Utpal Barman

The agriculture sector has gone through vast technological improvement, leading to increased productivity and sustainability. This research introduces AgriBot, a context-aware virtual assistant (VA) that helps farmers in rice cultivation and identifies diseases while providing instant suggestions for subsequent task. Using the RASA framework, AgriBot has been designed to understand the farmer's queries. Dual intent and entity transformer (DIET) classifier has been used for entity classification, achieving training and testing accuracy of up to 98% and 97%, respectively. Additionally, the system incorporates machine learning (ML) models for rice disease detection, utilizing a dataset of 4,624 images covering three major rice diseases: bacterial blight, brown spot, and blast. Among the tested models—neural network (NN), random forest (RF), support vector machine (SVM) and naive Bayes (NB) achieved an accuracy of up to 99.9%, demonstrating excellent classification performance. Using text-based query handling with image-based disease identification and instant suggestion makes it a more robust support system for the farmers.

The increasing difficulty of handling industrial and agricultural wastes has generated interest in reusing materials such as Cement Kiln Dust (CKD) and Rice Husk Ash (RHA) for sustainable soil stabilization. This study examined the enhancement of lateritic soil with the incorporation of CKD (0–12%) and RHA (0–25%) by weight. An integrated experimental and Artificial Neural Network (ANN) methodology was utilized to evaluate and forecast geotechnical features. Laboratory assessments were conducted to measure Atterberg limits, Maximum Dry Density (MDD), Optimum Moisture Content (OMC), and Unconfined Compressive Strength (UCS) at 0, 7, and 28 days of curing. The results indicated significant enhancements in soil characteristics with CKD-RHA combinations. Artificial Neural Network models, including GELU, LOGSIG-3, and Leaky ReLU activation functions, accurately predicted the UCS, MDD, and OMC, achieving R² values as high as 0.980. This work underscores the efficacy of CKD-RHA mixtures in improving soil stability and the promise of ANN models as excellent prediction instruments, fostering sustainable and economical construction methodologies.

With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result of botnet attacks include distributed denial of service (DDoS) attacks, phishing attacks, and extortion attacks, generation of spam, and identity theft. The stealthy nature of botnets, characterized by constant alterations in network structures, attack methodologies, and data transmission patterns, poses a growing difficulty in their detection. This paper introduces an innovative strategy for mitigating botnet threats. Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. The results demonstrate that our model achieves 99.99% accuracy, 99.49% precision, 98.98% recall, and 99.23% F1-score, which outperform the state-of-the-art P2P detection approaches.

Background: Polycystic ovarian syndrome (PCOS) is a prevalent endocrine and metabolic condition seen in premenopausal women. A number of pharmacological agents like metformin, oral contraceptives, and anti-androgen agents have been used to treat PCOS and its associated implications, but it has been associated with numerous negative effects like fatal and nonfatal lactic acidosis, weight gain, cardiovascular, and hepatic toxicity. Methodology: This study considered the Preferred Reporting Items for Systematic Review and Meta- Analysis (PRISMA) guideline where a vast number of article sources from different scientific databases like Google Scholar (https://scholar.google.com/), PubMed (https://pubmed.ncbi.nlm.nih.gov/), and Web of Science (https://mjl.clarivate.com/search-results) were screened out. Results: From a total of 154 studies, 24 studies were screened out following the inclusion and exclusion criteria, which reported 24 different plants that were used against PCOS preclinically. Details of various plants used, including their respective families, plant parts utilized, extraction methods employed, formulations developed, animal models of polycystic ovary syndrome (PCOS) utilized, and the geographical locations within India where the research has been conducted are all documented. Conclusion: This systematic review is the first in India to report the pre-clinical studies of medicinal plants against PCOS, and it provides a thorough summary of the application of medicinal plants for the management of PCOS. Nonetheless, molecular level investigations on the plants utilized, their bioactive phytoconstituent responsible for the anti-PCOS activity, the probable mechanism of action and the pathway through which they conserve their effect, the effective dose, and the potential adverse effects are the need of time.

Alzheimer’s disease is a weakening neurodegenerative condition with profound cognitive implications, making early and accurate detection crucial for effective treatment. In recent years, machine learning, particularly deep learning, has shown significant promise in detecting mild cognitive impairment to Alzheimer’s disease conversion. This review synthesizes research on machine learning approaches for predicting conversion from mild cognitive impairment to Alzheimer’s disease dementia using magnetic resonance imaging, positron emission tomography, and other biomarkers. Various techniques used in literature such as machine learning, deep learning, and transfer learning were examined in this study. Additionally, data modalities and feature extraction methods analyzed by different researchers are discussed. This review provides a comprehensive overview of the current state of research in Alzheimer’s disease detection and highlights future research directions.

In this research, the primary objective is to tackle the pressing issue of identifying and effectively managing diseases in rice plants, a problem that can results in substantial crop losses and pose a severe threat to food security. The study employs Convolutional Neural Networks (CNNs), a type of deep learning model widely used for image analysis, to conduct an extensive investigation using a sizable dataset comprising 5,932 RGB images. These images represent four distinct disease classes in rice plants: Bacterial Leaf Blight (BLB), Blast, Brownspot, and Tungro. To conduct this research, the dataset is split into two subsets: a training set, which comprises 80% of the data, and a testing set, which makes up the remaining 20%. This division allows for a systematic evaluation of the performance of four different CNN architectures: VGGNet, ResNet, MobileNet, and a simpler CNN model. The results of this study consistently show that ResNet and MobileNet outperform the other CNN architectures in terms of their ability to accurately detect diseases in rice plants. These two models consistently achieve remarkable accuracy in identifying these diseases. The research findings not only emphasize the potential of deep learning techniques in addressing the critical issue of rice crop diseases but also highlights the significant role that ResNet and MobileNet play in strengthening crop protection efforts and contributing to global food security.

With the surge in population growth, the demand for electricity has escalated, necessitating efficient solutions to enhance the reliability and security of electrical systems. Smart grids, functioning as self-sufficient systems, offer a promising avenue by facilitating bi-directional communication between producers and consumers. Ensuring the stability and predictability of smart grid operations is paramount to evaluating their efficacy and usability. Machine learning emerges as a crucial tool for decision-making amidst fluctuating consumer demand and power supplies, thereby bolstering the stability and reliability of smart grids. This study explores the performance of various machine learning classifiers in predicting the stability of smart grid systems. Utilizing a smart grid dataset obtained from the University of California’s machine learning repository, classifiers such as logistic regression (LR), XGBoost, linear support vector machine (Linear SVM), and SVM with radial basis function (SVM-RBF) were evaluated. Evaluation metrics, including accuracy, precision, recall, and F1 score, were employed to assess classifier performance. The results demonstrate high accuracy across all models, with the Deep Neural Network (DNN) model achieving the highest accuracy of 99.5%. Additionally, LR, linear SVM, and SVM-RBF exhibited comparable accuracy levels of 98.9%, highlighting their efficacy in smart grid stability prediction. These findings underscore the utility of machine learning techniques in enhancing the reliability and efficiency of smart grid systems.