Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC)
Sparklinglight Transactions on Artificial Intelligence and Quantum Computing (STAIQC) is a prestigious, peer-reviewed, double-blinded, biannual journal dedicated to advancing cutting-edge research in the rapidly evolving fields of Artificial Intelligence (AI) and Quantum Computing. Published by Sparkling Light Publisher, STAIQC stands as a beacon for scholars, researchers, and industry professionals who seek to explore the synergy between AI-driven innovations and quantum computational paradigms. With a strong Editorial Board comprising leading experts and a rigorously tested rapid review system, the journal ensures the publication of high-quality, impactful research that pushes the boundaries of scientific knowledge. STAIQC is committed to fostering intellectual discourse by welcoming original research articles, reviews, and breakthrough findings that redefine the future of computing and intelligent systems. Indexed in reputed databases, the journal (e-ISSN: 2583-0732) serves as a global platform for pioneering discoveries, bridging the gap between theoretical advancements and real-world applications in AI and Quantum Computing. Publishing Timeline Time to first decision: 6 days Peer review duration: 90 days Submission to acceptance: 180 days Acceptance to publication: 4 days
AI-Powered Skin Disease Detection with IKS-Inspired Remedies
Skin conditions rank among the most widespread health problems worldwide and typically demand prompt diagnosis and effective treatment to avoid serious complications. Traditional clinical diagnosis faces several significant obstacles, including limited access to qualified dermatologists, varying levels of medical expertise among practitioners, and potential misreading of symptoms and visual signs. This research presents a proposed solution to address these critical gaps through an artificial intelligence-powered diagnostic system specifically designed for skin disease identification. The innovative system incorporates treatment recommendations based on Indigenous Knowledge Systems, creating a unique blend of technological advancement and traditional healing wisdom. The approach leverages human understanding of pattern recognition in skin appearance to identify various dermatological conditions while supporting clinical decision-making through sophisticated computer-based analytical tools. Researchers trained and evaluated this system using a carefully assembled dataset that represents a broad spectrum of skin diseases and conditions. Beyond simply identifying the specific disease, the system provides comprehensive treatment suggestions that merge traditional Indigenous Knowledge Systems with contemporary medical approaches. This integration promotes a more complete and patient-focused healthcare methodology that respects cultural practices while maintaining medical effectiveness. Experimental testing demonstrates impressive accuracy rates in disease classification, with additional explainability features that enhance clinical reliability and build trust among medical professionals. These transparency mechanisms help doctors understand how the system reaches its diagnostic conclusions, supporting more confident treatment decisions. The convergence of Indigenous Knowledge Systems and artificial intelligence technology creates an innovative, equitable, and culturally appropriate healthcare solution. This approach proves especially valuable in rural and underserved communities where access to specialized medical care remains limited, offering hope for improved health outcomes across diverse populations.
An AI-Powered System for Real-Time Underwater Plastic Detection and Classification Using YOLOv7 and VGG16
Plastic pollution in marine environments poses a severe threat to aquatic ecosystems and global biodiversity. The traditional methods used to monitor the wastes in the sea are time consuming and in virtually every situation, not real time responsive. In order to achieve effective and efficient plastic underwater detection and classification, the current paper proposes a hybrid architecture that incorporates YOLOv7, a newly emerging object detector architecture, and VGG16, a deep convolutional neural network. The system is able to detect the plastic waste and classify it into different categories, such as bottles, bags, nets, and wrappers. An SMTP notification system is proposed to simplify the detection, while an email notification with annotated detection result is recommended to be sent at each plastic detection. It could be to train YOLOv7 on large amounts of underwater images to predict where they are in general and then use VGG16 to predict exactly where they are in cropped regions. The tests show that the system is highly accurate and performant under extreme conditions in the under-sea environment, including turbidity or low visibility. The automatic alerter also provides early alerting and immediate response and remediation. Overall, the book is a brilliant, measured, terrestrial effort to solve the problem of marine waste disposal and contributes to the body of literature on environmental protection and building a sustainable ecosystem.
AI-Based Pothole Detection for Road Safety Enhancement Using Computer Vision and Deep Learning
Potholes can be a real headache. They not only annoy drivers but can also cause accidents, damage vehicles, and lead to costly repairs. Traditionally, spotting these pesky road hazards has meant relying on manual inspections, which can be slow and often miss the mark. But thanks to advancements in artificial intelligence and computer vision, Now we have better ways to identify potholes using rea- time video. We trained the model on a custom dataset (annotated with Roboflow) and enhanced it with data augmentation so it performs reliably in challenging lighting and weather conditions. The software performs real time analysis of live video through equipments such as webcams or dashcams, then identifies potholes in that time, and marks them with bounding boxes around and confidence scores It does records every detection made , both in a local CSV file and in a Firebase Realtime database, so the data can be centrally tracked and analyzed and well monitored. We also made sure the application is user-friendly, meaning easy to use. It provides features such as simple graphical interface built with Tkinter and OpenCV, allowing users to start the detection process, see results live, and access logged data hassle- free. With good accuracy of—92.1% precision and a mean average precision (mAP) of 93.2%—this system provides both reliable and speedy. And it’s designed to work on smaller devices like Raspberry Pi or Jetson Nano, making it compact with less space-consuming. This project can be made advanced by utilising the same in either smart cities or other put together with drones. In brief, this pothole detection system provides help to cities and huge transport authorities to automate road maintenance system, prioritizing safety over everything for everyone. By inserting the power of AI, we can sort the pothole problem more effectively and efficiently than ever before.
Email has become the most largely used official communication mechanism for most of the internet users. In the past few years, the Email usage has increased. Also developed and increased the problems caused by spam Emails. spam or junk email is referred to the act of shared large unrequested messages. Thus, keeping this mind, it is main to build a complete system for spam classification based on semantics based text classification using NLP and ML. The performance of good emerging ML methods and algorithms is reviewed and assessed in this study. Spammers use developed and creative methods to bring their illegal activities using spam emails
Privacy Preserving Medical Image Inference Portal Using Homomorphic Encryption
The growing reliance on AI for medical image analysis presents important privacy risks when patient data is processed on external servers. To solve this problem, we suggest a Privacy- Preserving Medical Image Inference Portal that uses Homomorphic Encryption (HE) to keep sensitive images encrypted while they are being processed. The system lets users upload encrypted medical images that a neural network processes without decrypting them, which keeps privacy safe. A lightweight model that is optimized for encrypted operations strikes a balance between accuracy and speed of computation. The portal has an easy-to-use interface for safe uploads and result retrieval, showing that advanced cryptography can be used in healthcare di- agnostics. This work shows a safe way to use AI in medical imaging that protects both privacy and usefulness.
AI-Driven Resume Analysis and Optimization System for Enhanced Job Market Compatibility
Abstract Modern recruitment processes heavily depend on Applicant Tracking Systems (ATS) that automatically filter candidate applications before human review. Job seekers face considerable challenges in crafting resumes that effectively pass through these automated systems while meeting recruiter expectations. This paper presents an innovative artificial intelligencepowered platform that analyzes resume content and provides optimization recommendations to improve job market compatibility. The proposed system integrates multiple Natural language processing constitutes a few among the AI techniques , semantic analysis, and machine learning algorithms to evaluate resume-job description alignment. The platform employs a hybrid architecture combining client-side processing using transformer models with cloud-based enhancement capabilities, ensuring data privacy while delivering comprehensive analysis. Implementation utilizes React framework for user interface development and incorporates PDF processing capabilities for document handling. Experimental evaluation with 100 resume samples demonstrated 89% accuracy correlation with expert assessments and 95% reduction in processing time compared to manual optimization methods. The system achieved 4.2/5 user satisfaction rating and showed significant improvement in ATS compatibility scores across diverse professional domains.
Artificial intelligence (AI) and machine learning (ML) are some of the major drivers transforming the job market all over the globe and become a major focus of the fourth industrial revolution. The transition has led to the increased and emerging need of professionals with specialized expertise in fields such as data science, deep learning, and natural language processing. The gap between the supply and demand of such qualified talent is also high, mainly because of the difficulty and ever changing nature of such jobs that needs a particular set of skills encompassing advanced programming, mathematical also other domain-specific skills. The current paper proposes to present the idea of AI Career Couch, a new system aimed at solving this severe problem as a highly individualized and smart career counselor. The system will take advantage of an AI-based framework to deliver systematic, data-guided guidance to aspiring professionals and existing professionals who are looking to be upskill. AI Career Couch functions by examining a broad spectrum of existing marketing advances and market statistics, completing the aptitude to predict, with exactness, the abilities required in certain AI professional position. It then presents customized suggestions and ways of action to enable people to fill their individual competency gaps.
Healthcare AI Web Application for Disease Prediction
This project develops a Healthcare AI Web Applica tion that leverages Using machine learning to spot diseases early provide users with instant, accessible health insights.This system runs on Flask and uses things like Random Forest and Logistic Regression to check out health data– stuff like blood pressure, sugar, age, the usual. It cleans up the data and learns from actual medical records to spot risks for diabetes, heart trouble, and other diseases.It’s got a simple website where patients and doctors can type in info and get quick predictions. The results are easy to see. It uses a safe SQLite database to store everything.The point here is to help people, especially where healthcare is hard to get, diagnoses take too long, and there aren’t enough specialists. It’s not meant to switch out a doctor, but it can help people keep an eye on their health.
Insurance Fraud Detection and Analysis for Insurance Claim
As global economies continue to expand, safeguard ing financial systems against fraudulent activities has become a critical priority. Among these, insurance fraud stands out as a major concern because it causes billions of dollars in losses for both companies and policyholders. With the increasing volume and complexity of claims, traditional methods of fraud detection are no longer sufficient. Advanced techniques such as data-analytics and machine-learning provide powerful-tools for addressing this challenge by automating the detection process and improving accuracy. To design an effective solution, it is first essential to study existing research and identify the most effective approaches that have been applied in this domain. Building on these insights, we propose a machine learning–based model capable of identifying suspicious insurance claims. Such a system not only assists companies in reducing financial losses and operational overhead but also enables quicker responses to potential fraud, thereby improving overall efficiency and trust in the insurance sector.
Machine Learning Based Approach For Forecasting Municipality Waste Generation
MSW management remains one of the most pressing challenges in rapidly expanding urban centers, and Mangaluru is no exception. Anticipating future waste generation allows city authorities to plan efficient systems for collection, treat- ment, and disposal, thereby reducing environmental impacts and protecting public health. This study focuses on predicting municipal solid waste production in the Mangaluru region using historical datasets across categories such as wet waste, dry waste, silt, sanitary waste, and mixed waste. The collected data was carefully preprocessed and examined to uncover seasonal variations and temporal patterns. A data-driven forecasting framework was developed using the Prophet time-series model, which provides reliable short- to medium-term predictions with confidence intervals. To improve usability, the forecasting system was deployed as a web application built on the Flask framework, incorporating secure authentication and role-based access con- trol. Users can select a target year and generate category-wise predictions, thereby supporting evidence-based decision-making. The findings demonstrate the model’s ability to reflect yearly fluctuations specific to Mangaluru and provide critical knowledge to guide sustainable waste management initiatives. Moreover, the framework can be extended by integrating external drivers such as demographic increase, patterns of urban expansion, and climatic factors like rainfall to further enhance accuracy. Overall, the project highlights the promise of advanced computational learning methods in strengthening urban sustainability and assisting Mangaluru’s transition toward smarter MSw practices.
Detection of Dental Caries Risk Detection Using Deep Learning
Dental caries is the most common oral diseases affecting individual worldwide , necessitating the development of advanced diagnostic methods. This study introduces a novel approach using a score based multi-input deep neural network convolution(CNN) for the effective detection of dental caries. Our model attempts to enhances the accuracy of caries detection and help dental professionals make well-informed treatment decisions by utilizing a variety of input data, such as radiographic images and clinical parameters. When tested on a large dataset, the suggested methodology outperformed conventional methods. According to the results, combining multiple input modalities can greatly enhance dental practice's diagnostic results. This study opens the door for future advancements in caries management and prevention in addition to making a contribution to the field of dental diagnostics.
Health Condition Forecaster Using Machine Learning
The growing burden of chronic diseases highlights the need for early and reliable prediction. This study presents a machine learning framework, the Health Condition Forecaster, to estimate risks for seven conditions: diabetes, heart disease, Parkinson’s disease, hypertension, stroke, liver disease, and lung cancer. Using cleaned and balanced clinical datasets, models in particular Tree Ensemble Model, Classification Tree, Logistic Regression, Naive Bayes, and K-Nearest Neighbors were evaluated. The findings indicate that achieved higher accuracy and consistency. The system, deployed through a simple clinical interface, demonstrates potential to support timely diagnosis and strengthen preventive healthcare.
DeepFake Shield AI-Based Detection Fake Videos and Images in Real Time
The availability of deepfake technologies has sparked critical concerns about information authenticity, individual privacy, and cyber security Deepfakes are computer-generated images and videos that have been edited using sophisticated computer methods, rendering them extremely realistic and frequently hard to detect with the naked eye. Deepfake Shield is an approach to detecting forged visual media, including images and videos, through the use of convolutional neural networks (CNNs).It works by analyzing important features of media in a bid to determine whether its genuine or not. The system is simple to use, and non-experts can check for counterfeit content in real time. It is also simple to incorporate into existing cybersecurity architectures and is light enough and scalable enough to deploy widely. It shows, in early testing, the capacity to detect falsified media with over 90% accuracy on well-used public datasets. Future work on the project will further develop the system with support for a variety of media types, enhance quality of describing predictions, and incorporating IoT-based alerts to react swiftly in actual situations.
AI-Driven MRI Analysis: Disease Prediction and Report Generation
Magnetic resonance imaging is a widely used non-invasive imaging technique for identifying and diagnosing a variety of diseases. Nevertheless, the conventional manual interpretation of Magnetic resonance imaging scans is time-consuming, labor-intensive, and susceptible to inter-observer variability. Because of the increasing volume of imaging data, radiologists need instruments for artificial intelligence that can help them spot abnormalities, predict illnesses, and generate accurate and efficient diagnostic reports. This research offers a framework powered by artificial intelligence that combines automated diagnostic report generation with natural language processing. The system is designed to analyze identify disease-related features, predict possible conditions, and generate diagnostic summaries that are fact-based and well-organized. The produced reports reduce the diagnostic burden and are consistent with clinical standards, and experimental evaluations indicate promising accuracy in disease classification and lesion segmentation. The research highlights the artificial intelligence's possibilities as a clinical radiology assistive tool, emphasizing enhanced diagnostic precision, reduced reporting time, and greater accessibility to healthcare.
SynapseCare: Machine Learning Based Health Risk Assessment and Personalized Feedback System
The global healthcare systems handle unprecedented challenges in managing chronic diseases and providing personalized care to diverse populations. Traditional reactive healthcare approaches often result in delayed interventions, increased costs, and suboptimal patient out- comes. This paper presents SynapseCare, an innovative machine learning-based platform that revolutionizes health risk assessment through predictive analytics and personalized feedback mechanisms. The system includes data collected from a number of sources, like wearable device metrics and electronic health records, lifestyle parameters, and demographic information to generate comprehensive health risk profiles. Advanced machine learning techniques, like deep learning models and ensemble methods, use patient data to predict potential health risks with high accuracy. The platform delivers personalized recommendations through an intuitive interface, empowering users to make medical choices in data. Experimental validation demonstrates significant improvements in early risk detection, with the system achieving 92% accuracy in cardiovascular risk prediction and 89% accuracy in diabetes risk assessment. SynapseCare indicates a change in views to proactive, data-driven healthcare that can reduce cost while increase results for patients by using early intervention and personalized care strategies.
Live Monitoring and Smart Threat Detection Using YOLOv8 Based Deep Learning Model
This study introduces a real-time surveillance framework aimed at detecting weapons, with a particular focus on handguns and knives. The approach leverages the YOLOv8 object detection model, chosen for its balance of precision and computational speed. Implemented in Python and integrated through a Flask-based web application, the system provides interactive and user-friendly monitoring options. With a dataset of nearly 4,000 annotated images under varied environments, the framework supports three modes: static image analysis, video stream evaluation, and live webcam monitoring. Experimental evaluation achieved an overall accuracy of 64%, demonstrating its potential for real-world deployment in enhancing security measures.
Harnessing Reinforcement Learning for Agile Portfolio Management in Nifty 50 Stock Analysis
This ground-breaking research paper introduces a novel application of Reinforcement Learning (RL) for portfolio management in the context of Nifty 50 stock analysis. Traditional portfolio management methods often suffer from static allocation models that lack adaptability to market dynamics, resulting in suboptimal performance and increased risk. In response to this limitation, we propose a pioneering approach that harnesses the power of RL to construct an agile and adaptive portfolio management system capable of dynamically adjusting stock allocations based on real-time market trends and risk assessments. By leveraging the learning capabilities of RL, we demonstrate the system's efficacy in creating resilient and flexible investment strategies, ultimately leading to optimized portfolio performance within the Nifty 50 index.
Literature Review on Early PCOS Detection on Girl Child Using Artificial Intelligence or Machine Learning
Metabolic syndrome and polycystic ovarian syndrome (PCOS) are prevalent hormonal disorders affecting many women, often leading to long-term health complications. Timely and accurate diagnosis is crucial for effective treatment and prevention of further issues. However, traditional diagnostic methods can be inconsistent and may delay proper diagnosis. This study investigates the transformative potential of artificial intelligence (AI) in the detection, classification, and segmentation of PCOS and its correlation with metabolic syndrome. By leveraging AI's vast clinical data learning capabilities, we explore how AI can notify the main feature related with both conditions. The paper emphasizes AI's self-correcting ability, which facilitates continuous improvements in diagnostic accuracy. Through AI, enhance risk assessments for PCOS and related conditions like metabolic syndrome, enable earlier and more precise diagnoses, and ultimately increase individualized treatment plans tailored to each patient's unique needs. This research explores AI's potential in PCOS and metabolic syndrome, with the potential to revolutionize patient care and health outcomes.
Classification of Factors Used to Predict Vehicle Break down in Commercial Vehicles
Prediction of vehicle breakdowns in commercial vehicles is of utmost importance to ensure the safety, reliability, and cost-effectiveness of road transport. In today's fast-paced and competitive business landscape, the smooth operation of commercial vehicles is crucial for ensuring efficient logistics and supply chain management. Unplanned breakdowns of commercial vehicles can lead to significant financial losses, delays in delivery schedules, and potential damage to the reputation of the transportation companies. Commercial vehicle breakdowns pose significant challenges to both fleet operators and the broader transportation industry. The unexpected failure of a commercial vehicle can result in substantial costs, operational disruptions, and safety hazards. In light of these considerations, the development of effective breakdown prediction models has become an essential endeavor. Predicting when and why a breakdown might occur not only allows for proactive maintenance but also minimizes the economic and safety risks associated with breakdowns.
Commercial vehicle breakdown prediction parameters
prediction models and classification of parameters
Applicability of Machine Learning towards development of Patient Centered Decision Support System to Palliative Care Settings
Developing systems for assisting in the care of palliative patients requires an understanding of providing relief from distressing symptoms to ease suffering and improve the Quality of Life. To assess the applicability of Artificial Intelligence techniques to Palliative care settings, it is important to understand methods suitable for symptom care and management such that they provide relief from distress due to the life-limiting illness. With rapid advancements in Artificial Intelligence, developing automated healthcare systems for patient care has revolutionized personalized care which is of significance in palliative care settings. This article summarizes the existing literature which suggests the need for improved end-of-life care for a better Quality of Life and the implementation scenarios where AI is integrated in healthcare systems to assist the palliative care team in decision making and thus enhance patient care.
A Systematic Evaluation of Artificial Intelligence's Impact on the Landscape of Threat Modeling
This article systematically evaluates the influence of Artificial Intelligence (AI) on the landscape of Threat modeling in cybersecurity. The study involves an extensive review of relevant journal articles, books, and conference papers to comprehensively assess the current state of the field. By synthesizing existing literature, we identify and analyze the ways in which AI technologies are applied in Threat modeling methodologies. The evaluation explores the strengths and limitations of these applications, shedding light on the advancements that have significantly enhanced Threat modeling. Furthermore, the research includes a meticulous gap analysis within the existing literature, revealing areas where further investigation is warranted. Identified gaps in the current research landscape serve as a foundation for proposing future research directions in AI-enhanced Threat modeling. The current literature related to the current landscape of Artificial Intelligence research in cybersecurity predominantly focuses on articles and active studies pertaining to the detection and prevention of cyber-attacks. However, there is a noticeable gap in the existing literature when it comes to leveraging Artificial Intelligence to enhance Threat modeling. While numerous innovative methods have been proposed in recent articles, these predominantly concentrate on Threat modeling within specific domains, such as unmanned vehicles, Cyber-Physical Systems, and Healthcare. There is a lack of generalization in applying these findings to improve Threat modeling practices more broadly. A promising avenue for further research lies in the automation of Threat modeling. Existing literature predominantly emphasizes the study of Threat generation areas, leaving other crucial aspects, such as Architecture representation and Model validation, in need of more comprehensive exploration and analysis.
Systematic Literature Review on Parallel Sorting and Searching Algorithms for Graph Analytics Problems
This research paper aims to systematically review the literature published recently on the topic of parallel processing algorithms for sorting and searching methods. The paper emphasizes finding the major gaps in the existing methods of using parallel sorting and searching methods for graph analytics problems and their use in scalable data science. Many peer-reviewed research papers and articles from international and national journals, conferences and other sources are searched using appropriate keywords and they are analyzed to understand the concept, current trend in the area and to find the research gaps, by employing a systematic review method of literature. The outcomes of this literature review have resulted in the need to undertake a research work on "Design and Implementation of Parallel Sorting and Searching Algorithms for Graph Analytics Problems in Scalable Data Science". Also, we have identified the required research objectives and its plan of execution for the research proposal. Systematic literature review, Topic of research proposal along with research objectives, Listing of SWOT analysis of the research agendas, Listing of ABCD analysis of the research proposal.
Innovative Deep Learning-Based Medical Report Analysis for Timely Diagnosis and Improved Healthcare
In contemporary healthcare, a novel web-based platform harnessing the power of deep learning has been innovated to support frontline medical staff during critical emergencies, especially in the absence of expert consultation. This system is primed to swiftly analyze medical records, emphasizing early detection to curtail severe health risks, including potential fatalities. The foundation of this tool is rooted in deep learning algorithms, which sift through vast medical data, revealing patterns often overlooked by human eyes. By augmenting precise disease identification, the system strengthens decision-making in clinical settings. Its design fosters synergy between the healthcare sector and specialized bodies, ensuring its adaptability to the evolving medical landscape. This fusion of artificial intelligence empowers healthcare practitioners by highlighting immediate risks, enriching patient care efficiency, and integrating fluidly with prevailing operational protocols. The tool's proficiency in real-time anomaly detection aids clinicians in proactive decision-making, minimizing catastrophic health outcomes. Its pioneering application has demonstrated efficacy in early diagnostic evaluations for a spectrum of six predominant ailments, encapsulating succinct insights for each. With an emphasis on processing medical images, including X-rays, the deep learning models display exemplary performance in training and diagnostics. The system, crafted with streamlit, is intuitively designed for emergency scenarios and is fortified for scalability through Docker containerization and cloud hosting. While this initiative underscores the transformative potential of deep learning in health analytics, it heralds the dawn of an era where medical verdicts become more pinpointed, timely, and instrumental in preserving life.
Prediction of Diseases Based on Facial Diagnosis Using Deep Learning
Practitioners can diagnose diseases by looking at a patient's facial features, a process known as facial diagnosis. Doctors need to have a lot of real-world experience to diagnose facial conditions with high accuracy. Modern medical studies show that many diseases do, in fact, manifest matching unique traits on human faces. Due to the scarcity of medical resources, it is challenging to get a check-up today in many rural and undeveloped areas, which frequently causes treatment to be delayed. Limitations still exist, such as high expenses, lengthy hospital waiting-times, and conflicts between doctor and consulted patient that result in medical disputes, even in major cities. We can rapidly and easily do non-invasive screening and disease detection thanks to computer-aided face diagnosis. To perform CA facial-diagnosis of various diseases and test DTL methods for both single and multiple disease spotting on a short data-set, we therefore propose adopting deep transfer learning from face recognition.
A Novel Approach to Strengthen Additional Layer of Security to Caesar Cipher
We are living in the world of technology where we share our messages to the sender via several messaging applications. While we share the information, we are not aware about how secure our messages are and whether any person can hack the private messages or not. It is obvious when the messaging applications with no security pose great risk to our private information. Cryptography is a technique which is used to hide the information in the form of encryption. This preserves confidentiality, integrity and availability and also provides security and privacy of data to the users. In this study, we have developed a simple approach with an additional layer of security to be added over the Caesar Cipher to enhance the security that can be incorporated with messaging applications.
A Machine Intelligent Framework for Detection of Rice Leaf Diseases in Field Using IoT Based Unmanned Aerial Vehicle System
Rice is an important food in our day-to-day life. It has rich sources of carbohydrates that are highly essential for body growth and development. Rice is an important crop in agriculture, where it enhances a country’s economy. However, if rice plants are diseased and not monitored regularly then the crop in the field is wasted and it reduces the proper production rate. Therefore, there should be a mechanism which regularly monitors the crop in a field to detect any disease to rice plant. In this paper, a framework is proposed for identification of rice leaf disease using IoT based Unmanned Aerial Vehicle (UAV) system. Here, the UAV monitors an entire field, capture the images and sends the images to the machine intelligent cloud for detection of rice leaf diseases. The cloud is installed with a proposed stacking classifier that classify the diseased rice plant images received from UAV into different categories. The dataset of these rice leaf diseases is collected from Kaggle source. The performance of the stacking classifier installed at the cloud is evaluated using Python based Orange 3.26 tool. It is observed form the results that stacking classifier outperforms the conventional machine learning models in detecting the actual disease with a classification accuracy (CA) of 86.7%.
A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask
The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All people should wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the people who have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower the spread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images (HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, a machine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200 HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD.
Quantum Transfer Learning Approach for Deepfake Detection
Deepfake image manipulation has achieved great attention in the previous year’s owing to brings solemn challenges from the public self-confidence. Forgery detection in face imaging has made considerable developments in detecting manipulated images. However, there is still a need for an efficient deepfake detection approach in complex background environments. This paper applies the state-of-the-art quantum transfer learning approach for classifying deepfake images from original face images. The proposed model comprises classical pre-trained ResNet-18 and quantum neural network layers that provide efficient features extraction to learn the different patterns of the deepfake face images. The proposed model is validated on a real-world deepfake dataset created using commercial software. An accuracy of 96.1 % was obtained.
Optimized Skeleton graph based CNN for Human Abnormal Detection in Video Streams
Human Action Recognition (HAR) is the process of understanding human actions and behavior. HAR has a broad range of applications, and it has been focused on increasing the attention in various domain of computed vision. Abnormal detection from video stream is vigorous to guarantee the security in both outside spaces with the internal. Furthermore, the abnormal actions are really infrequent and rare, which makes the supervision process more challenging and difficult. In this research, skeleton graph-based Convolutional Neural Network (CNN) is devised for human abnormal activity detection. Here, the skeleton graph-based CNN (Skeleton graph_CNN) is devised based on the concept of classical convolution and skeleton graph generation. The human action recognition classifies the human actions into normal and abnormal class. The abnormal actions from the recognized outcome are detected with Skeleton graph_CNN, which provides the various actions of human as an output. The Skeleton graph_CNNgenerates the skeleton shaped human structure by connecting the joints within the frame to consecutive frames. Moreover, the HAR is carried out using IITB-Corridor Dataset based on metrics, such as testing accuracy of 0.961, sensitivity of 0.956 and specificity of 0.960, correspondingly.
Comparative Analysis of AODV and Optimal Residual Energy Selection AODV Protocol for MANET
The energy consumption of Mobile Ad Hoc network has become a crucial issue of Mobile Ad-Hoc networking, the issues such as link break, path loss, and packet loss. In the proposed work a protocol known as Optimal Residual Energy Selection ORES-AODV is introduced and the protocol is able to calculate the sum of mean value of the residual energy of the nodes in the path. Also, the average of least residual energy of the nodes in the path. And the path selection is based on the difference of the mentioned metrics in two separate nodes. Finally, the optimal path is selected for the data transmission by the protocol.