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227 articles for “Vectors”
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A Review on Lung Cancer Prediction Using Machine Learning
Abstract: Lung cancer continues to be a major contributor to cancer-related mortality across the globe. Timely diagnosis and reliable prediction models play a crucial role in enhancing treatment outcomes and survival rates for patients. The present study focuses on the utilization of machine learning (ML) methods for the prediction of lung cancer. Using datasets that incorporate clinical records, imaging modalities, and genetic profiles, the research assesses the predictive capabilities of multiple …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 3, 2025 · pp. 1–11 Read article
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Integrating Biotechnology, Physiology, and Agroecological Practices for Sustainable Crop Production and Protection
Abstract: Global agriculture is currently confronting a wide range of complex challenges, including a rapidly growing population, climate change, increasing pest and disease pressures, soil degradation, water scarcity, and the urgent need for sustainable intensification of crop production. Addressing these issues requires integrated strategies that combine crop improvement (through modern breeding and biotechnology), precision agronomic practices related to soil, irrigation, and nutrition, as well as advancements in plant physiology, molecular biology, …
Published in International Journal of Trends in Horticulture · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Using Machine Learning to Guess Photochemical Reaction Pathways
Abstract: Photochemical reactions are crucial to many activities in the fields of energy conversion, environmental cleanup, and synthetic chemistry. However, predicting their causes and results effectively is still very hard since they entail excited electronic states, nonadiabatic transitions, and complicated potential energy surfaces. Machine learning (ML) has been a powerful technique to go along with classic quantum chemistry methods in the last few years. It offers better prediction capability and lower …
Published in International Journal of Photochemistry and Photochemical Research · Vol. 3, Issue 2, 2025 · pp. 01–12 Read article
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AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
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Optimized Hardware Realization of AES for High-Throughput FPGA Platforms
Abstract: The Advanced Encryption Standard (AES) is the predominant symmetric-key cryptographic algorithm used for securing digital communication across embedded systems, IoT devices, cloud infrastructures, and defense networks. Although software-based AES implementations offer flexibility, they often fail to meet the high-speed, low-latency, and energy-efficient requirements of modern real-time applications. Reconfigurable hardware platforms such as Field-Programmable Gate Arrays (FPGAs) provide a powerful alternative by enabling architectural customization, intrinsic parallelism, and optimized hardware acceleration. …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 2, 2025 · pp. 11–22 Read article
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ML Model Comparison for Sentiment Analysis Across Diverse Datasets
Abstract: Analyzing sentiment is crucial for understanding public opinion on various issues in marketing, politics, and social sciences. This study compares the performance of seven different machine learning algorithms for sentiment classification, focusing on their effectiveness, accuracy, and complexity. The research is conducted on a pre-processed dataset with balanced text samples, utilizing feature extraction methods such as Term Frequency-Inverse Document Frequency (TF-IDF). The performance assessment criteria consist of accuracy, precision, recall, …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 26–33 Read article
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Immunological Biomarkers and Epidemiological Evaluation of Lymphatic Filariasis Elimination Efforts in Osun State, Nigeria
Abstract: Lymphatic filariasis (LF) remains a neglected tropical disease affecting millions globally, despite extensive elimination efforts. While the interruption of transmission is a key goal, the long-term immunological effects on individuals with existing complications like elephantiasis remain poorly understood. This study assessed the current transmission status of LF and the haematological profiles of infected individuals across selected Local Government Areas (LGAs) in Osun State, Nigeria. A total of 7,388 individuals from …
Published in International Journal of Tropical Medicines · Vol. 3, Issue 1, 2026 · pp. 22–30 Read article
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AI-Based Machine Learning Web Application Firewall (ML-WAF)
Abstract: This research investigates the use of deep learning techniques for the real-time detection of malicious activities in web traffic and proposes an intelligent, AI-driven Web Application Firewall (WAF) designed to provide automated and adaptive security. The system analyzes diverse components of HTTP requests, including request methods, URLs, headers, cookies, and payload content, to accurately identify and classify malicious behavior. The proposed model targets a wide range of common and critical …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 Read article
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A Detailed Survey of Machine Learning Applications, Methods, and Future Prospects in Agriculture
Abstract: Agriculture is undergoing a digital transformation driven by machine learning (ML) and artificial intelligence. The integration of ML techniques with data from sensors, drones, satellites, and IoT devices has enabled precision agriculture, early disease detection, optimized resource use, and improved yield prediction. This paper presents a comprehensive review of machine learning applications in modern agriculture, covering key areas such as crop monitoring, soil analysis, irrigation scheduling, pest, and disease detection, …
Published in Research & Reviews : Journal of Agricultural Science and Technology · Vol. 15, Issue 1, 2026 · pp. 39–45 Read article
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Electromagnetic and Dielectric Performance of Polymer–Ceramic Composite Substrates for Fractal-Based IoT-Antenna Fabrication
Abstract: Polymer–ceramic composite substrates play a crucial role in determining the electromagnetic performance, mechanical stability, and thermal reliability of radio-frequency devices. In this work, a polymer-based composite substrate is systematically investigated for its suitability in compact IoT and RFID antenna applications. A fractal-structured antenna is employed as a functional test platform to evaluate the dielectric behavior, impedance characteristics, and radiation efficiency of the composite substrate. Novelty of the proposed reader antenna …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1518–1534 Read article
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Log Identification and Monitoring System Using Generative AI
Abstract: In contemporary software ecosystems, application and infrastructure logs play a vital role in ensuring system reliability, performance optimization, fault diagnosis, and security compliance. As applications become increasingly distributed and cloud native, the volume, velocity, and variety of generated log data have grown dramatically. This rapid expansion makes traditional manual log inspection inefficient, error-prone, and largely impractical. To address these challenges, this paper proposes an artificial intelligence (AI) driven log monitoring …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 08–16 Read article
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Vaccines Through Time: Conventional Foundations and Next-Gen Innovations–Part 1
Abstract: Vaccination serves as a critical pillar of global public health, markedly decreasing infection rates and associated mortality. Traditional vaccine platforms such as live attenuated, inactivated, toxoid, and subunit vaccines have demonstrated effectiveness against pathogens like Mycobacterium tuberculosis and Plasmodium spp., yet they are constrained by antigenic variability, limited immune persistence, and complex production processes. Breakthroughs in synthetic biology, structural antigen design, and computational vaccinology have driven the development of advanced …
Published in Research and Reviews: A Journal of Microbiology and Virology · Vol. 16, Issue 1, 2026 · pp. 53–70 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
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Demodex spp. (Acari: Demodicidae) Infestation in Humans: Diagnostic Clues and Therapeutic Approaches to Primary and Secondary Demodicosis.
Abstract: Demodicosis represents an inflammatory dermatosis and adnexal disorder arising from pathologic overgrowth of Demodex mites, primarily Demodex folliculorum and Demodex brevis, which are ubiquitous human ectoparasites residing in pilosebaceous units and eyelid margins. Once regarded as benign commensals, these mites are now recognized as primary drivers or key cofactors in diverse clinical phenotypes, including papulopustular eruptions, pityriasis folliculorum, rosacea-like disorders, blepharitis, meibomian gland dysfunction, and exacerbations of comorbid dermatoses such …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 12–20 Read article
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Seroprevalence and Immunity to Dengue Virus Among Women in Punjab: A Public Health Concern
Abstract: Dengue virus (DENV) infection continues to be a public health concern in Punjab, India, where women are at higher risk because of socioeconomic factors. This cross-sectional study sought to assess the DENV seroprevalence, their neutralizing antibody profiles, and the risk factors associated with DENV among a sample of 1,200 women (aged 18–50 years) in Punjab. Serum samples were processed by IgG/IgM ELISA, and plaque reduction neutralization tests (PRNT90) were done …
Published in Research and Reviews : A Journal of Immunology · Vol. 16, Issue 1, 2026 · pp. 6–10 Read article
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Aerodynamic Optimization of UAV Wings Using Machine Learning
Abstract: Unmanned Aerial Vehicles (UAVs) are increasingly deployed across defense, transportation, agriculture, and environmental monitoring, demanding improved aerodynamic efficiency to enhance endurance, stability, and payload capacity. Traditional aerodynamic optimization approaches, relying on computational fluid dynamics (CFD) simulations and wind tunnel experiments, are often time-consuming and computationally expensive. This study proposes a machine learning (ML)-driven framework for the aerodynamic optimization of UAV wing geometries, aiming to significantly reduce design cycles while improving …
Published in International Journal on Drones · Vol. 2, Issue 1, 2026 · pp. 1–7 Read article
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AI-Driven Prediction of Mechanical and Thermal Properties in Polymer-Based Functionally Graded Composites
Abstract: The proposed architecture of the current paper is an artificial intelligence (AI)-driven model of forecasting mechanical and thermal aspects of polymer-based functionally-graded composites (FGCs). Traditional micromechanical and finite element models, which are practical in homogeneous composites, might not be able to account in nonlinear interaction that is caused by compositional gradient. To overcome the challenge, machine learning (ML) models like artificial neural network (ANN), support vectors regression (SVR), and gradient-boosted …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 70–89 Read article
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An Overview on Microwave Remote Sensing for Earth Observation
Abstract: To understand the Earth from space, one must look beyond the visible eye. While optical satellites rely on the Sun’s reflection much like the human eye they are frequently blinded by the curtain of cloud cover, smoke, or the dark veil of night. To bypass these limitations, we turn to Microwave Satellite Sensing. By utilizing electromagnetic waves with wavelengths ranging from 1 millimeter to 1 meter, we can see through …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 15, Issue 1, 2026 · pp. 21–25 Read article
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AI-Based Cybersecurity Framework for Protecting Smart Surveillance Infrastructure in Mumbai
Abstract: Smart city infrastructures increasingly rely on interconnected surveillance systems to ensure safety, operational efficiency, and public trust. However, the rapid expansion of IoT-based monitoring technologies has introduced new cyber risks, especially in high-density metropolitan areas. This paper proposes an AI-driven cyber resilience framework targeting smart surveillance infrastructure as a critical smart-living domain, focusing on Mumbai as a case study. Using the CIC-IDS2017 dataset, a machine learning-based intrusion detection model is …
Published in Journal Of Network security · Vol. 14, Issue 1, 2026 Read article
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Predictive Analytics and Adaptive Learning: A Machine Learning Framework for Reducing Learning Gaps
Abstract: Most contemporary digital learning environments encounter persistent challenges when it comes to accurately identifying students who are at-risk of academic underperformance. These challenges often arise due to limited visibility in learners’ engagement levels and gaps in conceptual understanding, particularly during the early stages of a course. To address this issue, the present study proposes an early prediction framework that leverages comprehensive student-related data through the application of machine learning techniques. …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 4, Issue 1, 2026 · pp. 16–21 Read article