Digital Commons Network^™

Managing Cybersecurity In Local Governments: 2022, Donald F. Norris Phd, Laura K. Mateczun Jd

Journal of Cybersecurity Education, Research and Practice

This paper, based on data from our second nationwide survey of cybersecurity among local or grassroots governments in the U.S., examines how these governments manage this important function. As we have shown elsewhere, cybersecurity among local governments is increasingly important because these governments are under constant or nearly constant cyberattack. Due to the frequency of cyberattacks, as well as the probability that at least some attacks will succeed and cause damage to local government information systems, these governments have great responsibility to protect their information assets. This, in turn, requires these governments to manage cybersecurity effectively, something our data show …

Promoting Digital Agriculture Adoption In Community-Based Agricultural Organizations, Jean Hardy, Abbey Palmer

Journal of Extension

Existing research and practice related to digital agriculture technology adoption is largely focused on large-scale producers. In this paper, we describe a case of adopting an advanced soil monitoring system in a community-based agricultural organization. We provide guidance for Extension professionals seeking to implement or promote digital agriculture technology adoption on: selecting appropriate technology, incorporating new technology into existing practices, harnessing local technology champions, and avoiding data-driven mission creep.

Improved Bidirectional A* Quadratic Path Planning Algorithm For Mobile Robots, Jiongyi Li, Qiang Li, Xinwen Zhang, Myo Htet Zin, Yongbin Cai

Journal of System Simulation

Abstract: Aiming at the problems of the traditional A* algorithm, such as the unhoped intersection between the planned path and the obstacles, the planned path has many inflection points and the search time is long, an improved bidirectional A* quadratic path planning algorithm for the indoor environments is proposed. Through the expansion of the map, the intersection between the planned path and the obstacle is solved. By new heuristic functions and bidirectional expansion methods, the search speed and accuracy of the bidirectional A* algorithm are improved. Turning cost function and adaptive weight are introduced to reduce the number of turning …

Improved Target Detection Algorithm For Aerial Images Based On Yolov5, Yecai Guo, Jingdong Sun, Saha Amitave

Journal of System Simulation

Abstract: In order to improve the existing small target detection methods, which suffer from low detection accuracy, high false detection rate and high leakage rate, the FSD-YOLOv5 algorithm is proposed, which has three improvements based on the YOLOv5 algorithm. The Focal EIoU is used instead of the original CIoU to improve the model convergence speed and regression accuracy. To cope with the deficiencies in CNN architecture, we adopt a new CNN building block called SPD-Conv is adopted. To address the problem of the reduced or lost information of small objects in feature maps caused by downsampling in convolutional neural networks, …

Comprehensive Insights Into Smart Contracts: Architecture, Sectoral Applications, Security Analysis, And Legal Frameworks, Farah Mazlan, Nur Faizah Omar, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin

Journal of Informatics and Web Engineering

Most conventional contract systems have issues with middlemen, drawn-out implementation procedures, fraud risk, and human error. Considering this, the project uses smart contract technology to provide a decentralized, automated, and safe solution in an effort to address such inefficiencies and the trust issues they raise. Smart contracts enable self-execution of contracts whose conditions are expressed explicitly in lines of code by presenting solutions using blockchain technology. The concept behind a smart contract is that each party may carry out their portion of the duties without depending on a third party and the contract will automatically execute in the meantime. This …

Predicting Short-Range Weather In Tropical Regions Using Random Forest Classifier, Sellappan Palaniappan, Rajasvaran Logeswaran, Anitha Velayutham, Bui Ngoc Dung

Journal of Informatics and Web Engineering

In this paper, we present a Random Forest classifier machine learning model for predicting short-range weather in in tropical regions like Malaysia. Our model uses environmental factors such as temperature, humidity, wind speed, and cloud cover to predict weather conditions like clear skies, rain, and thunderstorms. Tropical weather, influenced by high humidity, fluctuating temperatures, and frequent rainfall, present unique challenges for forecasting accurately. To address these challenges, we trained a Random Forest classifier on a synthetic (simulated) dataset comprising 1,500 samples, each representing a specific weather scenario. Our model achieved an accuracy of 98.66% in predicting short-term weather conditions, identifying …

A Comparative Study Of Oracle Erp Netsuite And Microsoft Dynamics 365 Contributions To Contemporary Business Development In India, Padmanabhan Subramanian, Ponmalar S

Journal of Informatics and Web Engineering

ERP systems centrally provide support to a company's business operations and are capable of providing solutions in the area of resource management, process streamlining, and making data-driven decisions. Acceptance of ERP systems is growing in India as companies begin to become more efficient and competitive. Among the most prominent players are Oracle ERP Netsuite and Microsoft Dynamics 365, both holding unique attributes which pose a challenge for businesses when choosing the right system. The aim of this paper of study is to compare between Oracle ERP Netsuite and Microsoft Dynamics 365 by exploring their contribution to business development in India. …

Improved Accuracy For Heart Disease Diagnosis Using Machine Learning Techniques, Neeraja Joshi, Tejal Dave

Journal of Informatics and Web Engineering

This work primarily focuses on diagnosis of heart disease before explicit visit to the expert doctor. Machine learning based systems have been found useful in medical diagnosis applications because of their ability to learn human like expertise and to utilize acquired knowledge for diagnosis. This work is performs classification of heart disease utilizing subject’s vital parameters. Pathological laboratory results available after testing are not understood by common people and patients have to wait till they visit expert doctors for inference. In this paper, traditional methods like linear regression  to various machine learning based systems including back propagation neural network, support …

Social Engineering Threat Analysis Using Large-Scale Synthetic Data, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Pulasthi Gunawardhana

Journal of Informatics and Web Engineering

We frequently hear news about compromised systems, virus attacks, spam emails, stolen bank account numbers, and loss of money. Safeguarding and protecting digital assets against these and other cyber-attacks are extremely important in our digital connected world today. Many organizations spend substantial amounts of money to protect their digital assets. One type of cyber threat that is rampant these days is social engineering attacks that work on human psychology. These attacks typically persuade, convince, trick and threaten naïve and innocent individuals to divulge sensitive information to the attackers. Consequently, traditional approaches have not been effective or successful in preventing these …

Hyperledger Fabric Blockchain For Securing The Edge Internet Of Things: A Review, Muhammad Haziq Zulhazmi Hairul Nizam, Muhammad Afiq Ahmad Nizam, Muhammad Hadi Husaini Jummadi, Nik Nor Muhammad Saifudin Nik Mohd Kamal, Ahmad Anwar Zainuddin

Journal of Informatics and Web Engineering

Life has become more convenient, efficient, and productive in aspects like homes, healthcare, and other businesses’ due to applied IoT. Nonetheless, the proliferation of IoT has led to enormous data production, which has presented daunting tasks of providing sound protective security solutions. It is crucial to address the above challenges in order to protect the data assets in IoT systems. This work deals with the concern on how to extend Hyperledger Fabric to IoT, this being a very crucial aspect in allowing for secure techniques in the collection, storage and sharing of data. Hyperledger Fabric offers advanced capabilities of smart …

Machine Learning Approaches For Detecting Vine Diseases: A Comparative Analysis, Waheed Ahmad, Eshill Azhar, Maham Anwar, Sarah Ahmed, Tayyaba Noor

Journal of Informatics and Web Engineering

This study investigates the classification of vine leaf diseases using convolutional neural networks (CNNs), focusing on three major diseases: powdery mildew, caused by fungus Uncinula necator, Red Blotches associated with pathogens such as Phomopsis viticola, Grapevine Leafroll Disease and leafroll associated Grape -linked virus (GLRaV). Accurate diagnosis of these high-risk diseases is critical to vine health and yields. We evaluated the performance of three CNN algorithms—MobileNetV2, ResNet50, and VGG16 —by comparing their training and validation accuracies, as well as loss over ten seasons. MobileNetV2 emerged as the most robust model, exhibiting high accuracy and low loss, indicating strong generalizability. ResNet50 …

Implementing Identity-Based Signature Schemes For Secure Data Transfer In Cloud Computing Environments, Paul Osinuga, Ji-Jian Chin, Terry Shue Chien Lau

Journal of Informatics and Web Engineering

In this paper, we present the implementation of the Cha-Cheon Identity-Based Signature (IBS) scheme to enhance secure data transfer in cloud computing environments. Cloud computing rely on traditional Public Key Infrastructure (PKI) systems, which is burdened by certificate management infrastructure. The primary focus of this research to simplify key and certificate management by leveraging identity-based elliptic curve cryptography (ECC) within the Cha-Cheon IBS framework. We show that the proposed IBS solution integrates seamlessly with Amazon Web Services (AWS), utilizing services like S3 for secure data storage and KMS for key management. By applying ECC, the Cha-Cheon scheme achieves efficient cryptographic …

Iot-Based Nerve Stimulator For Women’S Safety, K Revathi, W Gracy Theresa

Journal of Informatics and Web Engineering

Women deserve a right to live free from intimidation, mistreatment, unfair treatment, and eliminating hurdles from a hazardous workplace can help them reach their maximum potential both personally and as contributions to economies, societies, and the workplace. Physical, emotional, and environmental safety have a variety of effects on wellbeing, including stress management, emotional stability, and physical health. The Women Safety System with Nerve Stimulator is a comprehensive system that integrates essential components for women's safety using an Arduino Uno microcontroller. It has an SOS button for emergency activation, a temperature sensor for environmental monitoring, a pulse oximeter for tracking health, …

Lung Tumor Segmentation In Medical Imaging Using U-Net, J Jayapradha, Su-Cheng Haw, Naveen Palanichamy, Senthil Kumar Thillaigovindhan, Mutaz Al-Tarawneh

Journal of Informatics and Web Engineering

Tumors are a deadly condition often triggered by a range of abnormal modifications and genetic abnormalities. Early tumor diagnosis is essential due to the highly concerned nature of the disease. Early detection and treatment of tumors can significantly reduce mortality rates. This paper presents a model for tumor segmentation in medical imaging that uses the U-NET architecture to increase precision. The model’s encoding and decoding processes have been applied with skip connections to boost performance while simplifying model training. Images were cropped around the lower abdominal regions, and all images used in the study were then resized to 256*256 pixels …

A Hybrid Deep Learning Vgg-16 Based Svm Model For Vehicle Type Classification, Muhammad Imran, Jafar Usman, Muntazir Khan, Abdullah Khan

Journal of Informatics and Web Engineering

Car classification is important in daily life because there are many distinct types of automobiles made by various manufacturers. Although there are numerous methods for classifying autos, machine learning technologies have not been widely utilized, resulting in low accuracy levels. The goal of this paper is to create a machine learning system that is especially made to categories models of two Pakistan's top automakers, Toyota, and Honda. Ten Toyota models such as Avalon, Land Cruiser, Camry, Corolla, C-HR, Highlander, Prius, Tundra, RAV4, and Yaris and a dataset of Honda automobiles, which also includes 10 models (Accord, Civic, CR-V, Fit, HR-V, …

Early Identification Of Parkinson's Disease Using Time Frequency Analysis On Eeg Signals, Tanvir Hasib, V Vijayakumar, Ramakrishnan Kannan

Journal of Informatics and Web Engineering

Parkinson's Disease (PD) is a progressive neurological disorder. It affects movement and can significantly impact quality of life. Early and accurate diagnosis is crucial for effective management and intervention. Traditional diagnostic methods can be time-consuming and less effective in the early stages of the disease. This study aims to develop an automated approach for identifying PD using time-frequency image analysis of electroencephalogram (EEG) signals. The goal is to enhance diagnostic accuracy and efficiency, facilitating early detection. EEG signals, often contaminated with artifacts such as eye blinks and muscle movements etc., were first cleaned. Time-frequency images were then plotted from the …

Optimising Phishing Detection: A Comparative Analysis Of Machine Learning Methods With Feature Selection, Mohamad Asraf Daniel, Siew-Chin Chong, Lee-Ying Chong, Kuok-Kwee Wee

Journal of Informatics and Web Engineering

Phishing is an act of cybersecurity attack that tricks people into sharing sensitive data. Due to the inefficiency of the current security technologies, researchers have been paying much attention to employing machine learning methods for phishing detection lately. In our proposed solution, the effectiveness of machine learning techniques with feature selection techniques for phishing detection is investigated. To be specific, Random Forest (RF) and Artificial Neural Network (ANN) are integrated with feature selection techniques, Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE). The goal was to identify and classify the model with the highest accuracy. The experiments were evaluated …

Creating An Android-Based Calisthenics Application To Assist Students In Improving Their Physical Fitness, Restyandito, Narendra Poetra Wisnoewardhana, Danny Sebastian

Journal of Informatics and Web Engineering

College students, particularly those heavily involved in coursework, frequently do not prioritize physical fitness. Regular exercise is essential for preserving physical fitness and facilitating demanding academic tasks. Although there are other fitness programs available, such as GYM, jogging, CrossFit, and Yoga, users sometimes fail to make full use of them because the content is confined to exercise videos and descriptions. This matter highlights the progress of the Android application Kali Tech, which specifically concentrates on organizing and documenting calisthenics workouts. This program employs the concept of gamification by utilizing student achievement levels to encourage students to be diligent in doing …

Machine Learning Model For Predicting Net Environmental Effects, Sellappan Palaniappan, Rajasvaran Logeswaran, Shapla Khanam, Zhang Yujiao

Journal of Informatics and Web Engineering

Environmental sustainability is a global challenge in the face of increasing incidences of disasters affecting communities worldwide. This requires predicting net environmental effects accurately. While various approaches exist, we need more sophisticated prediction models that account for both environmental and social factors. This study presents a proof-of-concept machine learning model for predicting net environmental effects using synthetic data. We developed a multiple linear regression model incorporating nine key features: renewable energy usage, carbon emissions, air quality index, water usage, biodiversity impact, land use, public awareness, and environmental attitudes. We generated a synthetic dataset of 1000 samples using probability distributions and …

Fiber Break Prevention Using Machine Learning Approaches, Zhan Heng Ng, Tee Connie, Kan Yeep Choo, Michael Kah Ong Goh, Nurul Ain Abdul Aziz, Hong Yeap Ngo

Journal of Informatics and Web Engineering

Modern fiber-optic communication systems are built around optical fiber, which allows data to be sent by emitting infrared light pulses. It is widely used by telecommunications firms and is essential to the smooth transmission of information in internet communication as well as the transmission of telephone signals. Nonetheless, optical fibers intrinsic fragility raises a problem, especially in areas where building projects are taking place. Especially nowadays construction-related impact and crushing pressures can cause physical damage that jeopardizes the fiber optic's integrity. Therefore, this research emphasizes the necessity of taking preventative and mitigating actions to reduce the possibilities of fiber optic …

Migraine Generative Artificial Intelligence Based On Mobile Personalized Healthcare, Michelle Ting-Ting Yong, Sin-Ban Ho, Chuie-Hong Tan

Journal of Informatics and Web Engineering

Migraine is a complicated genetic disorder characterized by episodes of moderate-to-severe headaches that are usually unilateral and are frequently accompanied by nausea and increased sensitivity to sound and light. A migraine attack induces intense pain, hindering an individual from engaging in daily activities and potentially persisting for hours or even days. By the growth of the Internet of Things, we have new opportunities to try to apply it to the medical field. To identify the origin of a migraine, specialists need access to a patient's medical history and a comprehensive understanding of migraine symptoms for effective treatment. Determining the true …

Exploration Of The Impact Of Cyber Situational Awareness On Small And Medium Enterprises (Smes) In Malaysia, Tan Chee Keong, Sofiah Kadar Khan, Umar Farooq Khattak

Journal of Informatics and Web Engineering

The objective of this study is to explore the cyber situational awareness (CSA) level among the employees of small and medium-sized enterprises (SME) in Malaysia, by extending Endsley's situation awareness (SA) theory. It is crucial to understand the level of cyber situational awareness among employees as it sheds light on how well the employees understand the cyber threats and if they can handle them effectively. Literature has reviewed that SMEs are subject to a greater danger of cyber-attacks. Therefore, employees' awareness of cyber situations is of the utmost significance in studying cyber security. A convenient non-probability sampling method was chosen …

Path To A Healthy Work-Life Balance: Mobile Application For Work And Personal Life Mastery, Erfan Rahmani, Zarina Che Embi

Journal of Informatics and Web Engineering

Drawing insights from diverse organizational methods, this study endeavors to facilitate effective self-development and organization in the face of contemporary demands as solutions for integrating goal tracking, event coordination, and task management within a unified calendar framework in a mobile application. There are three primary objectives of this study: firstly, to explore essential functionalities crucial for addressing the multifaceted challenges of modern life; secondly, to design and develop a mobile application that seamlessly integrates these functionalities; and finally, to evaluate the usability of the application through rigorous testing and feedback mechanisms. Envisaged deliverables include a fully functional mobile application designed …

Personalized Drug Recommendation System Using Wasserstein Auto-Encoders And Adverse Drug Reaction Detection With Weighted Feed Forward Neural Network (Waes-Adr) In Healthcare, J Omana, P. N Jeipratha, K Devi, S Benila, K Revathi

Journal of Informatics and Web Engineering

In recent years, the use of deep learning approaches in healthcare has yielded promising results in a variety of fields, most notably in the detection of adverse drug reactions (ADRs) and drug recommendations. This paper promises a breakthrough in this field by using Wasserstein autoencoders (WAEs) for personalized medicine recommendation and ADR detection. WAEs' capacity to manage complex data distributions and develop meaningful latent representations makes them ideal for modeling heterogeneous healthcare data. This study intends to improvise the precision and efficiency of drug recommendation systems while also improving patient safety by combining WAEs and early ADR detection strategies. Previous …

Integrating Neural Networks For Predictive Torque Control And Obstacle Avoidance In Autonomous Robot, Viswanath Kodali, Harsha Vardhan Borra, Kiran P

Northeast Journal of Complex Systems (NEJCS)

In the field of robotics, precise motion control and accurate computation of joint forces are critical for ensuring optimal performance. Traditional methods, such as using the Jacobian matrix for joint angle determination and Euler-Lagrange equations for torque computation, are reliable but computationally intensive, making them less suitable for real-time applications. This paper presents an advanced approach to improving the productivity and efficiency of a 3-Degree of Freedom (DOF) robotic arm by utilizing Artificial Neural Network (ANN). The proposed system dynamically predicts joint angles and torque, enabling faster and more efficient motion control.

To address the challenge of obstacle avoidance in …

The Evolution And Future Of Microservices Architecture With Ai-Driven Enhancements, Jill Willard, James Hutson

Faculty Scholarship

Microservices architecture has revolutionized software development by enabling the decomposition of monolithic applications into smaller, more manageable services. While this shift has reduced risks and enhanced system resiliency, the increasing complexity of managing numerous microservices presents new challenges. As Artificial Intelligence (AI) continues to evolve, there is a growing need to explore how autonomous AI agents can optimize microservices architectures, particularly in terms of communication and workflow orchestration. The purpose of this study is to investigate how AI agents can autonomously interact and manage microservices, reducing human intervention and enhancing system efficiency. The key research question guiding this investigation relates …

Multimodal Search On A Line, Jared Coleman, Dmitry Ivanov, Evangelos Kranakis, Danny Krizanc, Oscar Morales Ponce

Computer Science Faculty Works

Inspired by the diverse set of technologies used in underground object detection and imaging, we introduce a novel multimodal linear search problem whereby a single searcher starts at the origin and must find a target that can only be detected when the searcher moves through its location using the correct of p possible search modes. The target’s location, its distance d from the origin, and the correct search mode are all initially unknown to the searcher. We prove tight upper and lower bounds on the competitive ratio for this problem. Specifically, we show that when p is odd, the optimal …

Artificial Intelligence (Ai) In Pharmacy, Giang Nguyen, Elizabeth Sartschev, John Reyes, Allie Honigford, Marisa Petrunich, Kiley Devoll, Brianna Lu, Joshua Honaker, T'Bony M. Jewell

Pharmacy and Wellness Review

Artificial Intelligence (AI) has transformed the pharmaceutical field by enabling computer software systems to learn and perform human behavior. Specifically, AI has revolutionized chronic diabetes management through continuous glucose monitoring, showcasing its immense potential in healthcare. However, alongside its transformative impact, AI’s increasing role in healthcare has prompted concerns over privacy and its premature integration. Despite these challenges, AI offers limitless opportunities to improve medication management and treatment regimens, driving advancements across various domains. From improving CT imaging to enhancing adenoma detection in colonoscopies and facilitating medication adherence, AI’s impact on healthcare is profound. Furthermore, AI plays a pivotal role …

Comparing In-Person, Standard Telehealth, And Remote Musculoskeletal Examination With A Novel Augmented Reality Exercise Game System: Pilot Cross-Sectional Comparison Study, Richard Wu, Keerthana Chakka, Sara Belko, Ninad Khargonkar, Kevin Desai, Balakrishnan Prabhakaran, Thiru Annaswamy

SKMC Student Presentations and Publications

BACKGROUND: Current telemedicine technologies are not fully optimized for conducting physical examinations. The Virtual Remote Tele-Physical Examination (VIRTEPEX) system, a novel proprietary technology platform using a Microsoft Kinect-based augmented reality game system to track motion and estimate force, has the potential to assist with conducting asynchronous, remote musculoskeletal examinations.

OBJECTIVE: This pilot study evaluated the feasibility of the VIRTEPEX system as a supplement to telehealth musculoskeletal strength assessments.

METHODS: In this cross-sectional pilot study, 12 study participants with upper extremity pain and/or weakness underwent strength evaluations for four upper extremity movements using in-person, telehealth, VIRTEPEX, and composite (telehealth plus VIRTEPEX) …

A Review Of Artificial Intelligence, Algorithms, And Robots Through The Lens Of Stakeholder Theory, Michael J. Matthews, Su Runkun, Lindsey Yonish, Shawn Mcclean, Joel Koopman, Kai Chi Yam

Management Faculty Publications

With the arrival of the Fourth Industrial Revolution, intelligent machines are affecting the daily lives of multiple organizational stakeholders. However, despite the continued expansion of intelligent machines in society, management scholarship has generally lagged, and current frameworks are under-equipped to offer meaningful guidance regarding the intersection of intelligent machines and organizations. We address this issue via a multidisciplinary review and a novel framework of intelligent machines and value creation. First, we discuss the characteristics of intelligent machines (i.e., autonomy, learning, inscrutability, and materiality) and how variation in these characteristics impacts their affordances and, subsequently, the value offered to stakeholders. We …