Engineering Commons^™

Cyber Threat Intelligence Sharing In Nigeria, Muhammad Abubakar Nainna, Julian Bass, Lee Speakman

Communications of the IIMA

Cybersecurity challenges are common in Nigeria. Sharing cyber threat intelligence is essential in addressing the extensive challenges posed by cyber threats. It also helps in meeting regulatory compliance. There are a range of impediments that prevent cyber threat intelligence sharing. We hypothesise that we want to maximise this cyber threat intelligence sharing to resist malicious attackers. Therefore, this research investigates factors influencing threat intelligence sharing in Nigeria's cyber security practitioners. To achieve this aim, we conducted research interviews with 14 cyber security practitioners using a semi-structured, open-ended interview guide, which was recorded and transcribed. We analysed the data using an …

Future-Ready Digitalized Education: Unraveling The Dynamics Of Sustainable And Ethical Digital Transformation, Vaishnavi Rode

Electronic Theses, Projects, and Dissertations

Amid the brisk advancement of digital technologies, higher educational institutions and universities are finding themselves at a crucial turning point, with significant obstacles and new prospects in the realm of digital transformation. This culminating experience project delves deeply into the compounded terrain of digital transformation in higher education, emphasizing the need for sustainable practices in the face of rapidly evolving technical advancements. The research questions are: (Q1) What strategies can universities adopt to foster digital literacy among students and faculty while promoting sustainability values within their digital education programs and Why? (Q2) What ethical considerations, concerning data privacy and digital …

Task Management Application, Dhaval Chaturbhai Hirpara

Electronic Theses, Projects, and Dissertations

The Task Management Application is a web-based platform designed to facilitate efficient task and project management, similar to other Project Management Tools like Jira, Trello, ClickUp, Wrike, Zoho Projects, and Asana. The application features three distinct roles: Administrator, Project Manager, and Employee, each with specific functionalities and permissions to streamline workflow.

Administrator: This role encompasses comprehensive project oversight, including adding, viewing, and managing project managers, supervising ongoing projects, and viewing employee details.

Project Manager: Project Managers can manage employees, assign tasks, and oversee project progress effortlessly.

Employee: Employees have dedicated functionalities to view and manage tasks assigned …

Crime Data Prediction Based On Geographical Location Using Machine Learning, Sai Bharath Yarlagadda

Electronic Theses, Projects, and Dissertations

This project employs machine learning methods like K Nearest Neighbors (KNN), Random Forest, Logistic Regression, and Decision Tree algorithms to monitor crime data based on location and pinpoint areas with risks. The project implements and tunes the four models to improve the precision of predicting crime levels. These models collaborate to offer a trustworthy evaluation of crime patterns. K Nearest Neighbors (KNN) categorizes locations by examining the proximity of data points considering coordinates and other factors to identify trends linked to increased crime data. Logistic Regression gauges the likelihood of crime incidents by studying the connection, between factors (like location …

Service Connect, Namrata Bomble

Electronic Theses, Projects, and Dissertations

ServiceConnect is an innovative web-based marketplace platform designed to revolutionize how local services are accessed and managed in the US. By connecting service providers and customers directly, ServiceConnect provides a simple, secure, user-friendly platform for a range of services such as home repairs, tutoring, pet care and more. Featuring convenient booking tools that increase efficiency while simultaneously building trust among both parties involved. ServiceConnect stands out with its comprehensive service range, user-friendly interface, secure payment processing and rigorous verification process for service providers. Leveraging advanced technologies like ReactJS on the frontend, Node.js & Express on the backend and MongoDB for …

Real-Time Gun Detection In Video Streams Using Yolo V8, Harish Kumar Reddy Kunchala

Electronic Theses, Projects, and Dissertations

In this research, we advance the domain of public safety by developing a machine learning model that utilizes the YOLO v8 architecture for real-time detection of firearms in video streams. A diverse and extensive dataset, capturing a range of firearms in varying lighting and backgrounds, was meticulously assembled and preprocessed to enhance the model's adaptability to real-world scenarios. Leveraging the YOLO v8 framework, known for its real-time object detection accuracy, the model was fine-tuned to accurately identify firearms across different shapes and orientations.

The training phase capitalized on GPU computing and transfer learning to expedite the learning process while preserving …

Classification Model For Discovering The Type Of Crop To Plant Using Ensemble Techniques, Uma Mahesh Addanki

Electronic Theses, Projects, and Dissertations

Farming plays a role in ensuring survival, especially with the growing need for increased agricultural output. It is vital for farmers to efficiently choose the crops to cultivate. By using crop recommendation systems farmers can make decisions on what crops to plant leading to yields and improved resource management. The success of crop production depends on maintaining the balance of soil nutrients and favorable weather conditions. In this research project, we created a crop recommendation system utilizing learning methods to predict the appropriate crops based on essential soil nutrients and weather patterns. We worked with a dataset sourced from Kaggle, …

Society Management App, Ruchit Rakholiya

Electronic Theses, Projects, and Dissertations

A comprehensive solution as native mobile application which is feasible economical and fast, which will establish the authenticity and reliability for society management overcoming the drawbacks of current system. In today's fast-paced technological ecosystem, the capacity to readily store and access information is becoming increasingly important. Residential societies, where individuals live together and manage collective resources, often require a large number of documents, registrations, vehicle parking records, and other forms of paperwork. The complexity and volume of these documents can lead to inefficiencies and frustrations among residents and management alike.

Automated Brain Tumor Classifier With Deep Learning, Venkata Sai Krishna Chaitanya Kandula

Electronic Theses, Projects, and Dissertations

Brain Tumors are abnormal growth of cells within the brain that can be categorized as benign (non-cancerous) or malignant (cancerous). Accurate and timely classification of brain tumors is crucial for effective treatment planning and patient care. Medical imaging techniques like Magnetic Resonance Imaging (MRI) provide detailed visualizations of brain structures, aiding in diagnosis and tumor classification^[8].

In this project, we propose a brain tumor classifier applying deep learning methodologies to automatically classify brain tumor images without any manual intervention. The classifier uses deep learning architectures to extract and classify brain MRI images. Specifically, a Convolutional Neural Network (CNN) …

Cultural Awareness Application, Bharat Gupta

Electronic Theses, Projects, and Dissertations

In an increasingly interconnected global landscape, cultural awareness and competency have become indispensable skills for individuals and organizations alike. This paper introduces a pioneering cultural awareness application, grounded in the Cultural Orientation Model—a comprehensive framework devised by Dr. Walker [8]to guide individuals in understanding, appreciating, and effectively engaging with diverse cultures. The application encompasses ten primary dimensions, each representing fundamental aspects of social life shared by members of any socio-cultural environment. Through a combination of cultural education, interactive learning, guidance on cultural etiquette, and integration of cultural events, the application aims to foster empathy, tolerance, and effective cross-cultural communication skills. …

A Smart Hybrid Enhanced Recommendation And Personalization Algorithm Using Machine Learning, Aswin Kumar Nalluri

Electronic Theses, Projects, and Dissertations

In today’s age of streaming services, the effectiveness and precision of recommendation systems are crucial in improving user satisfaction. This project introduces the Smart Hybrid Enhanced Recommendation and Personalization Algorithm (SHERPA) a cutting-edge machine learning approach aimed at transforming how movie suggestions are made. By combining Term Frequency Inverse Document Frequency (TF-IDF) for content based filtering and Alternating Squares (ALS) with Weighted Regularization for filtering SHERPA offers a sophisticated method for delivering tailored recommendations.

The algorithm underwent evaluation using a dataset that included over 50 million ratings from 480,000 Netflix users encompassing 17,000 movie titles. The performance of SHERPA was …

Automatic Speech Recognition For Air Traffic Control Using Convolutional Lstm, Sakshi Nakashe

Electronic Theses, Projects, and Dissertations

The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing …

Effectiveness Of Cnn-Lstm Models Used For Apple Stock Forecasting, Ethan White

Electronic Theses, Projects, and Dissertations

This culminating experience project investigates the effectiveness of convolutional neural networks mixed with long short-term memory (CNN-LSTM) models, and an ensemble method, extreme gradient boosting (XGBoost), in predicting closing stock prices. This quantitative analysis utilizes recent AAPL stock data from the NASDAQ index. The chosen research questions (RQs) are: RQ1. What are the optimal hyperparameters for CNN-LSTM models in stock price forecasting? RQ2. What is the best architecture for CNN-LSTM models in this context? RQ3. How can ensemble techniques like XGBoost effectively enhance the predictions of CNN-LSTM models for stock price forecasting?

The research questions were answered through a thorough …

Recommendation System Using Machine Learning For Fertilizer Prediction, Durga Rajesh Bommireddy

Electronic Theses, Projects, and Dissertations

This project presents the development of a sophisticated machine-learning model aimed at enhancing agricultural productivity by predicting the optimal fertilizer suited to specific crop requirements. Leveraging a diverse set of features including soil color, pH levels, rainfall, temperature, and crop type, our model offers tailored recommendations to farmers. Three powerful algorithms, Support Vector Machines (SVM), Artificial Neural Networks (ANN), and XG-Boost, were implemented to facilitate the prediction process. Through comprehensive experimentation and evaluation, we assessed the performance of each algorithm in accurately predicting the best fertilizer for maximizing crop yield. The project not only contributes to the advancement of machine …

Key Issues Of Predictive Analytics Implementation: A Sociotechnical Perspective, Leida Chen, Ravi Nath, Nevina Rocco

Journal of International Technology and Information Management

Developing an effective business analytics function within a company has become a crucial component to an organization’s competitive advantage today. Predictive analytics enables an organization to make proactive, data-driven decisions. While companies are increasing their investments in data and analytics technologies, little research effort has been devoted to understanding how to best convert analytics assets into positive business performance. This issue can be best studied from the socio-technical perspective to gain a holistic understanding of the key factors relevant to implementing predictive analytics. Based upon information from structured interviews with information technology and analytics executives of 11 organizations across the …

Does Personality Traits And Security Habits Influence Security Of Personal Identification Numbers? The Context Of Mobile Money Services In Tanzania., Daniel Ntabagi Koloseni

Journal of International Technology and Information Management

Security is an important ingredient in financial transactions; as such, it is imperative that attention should be paid to enhancing the security habits and user behaviours of mobile payment services. Establishing a link between security habits, personality characteristics, and security behaviours provides a new dimension to studying security behaviours regarding mobile money services. Therefore, this study investigates how personality traits affect security behaviours and habits and how security habits mediate the link between personality traits and PIN security practices. The study found that conscientiousness, openness to experience, extroversion and security habits influence PIN security practices, while conscientiousness, agreeableness, and neuroticism …

The Impact Of Social Media On Charitable Giving For Nonprofit Organization, Namchul Shin

Journal of International Technology and Information Management

Research has extensively studied nonprofit organizations’ use of social media for communications and interactions with supporters. However, there has been limited research examining the impact of social media on charitable giving. This research attempts to address the gap by empirically examining the relationship between the use of social media and charitable giving for nonprofit organizations. We employ a data set of the Nonprofit Times’ top 100 nonprofits ranked by total revenue for the empirical analysis. As measures for social media traction, i.e., how extensively nonprofits draw supporters on their social media sites, we use Facebook Likes, Twitter Followers, and Instagram …

How Does Digitalisation Transform Business Models In Ropax Ports? A Multi-Site Study Of Port Authorities, Yiran Chen, Anastasia Tsvetkova, Kristel Edelman, Irina Wahlström, Marikka Heikkila, Magnus Hellström

Journal of International Technology and Information Management

This article investigates the relationship between digitalisation and business model changes in RoPax ports. The study is based on six RoPax ports in Northern Europe, examining their digitalisation efforts and the resulting changes in their business models, leading to further digital transformation. The paper offers insights by reviewing relevant literature on digitalisation’s role in business model innovation and its application in ports. The findings reveal that digitalisation supports relevant business model changes concerning port operation integration within logistics chains, communication, documentation flow, and cargo flow optimisation. However, exploring digitalisation’s potential for diversifying value propositions is still limited. Most digitalisation efforts …

Media And Internet Censorship In India: A Study Of Its History And Political-Economy, Ramesh Subramanian

Journal of International Technology and Information Management

The Indian Constitution, which came into force on January 26, 1950, guarantees various fundamental rights, such as the freedom of speech and expression, freedom of religion, rights to form association, as well as rights to privacy. Yet, since the adoption of the Constitution, the Indian citizen has been subject to varying degrees of media censorship and surveillance. This paper seeks to delve into the historical evolution of media and Internet censorship and surveillance in India. It shows how media censorship of varying types have existed since the British colonists introduced restrictive laws in order to expand and control the native …

Lung Lesion Segmentation Using Deep Learning Approaches, Sree Snigdha Tummala

Electronic Theses, Projects, and Dissertations

The amount of data generated in the medical imaging field, especially in a modern context, is growing significantly. As the amount of data grows, it's prudent to make use of automated techniques that can leverage datasets to solve problems that are error-prone or have inconsistent solutions.

Deep learning approaches have gained traction in medical imaging tasks due to their superior performance with larger datasets and ability to discern the intricate features of 3D volumes, a task inefficient if done manually. Specifically for the task of lung nodule segmentation, several different methods have been tried before such as region growing etc. …

Determining The Effects Of Glycocalyx Modifications On The Electrophysical Properties Of Human Mesenchymal Stem Cells, Rominna E. Valentine Ico

Electronic Theses, Projects, and Dissertations

Human mesenchymal stem cells (hMSCs) have gained popularity in clinical trials due to their multipotent differentiation characteristics, ability to secrete bioactive molecules, migrate into diseased or damaged tissues, and their immunosuppressive properties. HMSC cultures are heterogeneous, containing stem cells, partially differentiated progenitor cells, and fully differentiated cells. One of the major challenges with hMSCs therapeutic potential is the inability to select specific cell subpopulations due to an insufficient number of biomarkers. Often the biomarkers used, like those for fluorescence-activated cell sorting, are not sufficient to define hMSCs because they overlap with other cell types. Consequently, there is a need to …

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Classification Of Large Scale Fish Dataset By Deep Neural Networks, Priyanka Adapa

Electronic Theses, Projects, and Dissertations

The development of robust and efficient fish classification systems has become essential to preventing the rapid depletion of aquatic resources and building conservation strategies. A deep learning approach is proposed here for the automated classification of fish species from underwater images. The proposed methodology leverages state-of-the-art deep neural networks by applying the compact convolutional transformer (CCT) architecture, which is famous for faster training and lower computational cost. In CCT, data augmentation techniques are employed to enhance the variability of the training data, reducing overfitting and improving generalization. The preliminary outcomes of our proposed method demonstrate a promising accuracy level of …

Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni

Electronic Theses, Projects, and Dissertations

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …

Hypothyroid Disease Analysis By Using Machine Learning, Sanjana Seelam

Electronic Theses, Projects, and Dissertations

Thyroid illness frequently manifests as hypothyroidism. It is evident that people with hypothyroidism are primarily female. Because the majority of people are unaware of the illness, it is quickly becoming more serious. It is crucial to catch it early on so that medical professionals can treat it more effectively and prevent it from getting worse. Machine learning illness prediction is a challenging task. Disease prediction is aided greatly by machine learning. Once more, unique feature selection strategies have made the process of disease assumption and prediction easier. To properly monitor and cure this illness, accurate detection is essential. In order …

Building An Application Model For Efficient Ride Booking In Ride-Hailing Industry, Nikunjkumar Butani

Electronic Theses, Projects, and Dissertations

The purpose of this study is to develop an efficient ride booking application in the ride-hailing sector. The objective of the research is to provide users with an easy way to book rides at reasonable prices and convenient times. This project helps to promote the ride-hailing industry by providing guidance for the creation of real-time applications. This project answered three questions. 1. What are the technology and infrastructure requirements for developing a consolidated online application for centralized ride sharing? 2. What are other examples of application aggregators in other industries than transportation and how do they work? 3. What are …

Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …

Quiz Web Application, Dipti Rathod

Electronic Theses, Projects, and Dissertations

The Quiz web application is designed to facilitate the process of quiz creation and participation. This web application mainly consists of three roles: Admin, Instructor, and Student. Each role has specific features, functionalities, and permissions. With a user-friendly interface, the admin role can handle the departments, courses, and instructors. This web application also ensures smooth quiz management, allowing the instructors to schedule the upcoming quizzes, create the questions, and manage the students with ease. Student roles have features like taking quizzes and seeing their results. Additionally, this web application includes a significant feature to prevent cheating during online tests, ensuring …

Enhancing Accident Investigation Using Traffic Cctv Footage, Aksharapriya Peddi

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge …

Automated Medical Notes Labelling And Classification Using Machine Learning, Akhil Prabhakar Thota

Electronic Theses, Projects, and Dissertations

The amount of data generated in medical records, especially in a modern context, is growing significantly. As the amount of data grows, it is very useful to classify the data into relevant classes for further interventions. Different methods that are not automated are very time-consuming and require manual effort have been tried for this before.

Recently deep learning has been used for this task but due to the complexity of the dataset, specifically due to inter-class similarities in the dataset and specific terminology having different meanings in medical contexts has caused significant problems in having a definitive approach to medical …