides significant guidance to general managers and human resource managers in maintaining High-Performance Work Systems (HPWS) in order to achieve and maintain organizational commitment. This research looks at how HPWS impacts organizational commitment and the primary characteristics that drive organizational commitment, such as work satisfaction, leadership style, and organizational environment. The purpose of this paper is to list all of the aspects that influence organizational commitment. This compilation assists HR managers in implementing, ensuring, and monitoring the elements that influence organizational commitment. As a result, they can retain and improve employee performance and company efficiency. The primary elements impacting job satisfaction include the working environment, working conditions, compensation management, promotion opportunities, job security, relationship with manager, relationship with coworkers, and management-employee connection. Employees will be more dedicated to their organization if their leaders exhibit transformational leadership behaviors. Organizational commitment is influenced by organizational environment dimensions such as training and development, communication satisfaction, performance assessment, and employee empowerment.

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 Keywords

Keywords: Promotion opportunities, Compensation management, Organizational commitment, High-Performance Work Systems, Organizational climate.

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 Abstract

Significant cybersecurity challenges have been caused by the development of smart goods due to the recent exponential rise in attack frequency and complexity. Although the huge developments that cloud computing has brought to the corporate sector, because of its centralization, using distributed services like security systems may be difficult. Due to the large amount of data that is sent between companies and cloud service providers, both maliciously and accidentally, valuable data breaches may occur. The malicious insider becomes a crucial threat to the organization since they have more access and opportunity to produce significant damage. Unlike outsiders, insiders possess privileged and proper access to information and resources. So, we proposes a machine learning-based system for insider threat detection and classification, which identifies various anomalous occurrences that may point to anomalies and security problems associated with privilege escalation. Multiple studies have been presented regarding detecting irregularities and vulnerabilities in network systems to find security flaws or threats involving privilege escalation. But these studies lack the proper identification of the attacks. We conclude that incorporating more than one machine learning algorithm can obtain a stronger classification in multiple internal attacks.

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 Keywords

Security, Machine Learning algorithms, Cloud Computing, Data models.

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This study shows the Inventory management stands as a vital aspect of operational efficiency and financial health across industries. This research delves into the intricate relationship between inventory management practices and business profitability. The analysis encompasses theoretical frameworks, such as just-in-time (JIT) inventory management, economic order quantity (EOQ), ABC analysis, which are instrumental in minimizing costs and optimizing inventory levels. The study scrutinizes the impact of inventory turnover rates on profitability metrics like return on investment (ROI) and gross margin. This study examines the challenges confronting businesses in managing inventories effectively, including demand volatility, stockouts, and supply chain disruptions The methodology explored is that descriptive statistics with a sample size of 110.Primary Data and secondary data are used to analyze the tools like percentage analysis Correlation and chi square test. The majority of the findings are taken from primary data. The research illuminates opportunities for enhancing inventory management efficiency. The outcomes of this study shows the actionable recommendations for businesses, encompassing strategies for lean inventory practices, technology adoption, supplier relationship optimization, and performance metrics tracking. This project concludes it is essential for businesses to prioritize investments in inventory managements practices and embrace technological advancements to drive sustainable growth and success.

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 Keywords

Inventory management, operational efficiency, Inventory turnover rates, Profitability metrics

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 Abstract

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Stock Price Prediction, Machine Learning Models, Sentiment Analysis, Root Mean Square Error (RMSE), Hybrid Techniques

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 Abstract

Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are used in different area of research for monitoring, collecting and analysis data from remote locations. Due to the vast increase in global industrial output, rural to urban drift and the over-utilization of land and sea resources, the quality of water available to people has deteriorated greatly. The high use of fertilizers in farms and also other chemicals in sectors such as mining and construction have contributed immensely to the overall reduction of water quality globally. Water is an essential need for human survival and therefore there must be mechanisms put in place to vigorously test the quality of water that made available for drinking in town and city articulated supplies and as well as the rivers, creeks and shoreline that surround our towns and cities. The availability of good quality water is paramount in preventing outbreaks of water-borne diseases as well as improving the quality of life. Fiji Islands are located in the vast Paci?c Ocean which requires a frequent data collecting network for the water quality monitoring and IoT and RS can improve the existing measurement. This paper presents a smart water quality monitoring system for Fiji, using IoT and remote sensing technology.

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Smart Water Quality Monitoring; Internet of Things; Remote Sensing.

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 Abstract

It is the rapid changes in the education system and teaching methods that affect students. This situation requires students to learn more effectively and learn more independently (Winters, Greene, & Costich, 2008). To achieve this goal, students need to be trained to improve their skills to select the most appropriate learning strategy (Azevedo and Cornley, 2004). If this is not done correctly, it will affect students' motivation to learn and may eventually cause them to lose interest in the subjects they are learning. The motivation of students to be open-minded is also an important aspect in statistics teaching. Therefore, motivation is essential to successfully master the challenges of the learning environment. Could behaviors important for academic motivation be key to students' ability to complete difficult tasks and remain in difficult situations for extended periods of time? The ability to face the challenges of everyday school life. The purpose of this article is to highlight the actual statistical problems faced by the students of Secondary School, Anand Town, so that the students' problems can be solved systematically for the benefit of the teacher, students and parents. This study provides a logical explanation to identify the real problems and challenges faced by students related to learning statistics and how teachers and parents have a great impact on a child's learning. Keywords: statistics, high school, student, teacher.

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statistics, Secondary School, Students, Teachers.

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 Abstract

Gold loans have gained significant popularity as a form of secured lending, providing individuals with quick access to funds against their gold assets. This paper presents a comparative analysis of gold loan products offered by private sector banks, public sector banks, and non-banking financial companies (NBFCs). The study examines various parameters such as interest rates, loan-to-value ratios, loan processing times, documentation requirements, customer service quality, and repayment options across these three categories of financial institutions. By synthesizing both quantitative data and qualitative insights, this research aims to provide a comprehensive understanding of the strengths and weaknesses of gold loan offerings from different types of lenders. The findings of this study can serve as a valuable resource for borrowers seeking gold loans and also assist financial institutions in refining their product offerings and service delivery.

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Gold loan, private sector banks, public sector banks, non-banking financial companies, interest rates, loan processing, documentation requirements, customer satisfaction, repayment options.

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 Abstract

Acne is a skin condition that occurs when the hair follicles underneath the skin become blocked. Chamomile oil is a better treatment option for acne. Microspheres are made of synthetic and biodegradable polymers is a key to delivering the drug to the site of treatment in a controlled manner. The purpose of this is to formulate and evaluate chamomile microsphere loaded cream for enhanced topical delivery. The ionotropic gelation technique used to prepare the microsphere of Chamomile in which sodium alginate is used as polymer and calcium chloride as cross linker. The particle size and the entrapment efficiency of the microsphere formulation M1 and M2 was a consideration when evaluating it. The microsphere formulation that was optimized i.e M1 was put into cream that contained neem extract. It is found that, as polymer concentration increases particle size and entrapment efficiency also increases. Based on this study, it was concluded that the cream filled with Chamomile microspheres meets all the requirements of dosage forms that release the active ingredient in a controlled manner and studies encourage further clinical follow up and long term stability studies with this formulation.

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Chamomile, Microsphere, Acne, Cream, Polymer.

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 Abstract

In the rapidly evolving landscape of electronic commerce (e-commerce), customer satisfaction plays a pivotal role in determining the success and sustainability of online businesses. This abstract provides an overview of a comprehensive study aimed at understanding and improving customer satisfaction within the e-commerce domain. The research explores the multifaceted factors influencing customer satisfaction in e-commerce, considering elements such as website usability, product quality, delivery efficiency, customer service, and the overall user experience. A combination of quantitative and qualitative research methods, including surveys, interviews, and data analytics, is employed to gather insights from a diverse sample of online shoppers. Key findings reveal the significant impact of website design and functionality on customer satisfaction, emphasizing the importance of user-friendly interfaces, seamless navigation, and secure transactions. Additionally, the study investigates the role of personalized recommendations, customer reviews, and social proof in shaping purchasing decisions and satisfaction levels. Logistics and order fulfilment emerge as critical components affecting customer satisfaction, with a focus on timely deliveries, transparent tracking systems, and hassle-free return processes. Effective customer service, both pre- and post-purchase, is identified as a key determinant of overall satisfaction, highlighting the need for responsive communication channels and issue resolution mechanisms. The research identifies emerging trends and technologies, such as artificial intelligence and chatbots, that have the potential to further enhance customer satisfaction in e-commerce. The insights from this study contribute to the development of actionable strategies for e-commerce businesses to optimize their operations, foster customer loyalty, and ultimately thrive in a competitive digital marketplace. As e-commerce continues to redefine the retail landscape, understanding and addressing the factors influencing customer satisfaction are essential for businesses seeking to build long-lasting relationships with their online clientele. This research aims to provide valuable insights and practical recommendations to empower e-commerce enterprises to deliver exceptional customer experiences and maintain a competitive edge in the dynamic digital marketplace.

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 Keywords

Investment goals,risk tolerance,investment style

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 Abstract

The ability to make excellent product recommendations to users is critical for improving user experience and boosting business success in the quickly changing e-commerce industry. The goal of this project is to give consumers personalized product recommendations by designing and implementing a cloud-based e-commerce recommendation system model. By utilising sophisticated recommendation algorithms, such as content-based and collaborative filtering, in conjunction with expandable cloud infrastructure, the system effectively handles massive amounts of data to produce precise recommendations. Scalability, performance, and security are guaranteed by the system architecture, which includes cloud deployment, preprocessing, recommendation engines, and data gathering. The system's efficacy in enhancing user engagement and conversion rates is demonstrated through thorough assessment and testing. The results of this study give a basis for the development of recommendation systems in e-commerce.

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 Keywords

E-commerce, Cloud Computing, Recommendation System

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