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25 articles for “issue trees”
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Leveraging Management Frameworks and Academia-Industry Collaboration for Enhanced Efficiency and Productivity in Startups
Abstract: Startups play a maternal role in the getaway of innovation and subsequently, economic growth, but all the while have a plethora of challenges against operational efficiency and productivity. This research thereby proceeds to leverage management frameworks on two of the difficult periods in a startup's lifespan: pre-opening and post-opening. The pre-opening phase has been characterized by emphasis upon strategic planning and market analysis. The industry dynamics, competitive positioning, or issues …
Published in Journal of Production Research & Management · Vol. 15, Issue 1, 2025 · pp. 7–14 Read article
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Risk Assessment of Nitration Process Using HAZOP and Fault Tree
Abstract: The present study addresses the safety issues associated with the nitration reactions. Since the nitration reaction is highly exothermic in nature exhibiting intense heat during the operation and may explode if wrongly handled, the risk assessment of nitration reaction is absolutely necessary. In the present work, the production of Ortho Nitro Chloro Benzene (ONCB) and Para Nitro Chloro Benzene (PNCB) is considered to perform the risk assessment of the nitration …
Published in Journal of Industrial Safety Engineering · Vol. 11, Issue 3, 2024 · pp. 18–23 Read article
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Climate Change Including Forest Fire Prediction using Machine Learning and Deep Learning
Abstract: Climate change alludes to long haul shifts in temperatures and atmospheric conditions. These movements might be regular, for example, through varieties in the sun-oriented cycle. In any case, since the 1800s, human exercises have been the fundamental driver of climate change, basically because of consuming fossil fuels like coal, oil and gas. Many individuals think climate change mostly implies hotter temperatures. Be that as it may, the temperature climb is …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 Read article
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Evaluation of Credit Risk of Bank Customers with a Hybrid Approach of Data Mining Techniques
Abstract: Credit risk poses the most significant threat to financial and monetary institutions. Banks strive to offer loans that generate high returns while minimizing risk. Achieving this requires the ability to accurately identify and classify credit customers, both individuals and legal entities, according to their likelihood of fully meeting their obligations. This classification is done using relevant financial and non-financial criteria. The primary goal of this study is to assess the …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 63–81 Read article
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Urban Eye: Manhole Surveillance with IoT
Abstract: Every nation hopes to implement the idea of the "smart city" on its own. Many different issues must be resolved in order to grow the seed into a large tree. The drainage system is one of them, and it is essential. With the right drainage system, even a tiny region can make a significant contribution to society. A sewer system can be accessed through manholes. Manholes are the best approach …
Published in International Journal of Machine Systems and Manufacturing Technology · Vol. 1, Issue 1, 2023 · pp. 13–22 Read article
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Real-time DDoS Attack Prediction in SDN Environments Using Machine Learning
Abstract: The ever-growing reliance on sdn-based services necessitates robust security measures against Distributed Denial-of-Service (DDoS) attacks that threaten service availability. This project investigates the development of a real-time prediction system for DDoS attacks in sdn environments, leveraging the power of machine learning. The proposed system employs a Decision Tree classification algorithm implemented in Python. To ensure accurate attack identification, the system meticulously addresses data preprocessing challenges inherent in network traffic datasets. …
Published in Journal Of Network security · Vol. 13, Issue 1, 2025 · pp. 16–27 Read article
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Identification of Brain Stroke Using Artificial Intelligence
Abstract: Globally, strokes are the primary cause of disability and mortality. Recently, machine learning (ML) and deep learning (DL) have been employed by artificial intelligence algorithms as effective stroke diagnosing techniques. These days, machine learning and data mining technologies are used in the construction of the main models. We have used five machine learning algorithms to determine if a stroke has occurred or is likely to occur based on a patient’s …
Published in Journal of Microwave Engineering and Technologies · Vol. 11, Issue 2, 2024 · pp. 15–22 Read article
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A Systemic Review: Pithecellobium dulce (Jangal Jalebi)
Abstract: Pithecellobium dulce (Roxb.) Benth., commonly referred to as Jangal Jalebi, Vilayati Babul, or Manila Tamarind, is a versatile tree in the Leguminosae (Fabaceae) family. It is commonly found in tropical and subtropical areas and is valued for its ecological, medicinal, and nutritional benefits. The plant is rich in a variety of bioactive compounds, including flavonoids, alkaloids, phenols, glycosides, tannins, saponins, terpenoids, and steroids, which enhance its pharmacological potential. Different extracts …
Published in Research & Reviews : Journal of Herbal Science · Vol. 15, Issue 1, 2026 · pp. 14–20 Read article
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Review on Elaeocarpus Ganitrus (Rudraksha)
Abstract: The seed of Elaeocarpus Ganitrus, also known as Rudraksha, is renowned for its electromagnetic characteristics. Recent scientific research has demonstrated that this seed possesses natural electromagnetic properties which can effectively treat various chronic diseases. This study focuses on the phytochemical screening and thin layer chromatographic analysis of the extract obtained from Elaeocarpus Ganitrus seeds, which belong to the Elaeocarpaceae family. Bombay, and is commonly grown as an ornamental tree in …
Published in Research & Reviews: A Journal of Pharmacognosy · Vol. 11, Issue 2, 2024 · pp. 8–15 Read article
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A Web Application for Predicting Diabetes Using Machine Learning Methods
Abstract: Diabetes is a long-term disease caused by high glucose quantity in the blood. It has the potential to result in serious health complications like heart disease, hypertension, and ocular damage. It is good to identify any health issues as early as possible to get the right medical treatment and make necessary lifestyle adjustments. One makes use of machine learning techniques to predict diabetes and develop treatment options using actual cases. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 92–102 Read article
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The Assessment of Electric Vehicle Fire Risk Using the Failure Tree Analysis
Abstract: With the global shift toward sustainable transportation, the widespread adoption of electric vehicles (EVs) is rapidly becoming a reality, largely driven by growing environmental awareness and concerns over climate change. However, alongside this transition comes a set of emerging challenges, most notably, the increasing incidence of EV-related fire hazards, which have attracted significant public and media scrutiny. This situation highlights the urgent need for a detailed and systematic approach to …
Published in Journal of Industrial Safety Engineering · Vol. 12, Issue 2, 2025 · pp. 27–38 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Modernizing Pharmacovigilance: Leveraging AI, Automation, and Real-World Data for Drug Safety
Abstract: Pharmacovigilance, or PV, is “the pharmacological science relating to the detection, assessment, understanding, and prevention of adverse effects, mainly long term and short-term adverse effects of medicines.” PV’s specific objectives are to increase patient care and safety when using medications and all medical and paramedical therapies; assist in evaluating the benefits, drawbacks, efficacy, and risks of medications, ensuring their safe, prudent, and more effective use; and promote clinical training, education, …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 12–21 Read article
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Reduction of Air Pollutants of Urban Canyons through Management of Particulate Matters 2.5 in the Streets
Abstract: Urban canyons are long and high sky-scrappers closely to narrow streets result in very different microclimate challenges. These spaces often trap pollutants and restrict air circulation and intensify more retention of heat making them very uncomfortable for pedestrians. In order to resolve this issue a strong set of design guidelines and frameworks were needed which can balance out the human comfort and environmental aspects. This research studies strategies to improve …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 3, Issue 1, 2025 · pp. 11–23 Read article
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Tank Water Quality Analysis Using Machine Learning
Abstract: Tank Water quality is a critical factor for public health, agriculture, as well as industry. Continuous monitoring of tank water quality: temperature, humidity, water level, CO2 concentration, and pH, is vital for safe usage. Using machine learning, real-time data analysis can detect anomalies, predict issues, and optimize water management, ensuring timely responses and improved safety. This intelligent approach enhances decision-making and maintains water quality effectively in various environments.We develop an …
Published in Journal of Control & Instrumentation · Vol. 16, Issue 2, 2025 · pp. 27–34 Read article
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Enhanced Multimodal Disease Prediction Using Hybrid Ensemble Learning and AutoML Techniques
Abstract: The integration of hybrid ensemble learning and automated machine learning (AutoML) is revolutionizing disease prediction by addressing the complexity, imbalance, and high dimensionality inherent in medical datasets. This paper proposes an advanced pipeline that combines diverse ensemble learning models with AutoML-based optimization to predict chronic diseases such as kidney diseas-e, Parkinson’s disease, and lung cancer. Publicly available datasets from UCI and PhysioNet repositories were preprocessed using outlier removal, normalization, and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 Read article
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Textual Clues to Stress: A Machine Learning Approach
Abstract: Nowadays, numerous individuals utilize social media platforms to share tweets about their daily lives, which often reflect their mental well-being. Recognizing and managing stress is essential before it becomes a serious issue. Each day, a significant volume of informal messages is posted on discussion forums, blogs, and social networking sites. This study introduces a method for detecting stress using information gathered from social media, with a focus on Twitter. The …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 1, 2025 · pp. 72–76 Read article
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Optimizing Heart Disease Prediction: Comparative Analysis of Machine Learning Algorithm for Early Detection
Abstract: The expanding realm of data analysis holds considerable importance in healthcare, particularly in the medical sector where forecasting heart disease is considered a complex endeavor. Early prediction of serious health conditions can be the determining factor between survival and fatality, with heart disease being one such critical health issue. Over the past decade, the main reason for death has been heart disease. Heart disorders come in many different forms, and …
Published in International Journal of Computer Science Languages · Vol. 2, Issue 1, 2024 · pp. 1–10 Read article
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An Automated Smart Contract Repair Framework for Reentrancy, Integer Overflow, and Denial- of-Service Vulnerabilities
Abstract: This paper introduces a novel static analysis framework designed to bridge a long-standing gap in Ethereum smart contract security: the disconnect between vulnerability detection and automated remediation. Although widely adopted tools such as Slither and Oyente are highly effective at identifying security weaknesses, they stop short of providing actionable fixes. As a result, developers manually patch vulnerabilities, a process that is not only time-consuming but also susceptible to human error …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 31–41 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article