International Journal of Engineering and Management Research

A Study of the Factors that Influence Cyber Bullying - Perspectives from Bullies

Atharv Aggarwal — 2024-06-01

Digitalisation has opened a lot many doors for people who wish to connect with their friends, relatives, colleagues irrespective of how distant they are physically. It is agreeable to an extent that digitalisation has benefitted a lot of people and help reduce their efforts, but it would be not right to talk about the darker side too. The study started with an initial study of available literature through papers published in Google Scholar, Google Books, and research articles. Accordingly, the research gap was found which helped to frame the research objectives- to identify the traits of bullies through available literature along with studying the reactions of victims and their level of awareness of cyberbullying. Initially it has been talked about the factors which might push a person to become a bully such as ego issues, social environment, upbringing etc. Moving further, various places where cyberbullying takes place has been brought into notice such as educational institutions, workplaces etc. By citing real-life incidents, various reactions of victims have been inferred and observation has also been made on the areas where such incidents have been taking place and how victims are responding to it.

In continuation to this, some findings have also been mentioned based on how globally widespread this heinous act has become. Lastly, to provide with the remedies, some information related to cyberlaws has been tapped and made the readers aware about it.

Fish Guard: A Holistic Approach to Automated Fish Farming with IoT and Image Processing

Senani B.R.K — 2024-06-03

The ornamental fish industry, which is important to the global economy, faces challenges that hinder its productivity and sustainability, particularly feeding practices, water quality management and disease control. This research introduces an integrated system designed to automate and optimize these aspects using the ESP32 microcontroller. Offers a lower cost and more effective, low-power solution with real-time processing capabilities. The proposed system has developed along four main lines. An automated feeding system that adapts to the food needs of fish, a water quality management system that monitors and controls critical parameters through machine learning algorithms, and a disease management system that uses image processing techniques for early detection. The systems were tested in controlled environments, showing significant improvements in nutrient efficiency, water quality stability and disease prevention. Our findings suggest that the integration of these technologies can significantly enhance the operational efficiency and sustainability of ornamental fish farms.

An Evaluation of Solar Photovoltaic System Depreciation Using PVSOL

Shweta Baviskar — 2024-06-04

This study provides a thorough examination of the design approaches for solar photovoltaic (PV) systems, utilizing the PVSOL software. Given the growing need for renewable energy sources, it is crucial to optimize solar PV installations. An in-depth analysis was conducted to determine the factors that influence the system's performance. These factors include the geographical location, shading effects, and module configurations. PVSOL is used to conduct a thorough analysis of these elements in order to assess their influence on the system's efficiency and amount of energy it produces. The study incorporates both theoretical analysis and practical simulations to offer significant insights into the design process. The results highlight the importance of meticulous design considerations in optimizing energy generation and system performance. This research enhances the development of solar PV technology by providing valuable information on the most effective ways to design optimal systems. This, in turn, helps to promote the shift towards sustainable energy alternatives.

Implementation of MQTT Protocol for Artificial Intelligence

Suraj Khot — 2024-06-04

The IoT-Based Attendance Management System is a sophisticated solution leveraging Azure services and MQTT protocol for efficient attendance tracking. The system comprises a Face Device equipped with facial recognition capabilities, which captures attendance data and communicates with Azure IoT Hub. The Azure IoT Hub, acting as the central hub, receives and processes attendance data from the face device, utilizing MQTT for real-time communication. A dedicated MQTT service, facilitated by the Mosquitto broker, subscribes to the Azure IoT Hub, ensuring seamless data flow between the face device and the broader system. The web application, hosted on Azure App Service and integrated with SQL Server, serves as the user interface. It not only interacts with the face device through Azure IoT Hub but also processes attendance data and generates comprehensive reports, enhancing the overall efficiency of attendance management through the power of IoT.

This architecture offers a scalable, secure, and real-time attendance management solution, bridging the physical and digital realms through facial recognition technology and Azure IoT services. The synergy of components, from the MQTT-enabled face device to the Azure-based web application, establishes a robust ecosystem for capturing, processing, and presenting attendance data with seamless integration and reliability.

Exploring DNA Methylation Biomarkers and Deep Learning for Cancer Epigenetics

Remyamol K M — 2024-06-06

Changes in DNA methylation, such as overall reduced methylation and increased methylation in specific CpG islands, are commonly seen in different types of cancer and also used as biomarkers to detect and diagnose cancer at an early stage. The distinct DNA methylation patterns provide valuable information about cancer progression and therapy. Recent advances in high-throughput techniques such as genome-wide profiling have transformed epigenetics by allowing computational analysis of intricate DNA methylation data. Deep learning techniques have become effective instruments for examining these patterns of methylation, enabling the identification of cancer markers, categorization of tumors, filling in missing data, and forecasting patient survival. This comprehensive review investigates the various uses of deep learning in examining DNA methylation and multi-omics data for cancer studies. It presents state-of-the-art deep learning architectures that are capable of addressing obstacles linked to research on cancer epigenetics. Nevertheless, the review also recognizes possible restrictions and areas for future investigation in this swiftly developing field. This work seeks to improve cancer diagnostics and therapeutic strategies by tackling these challenges and furthering knowledge of epigenetic mechanisms in cancer.

Electric Vehicle Battery Condition Monitoring System

Prof. Preeti S B — 2024-06-06

Most vital and expensive components of electric vehicles is the battery. Of course, the battery is the only source of electricity for an electric vehicle. However, the vehicle's power supply eventually declines, resulting in decreased performance. For battery manufacturers, this is a major concern. In this paper, it is proposed to use IoT approaches to monitor and display the battery performance.

Here, the various battery metrics, including voltage, current, and temperature, are tracked, observed, and shown. This alerts the user to prevent the battery from being overcharged or deeply discharged. With the application of various sensors, observation can be carried out. Data on voltage, current, and temperature are sent to a microcontroller unit, which subsequently transmits battery data via the cloud for display. Real-time data of voltage, current, and temperature may be displayed by the monitoring system, and the data can be seen on an Android smartphone and a computer at the same time. As a result, we might be able to increase the battery's efficiency and lifespan. The user interface and results presentation are the two main components of the proposed IoT-based battery monitoring system. According to test results, the system is able to recognize weakened performance of the battery and notifies the user for further use.

Make Scale Invariant Feature Transform “Fly” with CUDA

Yuhong Mo — 2024-06-07

This paper introduces an implementation of scale invariant feature transform (SIFT) algorithm with CUDA. Primary steps including building the Gaussian pyramid and the difference of Gaussian pyramid, identification, localization [1], and orientation generation of key-points are realized on GPU with CUDA. A detailed description of important kernel function implementations is covered along with optimizations made to achieve high performance, and a comparison between the CUDA version SIFT algorithm and a baseline sequential CPU implementation is included.

Sentiment Analysis of Social Media Data for Product and Brand Evaluation: A Data Mining Approach Unveiling Consumer Preferences, Trends, and Insights

Mahesh Prabu Arunachalam — 2024-06-26

Sentimental Analysis is an ongoing research field in Text Mining Arena to determine the situation of the market on particular entities such as Products, Services...Etc. This paper is a journal on sentiment analysis in social media that explores the methods, social media platforms used, and their application. It can be called a computational treatment of reviews, subjectivity, and sentiment. Social media contain a large amount of raw data that has been uploaded by users in the form of text, videos, photos, and audio. The data can be converted into valuable information by using sentiment analysis. We aim to collect details like Age, Gender, Education, Marital status, Salary, etc. So there requires data mining techniques like clustering. The Apriori Algorithm is the main algorithm used in our project. The Apriori algorithm is the general algorithm that can be used by developers according to their needs and implemented in their projects.

Mitigating Interoperability and Integration Hurdles in Cloud Computing Implementation: A Professional Perspective

Mahesh Prabu Arunachalam — 2024-06-27

Cloud computing is an option for organizations that do not intend to invest in internal IT resources. It offers a service model that assumes that the consumer has the means to manipulate information on the Internet according to their current needs. However, IT outsourcing presents various challenges, such as effective IT management, attention to threats from the Internet ecosystem, and concerns about efficient use of resources. Thus, the uncertainty associated with the transition to cloud computing can have a negative impact on the adoption of this technology. To better inform the decision-making process of organizations considering cloud computing, this study presents a literature-based list of key questions that can help IT managers guide an organization toward effective and secure adoption of cloud computing solutions. In addition, interviews were conducted with IT managers of companies using and providing cloud services to understand and prioritize these issues. The list takes into account the views of operators who have successfully experienced the transition to the cloud environment.

A Study on Enhancing Government Efficiency and Public Trust: The Transformative Role of Artificial Intelligence and Large Language Models

Hao Qin — 2024-06-29

This paper examines the transformative potential of Artificial Intelligence (AI), specifically Large Language Models (LLMs), in enhancing government efficiency and public sector service delivery. By integrating AI into various governmental functions such as automated administrative tasks, public safety, resource management, citizen services, policy development, and fraud detection, governments worldwide can significantly streamline operations, improve decision-making, and enhance citizen engagement. Detailed potential case studies from the United States’ IRS and local government agencies like SSA illustrate the successful implementation of AI, demonstrating its substantial benefits in operational efficiency and public satisfaction. The study concludes with strategic recommendations for further AI adoption, emphasizing the importance of robust governance, continuous technological investment, workforce training, and maintaining public trust. This research underscores AI's critical role in modernizing government functions and fostering a more responsive and inclusive public service landscape.