International Journal of Engineering and Management Research

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

2024-06-03T14:49:45+00:00

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

2024-06-03T15:17:04+00:00

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

2024-05-16T15:08:55+00:00

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

2024-05-29T11:08:19+00:00

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

2024-06-03T05:27:16+00:00

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

2024-05-27T07:20:33+00:00

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

2024-06-04T05:21:35+00:00

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

2024-06-26T06:19:03+00:00

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

2024-06-27T05:44:25+00:00

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

2024-07-02T05:12:47+00:00

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.

Editable Neural Radiance Fields Convert 2D to 3D Furniture Texture

2024-07-05T07:16:06+00:00

Our work presents a neural network designed to convert textual descriptions into 3D models. By leveraging the encoder-decoder architecture, we effectively combine text information with attributes such as shape, color, and position. This combined information is then input into a generator to predict new furniture objects, which are enriched with detailed information like color and shape.[1] The predicted furniture objects are subsequently processed by an encoder to extract feature information, which is then utilized in the loss function to propagate errors and update model weights. After training the network, we can generate new 3D objects solely based on textual input, showcasing the potential of our approach in generating customizable 3D models from descriptive text.[2]

A Bibliometric Analysis of Sustainable Solid Waste Management Technologies using Scopus Database

2024-07-08T07:08:37+00:00

A bibliometric analysis of sustainable solid waste management (SSWM) technologies was conducted to establish the hotpots and research shifts based on literature from Science Citation Index (SCI) database from 2010 to 2023 that was retrieved from Scopus database. The research trends and statistics are presented first and then a comprehensive bibilometric analysis using VOSviewer software is performed. The research establishes that, publication output increased between 2020 and 2022 and the Journal of Cleaner Production had the highest number of publications. Between 2017 and 2019 the focus of research on SSWM technologies was towards waste management and sustainable development. The technologies considered during this period were recycling, waste incineration, gasification, anaerobic digestion, and waste to energy, bioenergy and composting. Between 2020 and 2023 the focus was on environmental sustainability and circular economy. The SSWM technologies between 2020 and 2023 focused on resource recovery and pyrolysis.

Image Text to Speech Conversion using Optical Character Recognition Technique in Raspberry PI

2024-07-09T06:40:39+00:00

Optical Character Recognition (OCR) is a subset of artificial intelligence and is a subset of computer vision. Optical Character Recognition (OCR) is the use of Raspberry Pi to convert scanned bitmap images of handwritten or written text into audio performance. OCRs designed for a variety of world languages are now in use. In this method the context subtraction method based on the Gaussian mixture is used to recover the area of the moving object. For text content, the function of text localization and recognition is used. The text localization algorithm and the Tesract algorithm and edge pixel distributions based on the gradient properties of the stroke directions were used to automatically translate text areas from the object in the Ada enhancement model. In the translated text areas text characters are converted to binaries, which OCR software understands. For the blind, known text symbols are strongly pronounced. The potential of the algorithm for the proposed text location. The text file describes the character codes using the Raspberry system, which recognises the characters by using Tesract's and Python, and the audio output is heard in the recognition step.

Solar Powered Garbage Management System

2024-07-10T06:29:19+00:00

India is a developing nation. With this development the population is also increasing day by day, with this increment in population the problems like scarcity of land, power requirement & waste management is also increased. Among all these issues waste management is leading particularly in urban areas. This paper proposes the solar Garbage Collecting Vehicle. The mechanical arrangement can be utilized for isolating wet and dry waste into independent compartments. The proposed system is sustainable as it is powered by solar panels. The key components used in this system are a Solar panel, IR sensor, moisture sensor, and ultrasonic sensor. The proposed system comprises novelty features like Waste Segregation, eco-friendly fueling, etc.

On A Python-based Extensible Factor Analysis Platform with Quantile Regression Capability for Evaluating Stock Selection Strategies

2024-07-03T14:52:53+00:00

Factor analysis selects stocks by studying which factors affect stock returns significantly. However, the amount of stocks to be purchased based on the selected factors is usually important but not well-considered in factor analysis. Quantile regression can be used to evaluate the performance of factors at different return quantile(s) which further improves the accuracy and robustness of stock selection strategies, especially on the percentage of stocks with highest (or lowest) factor values to invest. In addition, with so many factors exist, an extensible platform based on python is proposed to perform factor analysis with quantile regression capability. The platform is designed, implemented, and validated for stocks in the Taiwan stock market.

The experimental results show that choosing the quantile with the largest impact coefficient appearing on the tail of head of quantile regression analysis can significantly improve the return rate of the investment portfolio and reduce risk. The proposed and implemented platform not only complete the designated works but also improve the efficiency of Python-based stock strategies backtesting using the Cython conversion as detailed in this work.

Finally, this study uses data from the Taiwan stock market over the past 20 years for backtesting, verifying the effectiveness of the proposed platform and strategy.

Safety Assessment of Junctions - A Case Study of Koottanad Junction

2024-07-15T04:06:14+00:00

Road safety is the practice of preventing congestion, accidents and minimizing the risk of injury or death to road users. There is a huge increase in vehicles and the number of accidents. Ensuring the safety of junctions has great importance, to reduce the number of accidents. Here the evaluation of the traffic is assessed by mainly focusing on mobility aspect, which is to reduce congestion. The existing conditions of Koottanad junction was assessed by conducting a questionnaire survey. It was found that the existing safety conditions were insufficient. So, based on the current scenario of Koottand junction, a base model is developed and simulated using VISSIM software. Then a signal was designed in the existing condition. But the delay was greater than the requirements. So as a further solution, the signalized design was modified by widening the road at the junction and thus providing space for free left.

Ethical Frontiers in Artificial Intelligence: Navigating the Complexities of Bias, Privacy, and Accountability

2024-07-22T07:12:29+00:00

The rapid advancement of artificial intelligence (AI) technologies has ushered in a new era of innovation and efficiency, but it has also raised profound ethical questions that challenge our existing frameworks and demand rigorous scrutiny. This paper explores the critical ethical issues that emerge from the integration of AI across various domains, focusing on bias and fairness, transparency and explainability, privacy, and accountability. We analyze landmark studies and recent cases that highlight the practical manifestations of these challenges, such as the discriminatory tendencies of facial recognition technologies, the opacity of deep learning models, and the privacy risks associated with large-scale data utilization. Drawing from a rich tapestry of interdisciplinary scholarship and case studies, we propose a set of guidelines aimed at fostering the ethical development and deployment of AI systems. By integrating theoretical frameworks and practical examples, this study not only maps the landscape of current ethical challenges but also offers forward-looking strategies to ensure that AI technologies enhance societal well-being without compromising moral values or individual rights.