Volume 12 | Issue 4 |

 Abstract

Training and development play a key role in the existence and growth of any organization.. Employees are the most valuable asset of an organization, so to enhance their performance it is necessary to pay attention to their learning. Training and development programs help organizations to build a skilled and competent workforce in order to maintain a high level of competency and to survive in a dynamic business environment. This study was conducted with the aim to investigate the effectiveness of training and development on employee performance at Dixon Technologies(india) Ltd.Tirupati.The research employed descriptive analysis. Primary data was collectedthrough ordering the questionnaire by direct contact and also involved in personal discussions to obtain insights by distributing questionnaires to 117 employees, who were selected through the Convenience sampling technique. Findings reveal that overall training and development has a significant impact on employee's performance. It helps the organization in personality Development, increasing the productivity of employees, and contributing to higher growth and Development for the organization. The study suggests that to highlight the problems of staff training and development in the company and also to offer suggestions for better training and Developmnt programs.

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 Keywords

Employee Training, learning, skills and knowledge, skill enhancement

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Computing ,Cryptography ,Elliptic curve cyptography algorithm,security

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition

 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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 Keywords

NA

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Creative Commons Attribution 4.0 and The Open Definition

 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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Creative Commons Attribution 4.0 and The Open Definition

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Random Forest, Crop Prediction System

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Creative Commons Attribution 4.0 and The Open Definition

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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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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

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

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Creative Commons Attribution 4.0 and The Open Definition