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491 articles for “Detection Challenges”
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Hybrid Techniques in Mango Leaf Disease Identification: Evaluating Neural Networks and Support Vector Machines
Abstract: Mango leaf diseases pose a significant threat to mango production, impacting both yield and fruit quality. Early and accurate detection of these diseases is crucial for effective management. This paper evaluates the use of hybrid techniques, specifically the integration of neural networks (NNs) and support vector machines (SVM), in the identification and classification of mango leaf diseases. NN excel in extracting complex features from images, while SVMs are robust classifiers, …
Published in Trends in Opto-electro & Optical Communication · Vol. 14, Issue 3, 2024 · pp. 19–27 Read article
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AI- based Prediction of Misinformation Virality Before Wide Dissemination using Attention-based Multi-modal
Abstract: Misinformation on social media has emerged as a critical global challenge, impacting public health, democratic institutions, and societal trust. While existing research has largely concentrated on detecting misinformation after it begins circulating, predicting its virality before wide dissemination remains an underexplored area, limited work addresses predicting its virality before wide dissemination. This paper presents a conceptual framework using attention-based multi-modal deep learning models to estimate the virality of misinformation posts …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 12, Issue 3, 2025 Read article
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Water Level Detection and Auto-cutoff
Abstract: The focus of this study is to explore Internet of Things and implement it to overcome a daily and reallife challenge. Additionally, we will talk about potential barriers to development. Throughout the study, we would cover the concept of Internet of Things, dealing with the definitions given in the past. We would also talk about the basic communication models of IoT (Internet of Technology) Devices. Moreover, we would consider a …
Published in Journal of Communication Engineering & Systems · Vol. 13, Issue 1, 2023 · pp. 33–40 Read article
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Depiction-inspired Recipe Generator Using Deep Learning
Abstract: Machine learning has become a crucial part of modern life, influencing various domains. Its applications range from enhancing data-driven business decisions to enabling autonomous vehicles. Advances in machine learning have brought about notable changes in how we interact with technology. In the culinary world, the idea of creating food recipes from images has gained increasing interest. This entails the development of innovative systems that seamlessly convert visual input, such as …
Published in Journal of Open Source Developments · Vol. 11, Issue 2, 2024 · pp. 47–55 Read article
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The Impact of Climate Change on Animal Health and Veterinary Practices
Abstract: Climate change poses significant challenges to animal health, altering disease dynamics, impacting livestock productivity, and threatening wildlife populations. Rising global temperatures, changing precipitation patterns, and increased frequency of extreme weather events have direct and indirect effects on both domestic animals and wildlife. Heat stress in livestock, for instance, reduces productivity, fertility, and overall well-being. Water scarcity, malnutrition, and changing ecosystems also compromise the health of animals, making them more susceptible …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 13, Issue 3, 2024 · pp. 26–30 Read article
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Real-Time Ocean Monitoring and Early Warning Systems with IoT Technology
Abstract: The increasing frequency of extreme weather events, rising sea levels, and threats to marine biodiversity This paper explores the application of IoT in enhancing real-time ocean monitoring and early warning systems, focusing on the deployment of smart sensors, connected buoys, and data analytics to collect key parameters such as temperature, salinity, pH, and wave activity. To preserve and responsibly utilise the seas, oceans, and marine resources in support of sustainable …
Published in Journal of Instrumentation Technology & Innovations · Vol. 15, Issue 1, 2025 · pp. 13–24 Read article
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Open-Source Software Empowering Artificial Intelligence, Machine Learning, and Cyber Security: A Comprehensive Research Study
Abstract: Open-source software (OSS) has become a foundational pillar for rapid innovation across artificial intelligence (AI), machine learning (ML), and cybersecurity. This paper delivers a comprehensive, journal-length analysis of OSS-driven ecosystems, emphasizing collaborative development, transparency, and accelerated deployment. By providing freely available libraries, tools, and frameworks, OSS makes it easier for developers and researchers to experiment, build models, and deploy solutions quickly. This study examines how OSS can be combined with …
Published in Journal of Open Source Developments · Vol. 13, Issue 1, 2026 · pp. 08–15 Read article
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Milk Allergy: Significance, Detection and Management
Abstract: Milk allergy is a significant public health issue, particularly among infants and young children, with prevalence rates between 2 and 6% in early childhood, declining to 0.1–0.5% in adulthood. It is an immune-mediated adverse reaction to milk proteins, primarily involving immunoglobulin E (IgE)-mediated responses. Symptoms range from gastrointestinal distress and respiratory complications to severe anaphylaxis. The etiology of milk allergy involves genetic predisposition, environmental factors, an immature immune system, and …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 14, Issue 1, 2025 · pp. 15–19 Read article
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Advancements in Nanotechnology and Biosensor Integration for Detection and Treatment of Alice in Wonderland Syndrome
Abstract: Alice in Wonderland Syndrome (AIWS) is an uncommon neurological condition characterized by profound distortions in perception. Individuals with AIWS experience altered body image and spatial awareness, often perceiving objects, surroundings, or even their own body as being unusually large, small, or distorted. The condition presents a unique challenge for both diagnosis and management due to its elusive and varied symptoms. This paper explores how advancements in nanotechnology and biosensor integration …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 26, Issue 3, 2024 · pp. 1–12 Read article
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Transformative Breakthroughs: Revolutionizing Potato Disease Detection Through Machine Learning
Abstract: Advancements in agricultural technology and the integration of artificial intelligence for diagnosing plant and leaf diseases are crucial for sustainable agricultural development. Conditions like early blight and late blight exert a notable influence on both the quality and quantity of potato harvests. Identifying these leaf diseases manually demands significant labor and a considerable level of expertise. Therefore, efficient, and automated methods for disease detection are essential to improve potato production. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 1, 2024 · pp. 54–62 Read article
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Classification of Plant Leaf Diseases Using Deep Learning Concepts
Abstract: Agriculture is vital to the economy of a country like India, where 70% of the workforce is employed in this sector. Plants suffering from illnesses experience a significant reduction in output. Delays in the identification of plant diseases lead to decreased yield and plant mortality. The cost of manufacturing is increased since it takes a big number of experts to manually detect plant diseases over several acres of land. The …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 46–55 Read article
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Fake Cryptocurrency Detection Using Python
Abstract: This study investigates the use of Python-based techniques for detecting fraudulent cryptocurrencies, addressing a growing concern in the digital financial ecosystem. The research methodology integrates various data science approaches, including web scraping, API integration, and advanced data analysis using Pandas and NLTK. Machine learning models, particularly classification algorithms such as Random Forest, are employed to analyze key features extracted from cryptocurrency whitepapers, social media discussions, and transactional data. By training …
Published in Recent Trends in Programming languages · Vol. 12, Issue 1, 2025 · pp. 1–7 Read article
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The Role of Artificial Intelligence in Automating Incident Response in Cloud-Based Cybersecurity
Abstract: As cloud computing continues to gain traction across industries, the complexity and scale of cloud environments present significant challenges to traditional cybersecurity practices. The dynamic and distributed nature of cloud infrastructures necessitates agile and effective incident response mechanisms to detect, analyze, and mitigate threats in real-time. However, conventional incident response methods often fall short due to the growing sophistication of cyber threats and the vast amounts of data generated in …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 15–24 Read article
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Development of a Low-Cost Autonomous Robot for Obstacle Avoidance Using Ultrasonic Sensing
Abstract: In the evolving landscape of automation, autonomous mobile robots are becoming critical for performing tasks with minimal human intervention. This project presents the design and development of a cost-effective, small-scale obstacle-avoiding robot using an Arduino microcontroller and an ultrasonic sensor. The robot operates by scanning its surroundings, identifying nearby obstacles, and navigating by altering its path in real time. Through intelligent programming and sensor integration, the system achieves smooth, collision-free …
Published in Journal of Advancements in Robotics · Vol. 13, Issue 1, 2026 · pp. 1–11 Read article
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CNN-Based Diagnosis of Skin Cancer from Dermoscopic Images
Abstract: Skin cancer has become one of the diseases widely spread over the globe, with melanoma becoming a severe threat to one’s health. Detection of such diseases at the initial stage saves an individual from drastic damage. Using a Convolutional Neural Network (CNN) for detecting skin cancer through image classification as benign or malignant provides significant support to dermatological practice and reduces dependence solely on subjective visual examination. Dermatologists often face …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 15, Issue 1, 2026 · pp. 37–42 Read article
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Animal Detection in Farms Using Opencv
Abstract: Agriculture plays a fundamental role in sustaining the Indian economy, providing employment and livelihood to a large portion of the population. Despite advancements in farming techniques, one of the persistent challenges faced by farmers is the intrusion of wild animals into agricultural fields. Such intrusions often lead to large-scale crop damage, financial loss, and emotional distress for farmers. Traditional animal deterrent methods, such as manual patrolling, fences, or scarecrows, have …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 13, Issue 2, 2025 Read article
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Artificial Intelligence for Tracking Cognitive Deviation in Aging Populations: A Comprehensive Review of Techniques, Challenges, and Ethical Concerns
Abstract: Population aging is accelerating worldwide, and with it the burden of cognitive health conditions such as mild cognitive impairment (MCI), Alzheimer’s disease (AD), and dementia. Detecting and monitoring cognitive change early is central to timely intervention, yet conventional diagnostic tools often miss the subtle signals that appear before overt symptoms. Artificial intelligence (AI) has emerged as a promising complement to clinical assessment because it can work through high-dimensional data and …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 16, Issue 2, 2026 · pp. 27–37 Read article
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Depression Detection using Machine Learning: A Comprehensive Review
Abstract: Depression is a leading mental health disorder worldwide, often underdiagnosed due to subjective assessment methods. The increasing availability of digital behavioral data and the advancement in machine learning (ML) have opened new avenues for automated depression detection. This review presents a comprehensive overview of recent developments in ML- based approaches for detecting depression. It explores data sources, feature extraction techniques, learning algorithms, evaluation methods, and highlights current challenges and future …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 Read article
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Diabetes Risk Prediction from Survey Data Using Machine Learning Algorithms
Abstract: Diabetes mellitus represents one of the most significant global health challenges, affecting millions worldwide and leading to severe complications if left undiagnosed or poorly managed. Early detection and risk assessment are crucial for preventing the progression of this chronic condition. This research presents a comprehensive machine learning approach for predicting diabetes risk using survey-based health parameters. The study implements and compares four prominent classification algorithms: Logistic Regression, K-Nearest Neighbors (KNN), …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 Read article
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Efficient Masked Face Recognition Methods using Deep Learning
Abstract: Covid-19 questioned not only people's health but alsoconventional scientific systems. Due to face masks that were made mandatory to wear, the existing cognitive systems failed to perform in real-time scenarios. The demand to develop face recognition systems that detect people even when they wore masks was naturallyhigh. Deep learning techniques help to solve this problem, working efficiently in detecting user face features and comparing them with a known image database. …
Published in Journal of Instrumentation Technology & Innovations · Vol. 13, Issue 2, 2023 · pp. 1–10 Read article