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310 articles for “early detection”
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Technical Review Of Manufacturing Defects In Cold Forged M6 Nuts
Abstract: This review delves into the intricacies of M6 cold forged nuts, a pivotal component in many assemblies. These nuts are essential for maintaining structural integrity, yet their production can often lead to defects that compromise their performance. Key issues include material flaws, such as impurities and inconsistencies, dimensional inaccuracies that can affect fit and function, and surface roughness that can impact the nut's strength and durability. Material flaws can arise …
Published in Journal of Materials & Metallurgical Engineering · Vol. 14, Issue 1, 2024 · pp. 01–06 Read article
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Glaucoma Detection Using CNN
Abstract: The word “glaucoma” refers to both the progressive loss of retinal cells within optic nerve, and the gradual loss of vision caused by optic neuropathy. A condition that affects eye vision is called glaucoma. This condition is thought to be permanent and causes visual impairment. There are no early warning signs of this glaucoma in them. The effect is so subtle that we could not even observe that your vision …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 2, Issue 1, 2024 · pp. 7–15 Read article
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Classifying Abnormalities in Heartbeat Sound
Abstract: Heartbeat sounds play a major role in the detection of various diseases such as heart disease, hyperthyroidism, and high blood pressure in their early stages. In the proposed method, various abnormal and healthy heartbeat audio signals are given as input and the features are extracted using MFCC (mel-frequency cepstral coefficients). Then, a deep learning approach is applied in which the MFCC audio signals are sent to the CNN (convolutional neural …
Published in Research & Reviews: A Journal of Embedded System & Applications · Vol. 12, Issue 1, 2024 · pp. 24–31 Read article
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Comparative Study of Machine Learning Algorithms for Detection of Breast Cancer
Abstract: Breast cancer continues to be the most commonly diagnosed cancer among women, with more than 2.3 million new cases diagnosed yearly worldwide. It is stated as the leading cause of cancer-related deaths. Therefore, this emphasizes the dire necessity for early diagnosis with a view to improving survival. Early diagnosis elevates the effectiveness of prediction and treatment. This research carries out a structured and analytical evaluation of various machine learning algorithms, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 113–129 Read article
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Multimodal Disease Detection Using Deep Learning
Abstract: Artificial Intelligence (AI) is playing an increasingly pivotal role in modern healthcare, particularly in improving the speed and accuracy of disease detection. With the evolution of Machine Learning (ML), Deep Learning (DL), and high-performance computing, AI-based solutions are now capable of processing extensive medical datasets, ranging from patient records to diagnostic images, with remarkable efficiency. These systems offer immense potential for early intervention, improved clinical decision-making, and alleviating pressure on …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 129–139 Read article
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Role of Therapeutic Index for Local Infections Score in Wound Assessment
Abstract: Local wound infections pose a significant challenge, often detected later, leading to complications like systemic infections. Global nomenclature lacks uniformity, resulting in varied treatments for similar diagnoses. Early intervention is crucial, advocating for local antimicrobial therapy with diverse active agents to avoid systemic antibiotics and mitigate bacterial resistance. The Therapeutic Index for Local Infections (TILI) score, innovated by the German society Initiative Chronis Che Wenden (ICW), emerges as a pivotal …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 13, Issue 1, 2024 · pp. 44–48 Read article
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Driver Anti-Sleep Alarming and Protection
Abstract: Road accidents due to driver drowsiness is one of the biggest problems worldwide, which kills thousands of people every year. Fatigue slows the reaction time, reduces the concentration, and, most importantly, impairs the judgment, thus making drowsy driving as dangerous as drunk driving. To reduce such accidents, various technologies have been employed to develop driver anti-sleep devices. These include sensor-based detection, camera-based eye monitoring, EEG analysis, and real-time alert systems. …
Published in International Journal of Electronics Automation · Vol. 4, Issue 1, 2026 Read article
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AI Powered Fault Detection in DC Motor using STM32
Abstract: This work presents the design and implementation of an embedded artificial intelligence system for real-time fault detection in a direct current (DC) motor using the STM32 Nucleo- F411RE microcontroller. The objective of the study is to develop a low-cost and efficient predictive maintenance solution capable of identifying abnormal motor behavior at an early stage. Vibration and temperature signals are acquired using an MPU6050 sensor and processed directly on the microcontroller …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 39–49 Read article
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X-Ray Insight: Deep Learning-Enhanced Detection and Grading of Knee Osteoarthritis
Abstract: Osteoarthritis (OA) is the most prevalent form of arthritis affecting the knee. It is a degenerative joint disease characterized by the gradual deterioration of cartilage, typically impacting individuals aged 50 and above, although it can also occur in younger people. The condition progresses slowly, with symptoms intensifying over time, leading to significant pain and discomfort. Early diagnosis and intervention can significantly alleviate pain and enhance the quality of life for …
Published in International Journal of Biomedical Innovations and Engineering · Vol. 2, Issue 2, 2024 · pp. 1–6 Read article
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Enhancing Crop Health: A Review of Image Processing Methods for Leaf Disease Identification
Abstract: This research presents an overview of different image processing techniques for the identification of leaf disease. Many algorithms can be used to identify and categorize leaf diseases in plants, and digital image processing provides a quick, dependable, and accurate method of disease detection. This paper presents various techniques used on multiple crops and the achieved accuracy for each model. Leaf disease detection is a critical task in agriculture to ensure …
Published in Current Trends in Signal Processing · Vol. 14, Issue 1, 2024 · pp. 10–14 Read article
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Harvestify: ML Based Tool for Home Gardening and Farming
Abstract: This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system's architecture. The Crop Disease Detection module analyses …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 18–28 Read article
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Pharmacovigilance: The Silent Guardian of Patient Safety
Abstract: Pharmacovigilance is critical in protecting the populace health by monitoring, detecting and preventing adverse drug reaction (ADR) post drug approval. Although there is technological advancement and national initiatives such as the Pharmacovigilance Programme of India (PvPI), there is still a great problem of underreporting. Many benefits of effective pharmacovigilance include identification of risks early, better prescriptions and patient confidence. There are historical examples of drugs being withdrawn due to thalidomide, …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 1, 2026 · pp. 01–05 Read article
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Quasar: Quantum-Accelerated Sustainable Anomaly Recognition in Climate Systems
Abstract: Accurate detection of climate anomalies is vital for disaster alleviation and policy making in a sustainable manner, but customary detection methods face the challenges of computational inefficiency and physical inconsistency. In this study, we propose a novel approach called Quantum-Optimized Fuzzy Physics-Informed Neural Networks (QFuzzy-PINNs), which integrates quantum computing, fuzzy logic, and physics-informed deep learning. As a first step, we employ quantum annealing for conventional optimization to adjust multiple Gaussian …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 18–27 Read article
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HPV and Cardiovascular Risk: Unpacking a Novel Link Between Viral Infection and Heart Disease
Abstract: Human papillomavirus (HPV) is best known for its oncogenic potential, yet a growing body of evidence suggests that persistent HPV infection may also contribute to cardiovascular disease (CVD). In 2025, pooled analyses and conference reports galvanized attention by estimating that HPV‐positive individuals have ~40% higher risk of CVD and approximately double the risk of coronary artery disease (CAD) compared with HPV‐negative peers, even after adjustment for traditional risk factors. These …
Published in International Journal of Virus Studies · Vol. 2, Issue 2, 2025 · pp. 1–5 Read article
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Computer Aided Diagnosis of Breast Cancer using Machine Learning Techniques
Abstract: Breast cancer is one of the significant health problems that lead to early mortality in women, especially those between 40 and 55 years of age all over the world. In recent years, the number of breast cancer cases among women has risen significantly, making early and accurate diagnosis more important than ever. Computer-aided diagnostic (CAD) tools have become valuable in supporting radiologists by enhancing the precision of breast cancer detection. …
Published in Nano Trends – A Journal of Nano Technology & Its Applications · Vol. 27, Issue 2, 2025 · pp. 1–11 Read article
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Nanotechnology in Internet of Things: A Powerful Partnership Shaping the Future
Abstract: IOT holds potential to improve lives and revolutionize industries. However, its widespread adoption depends on addressing the growing energy consumption concerns. Nanotechnology provides a pathway to create a more energy-efficient IoT ecosystem by enabling the development of self-powered devices, low-power electronics, high-performance batteries, and advanced sensors. IoT is changing our daily lives and work by linking countless devices and producing vast amounts of data.. However, this explosive growth comes with …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 2, 2025 Read article
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Revolutionizing Plant Disease Detection: A Comprehensive Review
Abstract: Rise in population demands more food production but the diseases in plants contribute to loss. The advancement in agricultural field has a remarkable effect in detecting plant diseases. These diseases will have a major impact on the quality of plant and yield and hence can destroy the entire plant if they are not controlled on time. To reduce disease-related losses, it is necessary to identify different types of diseases and …
Published in International Journal of Advance in Molecular Engineering · Vol. 1, Issue 2, 2023 · pp. 44–55 Read article
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IOT Based Effortless Gas Leak Detection and Reservations
Abstract: The paper proposes an IOT Based Effortless Gas Leak Detection System for LPG and CO, utilizing gas sensors and a microcontroller. Positioned strategically, the sensors monitor air quality, triggering alarms if LPG or CO levels surpass safety thresholds.We plans to use this product for determination of gas leakage of LPG from household appliances and Carbon Monoxide produced from household appliances,vehicles and air conditioners with the help of MQ series gas …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 11, Issue 1, 2024 · pp. 33–41 Read article
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A study in Leveraging Deep Learning and IoT Arrays for Dynamic, Hyper-Local Atmospheric Intelligence
Abstract: The critical demand for high-resolution, actionable atmospheric data is challenged by the high cost and sparse coverage of traditional regulatory monitoring stations. This paper explores the synergistic paradigm shift enabled by integrating low-cost, dense Internet of Things (IoT) sensor arrays with advanced Artificial Intelligence (AI) methodologies, specifically Deep Learning (DL) models. We address the primary limitations of low-cost sensors—inherent bias, sensitivity to environmental drift (temperature/humidity), and calibration inconsistency—by utilizing AI …
Published in International Journal of Atmosphere · Vol. 2, Issue 2, 2025 · pp. 50–62 Read article
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Intelligent Earth: AI As A Catalyst For Climate Action
Abstract: Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes …
Published in International Journal of Environmental Noise and Pollution Control · Vol. 4, Issue 1, 2026 · pp. 48–52 Read article