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Research ArticleOpen accessIn the keywordsLinear Smoothing of Noisy Spatial Temporal SeriesValter Di Giacinto, Ian dryden, Luigi ippoliti, Luca RomagnoliThe main objective of the study is the development of a linear filter to extract the signal from a spatio-temporal series affected by measurement error. We assume that the evolution of the unobservable signal can be modelled by a space time autoregressive process. In its vectorial form, the model admits a state space representation allowing the direct application of the … …Journal of Mathematics and Statistics, Vol. 1, Issue 4 · 31 Dec 2005 · 6,311 accessesView PDFCite
Research ArticleOpen accessIn the abstractDevelopment of a Machine Learning Based Mobile and Web Crop Recommendation System for Precision Farming in WardhaAishwarya Kadu, KTV ReddyAgricultural productivity is crucial for economic growth of a nation, especially for countries like India which is highly dependent on agriculture. This study addresses the need for cutting-edge technology to enhance crop productivity by proposing a Mobile/Web-based precision farming system. Leveraging Machine Learning (ML) algorithms, the proposed system recommends optimal crops based on several factors such as environmental parameters (Nitrogen … …Journal of Computer Science, Vol. 22, Issue 3 · 20 Mar 2026 · 1,660 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest-Based Genome Analysis for Disease Association and SNP Marker IdentificationXiangdong LiuAt present, genomic data analysis has the problems of insufficient model interpretation and limited computational efficiency in disease association research. As a powerful machine learning algorithm, Random Forest has demonstrated excellent ability in processing complex and multi-dimensional data sets and has gradually become a popular tool in the field of bioinformatics. The purpose of this paper is to explore how … …American Journal of Biochemistry and Biotechnology, Vol. 21, Issue 2 · 25 Jul 2025 · 5,533 accessesView PDFCite
Research ArticleOpen accessIn the abstractLeveraging Modified Cascaded Fully Convolutional Neural Networks and Optimized Swin U-Net for Liver Lesion PredictionSugandha Chakraverti, Tejaswi Khanna, Vijay ShuklaLiver lesions are abnormal growths that can develop for various reasons, some of which are noncancerous (benign) while others are cancerous. Machine learning and, deep learning techniques are employed for liver health assessment and lesion detection, but the quality and, diversity of training data can hinder the performance, leading to overfitting or underfitting. These models often need extensive pre-processing and … …Journal of Computer Science, Vol. 22, Issue 10 · 8 Oct 2026 · 372 accessesView PDFCite
Research ArticleOpen accessIn the abstractDetermination of Crop Yield Detection and Prediction With Multimodal Data Using Fusion TechniquesKutlwano Sedimo, Rajalakshmi Selveraj, Venumadhav Kuthadi, Thulaganyo DimakatsoMost often, soil, environmental, and agronomic data are used in pieces, hence limiting the performance of crop selection and yield forecasting. This study develops an integrated, fusion-based framework that combines soil image classification, environmental feature analysis, and input-driven yield prediction to support precise agricultural decision-making. The system consists of three major modules: (i) a CNN for the classification of soil … …Journal of Computer Science, Vol. 22, Issue 10 · 5 Oct 2026 · 481 accessesView PDFCite
Research ArticleOpen accessIn the abstractCascade-Based Non-Linear Feedforward Neural Networks for Bi-Directional MemoryThipendra Pal Singh, Tanupriya Choudhury, Rohini AIn today's world, efficient computation is the key to success in many fields. Pattern association plays an influential role in many areas of life, such as learning and memory. In complex dynamics, bidirectional associative memory has been effectively demonstrated by neural networks. However, these neural networks face challenges in terms of performance, such as computational time. A random bipolar input … …Journal of Computer Science, Vol. 19, Issue 4 · 28 Feb 2023 · 5,640 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Deep Learning Framework for Classifying and Evaluating Yoga ExercisesMehar Latif Malik, Arun KhoslaYoga is a type of physical activity that combines physical postures with breathing techniques. Cardiovascular disease is the leading cause of illness and mortality. Yoga's potential involvement in cardiac recovery can be incorporated into cardiovascular rehabilitation. Classifying and evaluating yoga exercises using a deep learning framework plays a significant role in postoperative recovery. Home based rehabilitation often suffers from a … …Journal of Computer Science, Vol. 19, Issue 2 · 30 Jan 2023 · 6,091 accessesView PDFCite
Research ArticleOpen accessIn the abstractMachine Learning Oceanographic Data for Prediction of the Potential of Marine ResourcesDenny Arbahri, Oky Dwi Nurhayati, Imam MuditaMarine data and information are very important for human survival, therefore this data and information is attractive to investors because of the potential economic value. This data and information has been difficult to obtain, the solution to overcome this is by analyzing oceanographic data for 2009-2019 collected from the marine database belonging to the Agency for the Study and Application … …Journal of Computer Science, Vol. 20, Issue 2 · 8 Jan 2024 · 5,970 accessesView PDFCite
Research ArticleOpen accessIn the abstractLossy Asymptotic Equipartition Property for Networked Data StructuresKwabena Doku-AmponsahIn this study, we prove a Generalized Information Theory for Networked Data Structures modelled as random graphs. The main tools in this study remain large deviation principles for properly defined empirical measures on random graphs. To motivate the paper, we apply our main result to a concrete example from the field of Biology. …Journal of Mathematics and Statistics, Vol. 13, Issue 2 · 24 May 2017 · 6,979 accessesView PDFCite
Research ArticleOpen accessIn the abstractRandom Forest Classification and Support Vector Machine for Detecting Epilepsyusing Electroencephalograph RecordsHeri Kuswanto, Mutiah Salamah, Muhammad Idrus FachruddinComplexity in data structure has led to the rapid development of computational statistics methods. Machine learning approaches have been introduced and applied to solve complex problems in many fields. This paper applies two common machine learning approaches, Random Forest (RF) and Support Vector Machine (SVM), in the detection of epilepsy. The diagnosis of epilepsy can usually only be made when … …American Journal of Applied Sciences, Vol. 14, Issue 5 · 4 May 2017 · 8,134 accessesView PDFCite