Journal Repository is a multidisciplinary, open-access platform dedicated to disseminating high-quality, peer-reviewed research from diverse academic fields. The journal welcomes original articles, reviews, and case studies that contribute to the advancement of knowledge and foster critical academic discourse. By providing an inclusive and accessible medium, Journal Repository aims to bridge the gap between researchers, practitioners, and policymakers worldwide. With a strong commitment to upholding rigorous editorial standards and promoting ethical publication practices, Journal Repository serves as a reliable resource for researchers and institutions seeking visibility and impact. The journal encourages submissions from an international audience and supports the global exchange of ideas, data, and innovations to address emerging challenges and opportunities across disciplines.
Epidemic Spread Prediction Using AI and Population Data Through Predictive Analytics
Epidemics, such as Dengue and Influenza, remain significant threats to public health, particularly in densely populated areas. These diseases can spread rapidly, posing a challenge to early detection and containment. Existing methods for epidemic prediction often rely on basic surveillance and historical data, but these approaches have limitations, including a lack of real-time updates and the ability to predict disease trends with high accuracy. This project aims to leverage artificial intelligence (AI) and machine learning techniques to predict the infection status of individuals based on blood sample data and visualize epidemic spread patterns. Using medical datasets containing blood parameters such as WBC count, CRP levels, platelet count, and age, the AI model can classify individuals as infected, cured, or deceased, and determine whether the cause is Dengue or Influenza. Machine learning algorithms like Logistic Regression, AdaBoost, ANN, Random Forest, LGBM, and Decision Trees have been applied to the data, resulting in high prediction accuracy. The system also integrates real-time visualizations, showing infection statistics, recovery trends, and mortality rates, and it utilizes population data to predict the regional spread of the epidemic. This project represents a significant advancement over traditional methods by providing a robust, AI-based solution for epidemic prediction and management.
Lane-Wise Traffic Intelligence Using Deep Vision Systems for Signal Optimization
Urban traffic congestion remains a critical challenge affecting commute times, fuel efficiency, and air quality. This project presents a data-driven approach to traffic flow optimization by dynamically adjusting traffic signal timings based on real-time vehicle density across multiple lanes. Utilizing computer vision techniques, such as YOLO-based vehicle detection, the system captures live video feeds from intersections to estimate vehicle count per lane. The signal timings are then optimized to prioritize lanes with higher traffic density, thereby reducing overall waiting times and improving traffic throughput. Experimental results from a simulated environment demonstrate significant improvements in traffic flow efficiency and reduced signal idle times. The proposed solution offers a scalable and adaptive framework for smart city traffic management systems.
AI- Driven Identification of Therapeutic Plants
The AI Driven Identification of Therapeutic Plants project introduces an innovative solution for identifying medicinal plants using deep learning techniques. By leveraging a Convolutional Neural Network (CNN) algorithm, the system accurately classifies plants from user-uploaded images. Upon identification, the system provides comprehensive information, including the predicted class of the plant, its medical content, age restrictions, gender restrictions, pregnancy restrictions, recommended dose per day, and the mode of use. This detailed, user-friendly web interface allows individuals to upload images and quickly access relevant medicinal details, bridging the gap between technology and traditional plant knowledge. The system is designed to assist healthcare professionals, researchers, and individuals interested in natural remedies, offering an accessible and reliable resource for therapeutic plants. With the continuous growth of data available on medicinal plants, the integration of AI allows for faster and more accurate identification, reducing human error and the time required for manual identification. Additionally, the model’s scalability ensures that as more plant species are studied, the system’s database will continue to expand, providing a valuable tool for plant-based healthcare applicationsnt.
Early Detection of Alzheimer’s Disease Using Deep Learning
Alzheimer's Disease acts as a degenerative brain trauma that results in poor memory, alongside impairing mental functioning. Early detection of Alzheimer's Disease is aimed at controlling symptoms in addition to improving medical care for patients. Traditional diagnosis based on imaging procedures in conjunction with cognitive examinations takes a long time for experienced experts to analyze. Deep learning technology triggered interest in automated MRI data analysis techniques because they execute quickly and attain high accuracy levels. The application explores the development of an Alzheimer's Disease Diagnosis System as a web-based light application by using a custom Convolutional Neural Network (CNN) in its implementation. The system analyzes brain MRI scans to perform patient group assignment into Mild Demented, Moderate Demented, Very Mild Demented, and Non-Demented categories. The system shows a valid accuracy of 88.5%, prediction probabilities, and stage-dependent medical advice that generate downloadable diagnosis reports on the Streamlit platform. The system maintains user privacy through non-storage of their information and includes readable medical warning notices during use. The system is a working bridge between experimental technologies for Alzheimer's identification and available medical practices in clinical environments.
Automated Brain Tumor Classification Using Hybrid Deep Learning Models
The accurate and timely diagnosis of brain tumors remain a major challenge in medical imaging, traditionally dependent on radiologist expertise. To address this, we propose a hybrid deep learning framework that integrates computer vision models with transfer learning for improved brain tumor detection and classification. Our system combines the feature extraction capabilities of VGG16 and ResNet-50 into a hybrid VGG16–ResNet-50 model and leverages a fine-tuned EfficientNetB2 network. These models classify tumors into glioma, meningioma, pituitary, and no tumor categories using the CE-MRI dataset. The hybrid model achieves an accuracy of 80.0%, while EfficientNetB2 delivers 85% accuracy and an F1-score of 80%. Beyond imaging, we introduce a symptom-based interface that allows users to input neurological symptoms for early risk screening and personalized treatment recommendations. Grad-CAM visualization is integrated to highlight tumor regions, enhancing interpretability and clinical trust. The system is deployed as a web-based application under the name TumorTrace AI, providing instant classification, explainability, and automated PDF medical reports. This framework bridges the gap between research and real-world application by combining deep learning, clinical usability, and a user-friendly interface. It aims to improve diagnostic workflows, support early detection, and serve as a scalable tool for personalized brain tumor care.
Maximizing Returns with Linear Programming in Systematic Investment Plans
Systematic Investment Plans (SIPs) have gained popularity as a disciplined approach to investing, allowing individuals to invest fixed amounts at regular intervals. However, optimizing the allocation of funds across various assets to maximize returns while minimizing risk remains a challenge. This paper explores the application of Linear Programming (LP) in optimizing SIPs. Using Python, the investment problem has been formulated as a linear programming model. It demonstrates how investors can maximize returns subject to constraints such as budget, risk tolerance, and investment horizon. The results indicate that LP can be a powerful tool for enhancing the efficiency of SIPs, providing a structured approach to asset allocation that aligns with investors' financial goals. The research highlights the role of LP in determining the optimal portfolio by solving objective functions that represent returns, while also factoring in real-world investment constraints. Through this approach, investors can potentially improve the performance of their SIP portfolios and make more informed decisions that align with their financial goals. The results demonstrate the effectiveness of LP in systematic investment strategies.
Characterization of TiAlN PVD Coating on High-Speed Steel Plate
This research investigates the performance enhancement of High-Speed Steel (HSS) using Titanium Aluminum Nitride (TiAlN) coatings applied through Physical Vapor Deposition (PVD) via the Arc method. The coated specimens were tested using CALO test, Rockwell Hardness, Rubber Wheel Abrasion Test, and simulations for stress and thermal behavior. TiAlN significantly improved the thermal, mechanical, and abrasive resistance of HSS, showing its potential in industrial applications where durability and precision are crucial.
CROP MSP Forecasting and OTP-Verified SMS Notification System
Agricultural price forecasting plays a vital role in empowering farmers with market intelligence, enhancing crop planning, and supporting economic resilience. This project presents an efficient and user-friendly system for predicting the Minimum Support Price (MSP) of crops using machine learning techniques, with a real-time interface built using Streamlit. The system leverages an XGBoost regression model trained on historical crop price datasets, including commodity name, crop variety, type, and year. To increase accessibility and impact, the application incorporates Twilio SMS integration, enabling users to send MSP predictions directly to farmers’ mobile phones. The web interface includes a step-wise selection mechanism for crop type, commodity, and variety, along with intuitive visualization of prediction results and comparison with actual MSP values when available. The model achieves a strong R² score, indicating reliable predictive performance across crop types and years. By integrating machine learning with SMS-based communication, this solution offers a practical and scalable tool for agricultural advisory systems, especially in rural and low-resource settings.
Confidence Based Ship Detection Using YOLOv8
Maritime monitoring and ship detection play a crucial role in ensuring coastal security, managing maritime traffic, and supporting environmental surveillance. This paper presents an efficient and lightweight approach to ship detection using YOLOv8, a state-of-the-art object detection model, applied to both satellite and aerial imagery. The proposed system is designed for real-time inference and enhanced user interaction by integrating a dynamic confidence threshold slider in the web interface. This feature allows users to fine-tune detection sensitivity on-the-fly based on image conditions and detection accuracy preferences. The model is trained on a diverse dataset containing annotated images in YOLO format, ensuring robustness across different imaging conditions and ship sizes. The implementation emphasizes accessibility, allowing deployment on devices without GPU support, and includes a clean, responsive web interface built with Streamlit for intuitive interaction. Experimental results demonstrate reliable detection capabilities with acceptable precision-recall balance for practical applications. The integration of a confidence slider and user-focused design elements marks a significant improvement in usability for non-technical stakeholders, making the system suitable for both research and field deployments.
Parkinson’s Disease Detection Using Spiral Images and Voice Data Set
Parkinson’s Disease (PD) is a degenerative neurological disorder that significantly affects motor functions, vocal clarity, and overall coordination. The accurate and early detection of PD is critical for managing symptoms and improving patient outcomes. This base paper presents a dual-modal diagnostic approach that utilizes both voice feature analysis and spiral drawing interpretation to detect Parkinson’s Disease. Voice data is processed to extract features such as jitter, shimmer, and harmonic-to-noise ratio, which are then analyzed using a Random Forest classifier. Parallelly, spiral drawings — commonly used in clinical motor assessments — are classified using a Convolutional Neural Network (CNN) to detect tremor patterns. A web-based application is developed using Streamlit, allowing users to upload data and receive real-time predictions. The integration of two distinct data modalities enhances diagnostic accuracy and provides a non-invasive, cost-effective alternative to traditional methods. Experimental results validate the effectiveness of both models independently, demonstrating that a combined system can provide a more reliable screening tool. This project highlights the potential of AI-powered healthcare tools in supporting early-stage Parkinson’s diagnosis, particularly in remote or resource-limited settings.
Smart Lightning Solutions for Smart City with Solar Energy
The increasing demand for sustainable urban development has led to the emergence of smart cities that leverage modern technologies to improve the quality of life, optimize resources, and reduce environmental impact. This paper proposes Solar Smart City framework that integrates Internet of Things (IoT) technology with solar energy systems to create an intelligent, energy efficient, and eco-friendly urban environment. The system utilizes smart sensors, actuators, and wireless communication to monitor and control solar power generation, distribution, and consumption in real time. Key components include smart street lighting, solar-powered public infrastructure, energy-efficient buildings, and centralized monitoring systems. Data collected from various nodes is analysed using cloud computing and machine learning algorithms to optimize energy usage, predict maintenance needs, and enhance decision-making. The integration of IoT with renewable energy not only reduces dependency on fossil fuels but also ensures resilience and sustainability in urban planning. The proposed system demonstrates a scalable and cost-effective solution to future-proof cities while contributing to global carbon reduction goals.
Studies On Static and Fatigue Properties of Al2219 Reinforced with B4C
The static and fatigue behavior of Al 2219/B4C particle metal matrix composites are examined in this work. Using a stir casting procedure, 200 micron B4C particles with different weight percentages of 4 and 6 reinforced to create the composites. To examine how reinforcing particles improved fatigue and static behavior. The generated composites' performance compared to that of the alloy Al 2219.The outcome shows that adding B4C increases the fatigue strength of composites over the alloy. Using an optical microscope, the uniform distribution of the reinforcement particles within the matrix investigated. Additionally, both attributes significantly improved when the weight percentage of B4C reinforcement in the matrix was increased. There were fewer cycles as the load increased, suggesting that the load had a greater impact on the fatigue strength. In general, flexural strength and fatigue strength.
particulate metal matrix composites (MMCp)
Precision Agriculture through Smart Irrigation using IOT and Hybrid Machine Learning
Optimized water management in agriculture is a critical issue, especially in water-scarce regions. This work introduces an IoT-based Automated Irrigation System that utilizes real-time sensor feedback, machine learning, and weather forecasting to manage water efficiently. The system utilizes Node-RED and HiveMQ(MQTT) for convenient communication and control, employing ESP8266 microcontrollers to interface with soil moisture, temperature, and humidity sensors. Moreover, external weather data is retrieved through the Open Weather API to enhance irrigation scheduling accuracy. The machine learning model trained to predict the irrigation need based on environmental and sensor inputs allows the system to automate motor operation with minimal human intervention. The model learns and adapts continuously to changing climate patterns and soil types, thus improving reliability and efficiency. This method not only saves water but also aids in sustainable agriculture. The system has been proven in a laboratory setting with encouraging results, showing that it can be scaled up for deployment in smart farming.
Traffic Signal Timing and Real Time Optimization
The optimization of traffic signal timing has evolved from simple fixed schedules to dynamic, real-time adaptive systems empowered by artificial intelligence (AI) and machine learning (ML). As urban traffic congestion intensifies and environmental sustainability becomes critical, intelligent traffic control systems are poised to play a central role in modern smart cities. This review synthesizes recent advancements in real-time traffic signal optimization, highlighting methodologies such as deep reinforcement learning, evolutionary algorithms, and multi-agent systems. While experimental results show significant improvements in traffic flow, emissions reduction, and travel times, challenges around scalability, robustness, and data security persist. This article outlines key gaps and proposes future directions, emphasizing the need for resilient, explainable, and scalable traffic management systems.
In industrial environments, unexpected machinery failures can result in expensive downtime along with SAP AI Joule marks a pivotal advancement in enterprise AI, serving as a dynamic copilot that augments user productivity, enhances decision-making accuracy, and accelerates workflow automation across SAP’s cloud portfolio. By embedding generative AI and conversational interfaces into core business applications, Joule is redefining how enterprise users interact with data and processes. This review synthesizes Joule’s supported use cases, experimental impacts, architectural principles, and prevailing challenges. While early results are promising, sustained success will depend on advancements in AI transparency, domain adaptation, ethical governance, and scalability. Future research must prioritize explainable AI, federated learning approaches, and dynamic adaptability to truly unlock Joule’s transformative potential for the intelligent enterprise.
Building Self-Healing Enterprise Workflows with AI-Driven Observability and Remediation
The increasing complexity of enterprise IT ecosystems demands workflows that are not only resilient but capable of autonomously detecting and recovering from failures. AI-driven observability, combined with automated remediation engines, presents a promising pathway to realize self-healing enterprise systems. This review consolidates current research on anomaly detection, root cause analysis, and AI-based remediation strategies. While notable progress has been made, challenges such as explainability, training data scarcity, and robustness under dynamic environments persist. We conclude by outlining future research directions aimed at enhancing system adaptability, fairness, and trustworthiness, paving the way for more intelligent and resilient enterprise infrastructures.
Synthetic Data Generation and Privacy-Preserving AI
Synthetic data generation has rapidly emerged as a cornerstone technology for achieving privacy-preserving artificial intelligence (AI). In light of tightening data protection regulations and the growing ethical emphasis on safeguarding personal information, researchers have developed a range of methods to synthesize realistic datasets without compromising individual privacy. This review presents a comprehensive synthesis of existing approaches, focusing on generative adversarial networks (GANs), variational autoencoders (VAEs), and Bayesian techniques. We systematically evaluate these models based on data utility, privacy guarantees, and vulnerability to adversarial attacks. Despite significant progress, challenges such as utility-privacy trade-offs, model bias, and lack of standard evaluation metrics persist. This paper highlights these gaps and proposes strategic future directions for the research community, advocating for hybrid models, interpretability-focused synthetic generation, and cross-disciplinary collaborations to achieve more trustworthy AI ecosystems.
Surviving Liquidity Shocks: Insights and Lessons from The Recent Banking Crises
Financial institutions worldwide still face liquidity shocks through rapid deposit outflows, wholesale funding scarcity, and failure of interbank lending. Banking crises in recent years have highlighted that vulnerabilities are still left to be resolved, even just after the reforms that came post-2008, particularly during accelerated market conditions. This review studies the critical mechanisms that generate liquidity stress and the channels through which liquidity stress can propagate, discusses empirical findings from recent episodes of liquidity stress, and comments on the efficacy of the risk management tools used by supervisors and the policy responses that were taken. In addition, the paper presents a theoretical framework for multi-layered liquidity defense and suggests directions for further research in stress testing, digital banking risks, regulatory design, and as well as behavioral triggers for liquidity events. The goal is to advise a more shock-resilient financial system that absorbs liquidity shocks without destabilizing the financial system as a whole.
AI-Driven DevSecOps: Advancing Security and Compliance in Continuous Delivery Pipelines
The inclusion of Artificial Intelligence (AI) into the DevSecOps pipelines has revolutionized the way organizations think of the implementation of security and compliance in the Continuous Delivery pipelines. The results on the performance, accuracy, and flexibility have been measurable in the dynamic software environments wherein the teams have utilized AI-enhanced static analysis, vulnerability scanning, real-time threat detection, and automated compliance validation. This paper critically reviews the current state of practice in using AI for the support of DevSecOps tools and methodologies, evaluates empirical performance, and identifies the building blocks of the core architecture and operations of these emerging weapons in the fight against software insecurity. The end of the paper outlines future research directions to overcome the remaining model explainability, data privacy, standardization, and regulatory compliance challenges. The findings indicate that AI plays a major role in expanding and transforming the existing DevSecOps practices and the way security can be used in software engineering workflows.
Machine Learning in Software Engineering
Computer Vision Techniques for Detection and Tracking of Surgical Instruments in the Operating Room
In response to the ever-growing demand for real-time intraoperative intelligence in modern operating rooms, surgical instrument detection and tracking as a part of computer vision techniques for surgical instruments has made significant progress. These methods are applied as tool usage analysis and skill evaluation, autonomous assistance, and surgical workflow optimization. Deep learning architectures like CNNs, transformers, as well as HA, although they have recently emerged for SPTIC, still face challenges associated with occlusion, visual ambiguity, domain variability, or real-time performance. The state-of-the-art approaches, their experimental performance are reviewed, and gaps preventing clinical deployment are identified. The emphasis is made in terms of the evaluation metrics, the algorithmic advancements, and the system integration. The last section concludes with discussions of unresolved issues and possible directions to further develop the clinical utility of these technologies.
Bridging the Technological Divide: Upgrading Enterprise Legacy Applications to Cutting-Edge Architectures
Modern organizations are increasingly held back by the enterprise legacy applications that are built on antiquated architectures that are still in use on outdated technologies. As cloud native paradigm drove microservice architecture and AI-driven infrastructure, the technological gap between legacy systems and the latest one increased significantly. This review reviews the current enterprise legacy modernization, analyzes the prevailing approaches like rehosting, refactoring, reengineering, and service-oriented migrations, and reports the empirical results from industrial cases. The article presents a basis for major technical and organizational challenges, addresses the role of automation and smart tooling in the transformation processes, and provides future research directions in order to achieve better transformation outcomes. Finally, it advises on the need for an integrated and strategic, with risk-managed, approach to bridging of legacy and modern computing worlds.
Autonomous Generative AI Agents in the Workforce: Transforming Industry Operations
The advent of autonomous generative AI agents is poised to revolutionize workforce dynamics across various industries. These AI agents, capable of independently generating solutions, designing systems, and performing complex tasks, promise to optimize operations, reduce human error, and foster unprecedented levels of innovation. This paper examines the potential of autonomous generative AI agents in transforming industry operations, including their implications on labor markets, productivity, and the future of work. By exploring advancements in machine learning, robotics, and natural language processing, the paper highlights how AI agents are reshaping tasks traditionally carried out by humans and their potential to redefine organizational structures. It also addresses the ethical, economic, and regulatory challenges that arise with the deployment of such technologies.
Cloud ERP Implementations: A Comprehensive Guide to Oracle Financials and Master Data Management
Cloud-based Enterprise Resource Planning (ERP) systems have revolutionized how organizations manage their financial operations, data governance, and compliance requirements. Among the leading solutions, Oracle Financials Cloud, when integrated with Master Data Management (MDM) systems, offers unparalleled capabilities in automation, real-time analytics, and financial control. This review presents a comprehensive examination of Oracle Financials Cloud architecture, MDM integration, implementation strategies, and empirical performance outcomes across industries. Drawing from over a decade of research and practical deployments, the paper identifies critical success factors, common pitfalls, and emerging trends shaping the ERP landscape. The review concludes with strategic insights for future research and practical innovations in Cloud ERP systems, particularly as artificial intelligence (AI), data governance, and sustainability become increasingly central to enterprise technology roadmaps.
Design Verification and System-Level Verification: Methodologies for Ensuring Robust Systems
As system complexity increases exponentially in industries such as automotive, aerospace, and consumer electronics, the demand for comprehensive design verification and system-level verification has intensified. Traditional verification techniques like simulation and formal methods, though essential, are increasingly being complemented by AI-driven strategies and hybrid verification frameworks. This review synthesizes research trends, practical methodologies, and experimental insights into scalable and efficient verification approaches. The review concludes by emphasizing the need for adaptive, intelligent, and sustainability-aware verification methodologies to address the growing demands of modern digital systems.
Performance Optimization and Cost Control in Snowflake: A Strategic Approach
As organizations increasingly migrate critical data workloads to cloud-native platforms, Snowflake has emerged as a leading data warehouse solution offering flexibility, scalability, and performance. However, its utility-based pricing model introduces new complexities in managing cost and optimizing performance. This review provides a strategic analysis of Snowflake’s architectural elements, AI-driven optimization approaches, cost governance techniques, and workload management best practices. Experimental results demonstrate that intelligent orchestration, auto-scaling, and query optimization can lead to performance gains of up to 50% and cost savings of 30–50%. The paper also introduces a theoretical model that combines AI prediction, policy enforcement, and dynamic warehouse orchestration to enable adaptive, cost-efficient resource management. We conclude with future research directions emphasizing real-time analytics, deeper AI integration, and multi-cloud interoperability.
A Comparative Study of Machine Learning and Deep Learning Approaches for Enhancing Intrusion Detection in IOT Environments
With the increasing deployment of IOT and Edge Cloud environments, ensuring the security of these systems against cyber-attacks bas become a critical challenge. Traditional intrusion detection systems (IDS) often fall short in addressing the complexity and scale of attacks in such heterogeneous environments. This paper aims to explore machine learning (ML) and deep learning (DL) techniques for efficient attack detection in IOT environments, with a focus on evaluating the performance of various models. The objective was to analyses the effectiveness of model, i.e., XGBoost (XGB) in identifying attacks using the TON-IoT dataset. The findings showed that XGB achieved an accuracy of 95.04%. The paper concludes that advanced ML and DL models, especially ensemble methods, offer significant improvements in intrusion detection. The novelty of this work lies in comparing various state-of-the-art approaches on a insights for enhancing security in Edge Cloud environments.
Design and Simulation of Electric Vehicle Powertrain Kinematics and Components Using MATLAB/Simulink
The introduction of electric power trains is a significant factor that has made it possible to come up with power trains with unique characteristics needed for EVs. In this paper, the simulation of electric vehicle powertrain and Modeling and simulation of electric vehicle powertrain with MATLAB/Simulink has been made in depth for the design of Electric vehicle powertrain. This include detail modeling of the elements of the powertrain including the electric motor, the transmission system, and the battery pack together with the vehicle dynamics. In this case kinematic analysis of mechanical and electrical subsystem is done in order to determine compatibility for torque and speed of different cycles of drive. This allows for characterization of the component’s performance during dynamic situations and the assessment of the entire system response, power consumption and drivetrain dynamic characteristics. The outcomes confirm MATLAB/Simulink to be a viable tool in the study of Electric powertrains as a vehicle concept and an effective medium on pre Relative Layout stage and compressed electrical automobile advancement.
Comprehensive survey on Exploratory Data Analysis and Machine Learning Approaches for Lung Cancer Detection
Lung cancer continues to be a leading cause of death, and hence there is a significant need for early detection toimprove survival rates. This current research addresses some loopholes existing in the current diagnostic methods, namely overfitting and poor generalization capabilities, by integrating the techniques of exploratory data analysis and machine learning. This research employs regression algorithms and K-Nearest Neighbors (KNN) enhanced with Principal Component Analysis (PCA) to attain a classification accuracy of 95%. The proposed framework offers a scalable solution for the precise detection of lung cancer and addresses challenges in clinical diagnostics.
Deep Learning Model for Automated Landslide and Debris Flow Detection
Landslides and debris flows pose significant risks to human life, infrastructure, and the environment, necessitating timely detectionand monitoring. Traditional methods rely on manual analysis of satellite images and geological surveys, which can be time-consuming and prone to human error. This study introduces a novel approach using deep learning techniques, specifically the YOLOv11 architecture, to enhance image segmentation for accurate landslide and debris flow detection. By automating the identification of key risk areas, our method improves both the accuracy and efficiency of assessments. The dataset for this research was sourced from open access satellite imagery repositories and annotated using Roboflow, ensuring high-qualitytraining data. Model training and testing were conducted on Roboflow, utilizing its powerful computational resources. The results show that the YOLOv11 model effectively detects debris flow and landslide-prone areas, achieving high Dice and Intersection over Union (IoU) scores, validating its ability to produce consistent and precise detections. Furthermore, the adaptability of the YOLOv11 model enables it tobe trained on diverse datasets, making it applicable to various geographical regions and terrain types by leveraging the power of deep learning and advanced image segmentation techniques, this research aims to contribute to the development of more effective and efficient disaster management strategies, ultimately reducing the impact oflandslides and debris flow on communities and the environment.
The drug discovery process has always been a problem for the pharmaceutical industry’s low rates of success, high expenses, and long-time frames. Quantum computing (QC) is being touted as a revolutionary advancement because of its unparalleled speed to carry out intricate multi molecular simulation and optimization. Unfortunately, its practical use has been suppressed due to the absence of proper hardware and the integration problems with traditional systems. Our solution is a quantum-classical pipeline for drug discovery. This pipeline utilizes machine learning (ML) for protein-ligand binding, drug property optimization, and even de novo drug design. In this paper we describe the pipeline, its components, the way it operates using NISQ devices, and the improvements it has in terms of accuracy and scalability (93.5% vs 78.2%) when compared with classical methods. Finally, we summarize how this can aid in modern drug discovery. It could significantly decrease the cost and timeline for pharmaceutical innovations.