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Intelligent Agents System for Vegetable Plant Disease Detection Using MDTW-LSTM Model
When it comes to agricultural output, nation, India, ranks first in the world, and agriculture is unparalleled. The need to categorize and trade agricultural goods is paramount. Manual organization, which is tedious and laborious, is not a choice. When agricultural products are graded automatically, a lot of time is saved. The application of image processing techniques facilitates the examination and evaluation of the products. A technique for identifying diseased vegetables is the focus of this effort. Feature extraction, preprocessing, segmentation, and training the model are all heavily dependent on sequence. Among the preprocessing technologies at disposal are image segmentation and filtering. Using Kapur's thresholding based segmentation method, the image's sick areas can be located during the segmentation process. Use k-means clustering for feature extraction to identify vegetable plant diseases. The training of an MDTW-LSTM model relies heavily on feature selection. In terms of performance, the proposed method surpasses two cutting-edge algorithms: LSTM and DTW. The results showed an accuracy of 97.35 percent, indicating a remarkable improvement. 2024 IEEE. -
The red terror and a state of uncertainty: United Nations' role In the Indian maoist struggle
In this paper, the authors argue that the long drawn armed conflict between the Maoists and the Indian State has acquired the status of a non-international armed conflict due to the organized nature of the Maoists and the scale of violence arising out of the conflict. The systematic human rights abuses by both parties and forceful displacement of civilians is a tangible threat to international peace and security in the region. In light of the deadlock between the parties, the authors make a case for United Nations' intervention in mediating an end to the conflict and restoring peace and security in the region. Drawing inspiration from the role played by the UN in ending civil wars across the globe, this paper argues for a similar intervention in the non-international armed conflict in India. The authors argue that the UN should venture to exert pressure on the State to eliminate any further abuses of human rights, and remove the impasse between both the parties to facilitate a constructive dialogue. Copyright 2012 De Gruyter. All rights reserved. -
Policy Intervention Towards Ecological Balance Through Reduction of Carbon Footprint in India
India is experiencing unprecedented urbanisation and industrialisation with consequential carbon emission, which significantly increases the gap between international commitments and reality. This paper explores the recent policy initiatives aimed at achieving promised indicators and engage in recent developments on decarbonisation. The paper further identifies the gap between sustainable development and economic advancement. The paper reflects on the recent carbon-mitigating policies adopted by India. A doctrinal research strategy has been used to address this issue. International, foreign and domestic policies have been discussed to understand the impact of these policies on climate change crisis. Models from other nations were also evaluated, taking into consideration India's unique socio-economic context. This research aims to identify adaptable techniques to effectively manage emissions in critical sectors, including energy, transport and industry. The paper proposes a set of concrete policy interventions that can promote ecological balance while sustaining economic development. Once the regulatory bodies initiate appropriate implementation of these policies, the carbon emissions in India shall be more resilient and effective. 2026 International Union of Biochemistry and Molecular Biology, Inc. -
Mobilizing Automated Vehicles: Harmonizing the Intersection of Technological Innovations and Legal Regulations
There will be significant shifts in transportation with the introduction of autonomous vehicles (AVs), which will increase efficiency, safety, and environmental friendliness. But these technologies can only be used to their full potential if technological breakthroughs are seamlessly integrated with strong legal regulations. The article delves into the ways in which technological advances and legal frameworks meet, highlighting how important regulatory measures are for ensuring the secure use of AVs. In addition, the article delves into the current legal framework around AVs, drawing attention to the difficulties caused by inconsistent regulations and the necessity for flexible rules that can stay up with the fast-paced advancements in technology. The purpose of this article is to examine current policies and case studies to shed light on how to effectively integrate technology advancements with legal requirements to create conditions that are favorable to the broad use of autonomous vehicles. The results highlight the need for manufacturers, lawmakers, and the general public to work together for the sake of society's safety and well-being during the shift to autonomous transportation. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Sustainability and Urban Quality of Life: Research, Policy and Practice
This book conceptualizes and synthesizes worldwide research on the quality of urban life. It looks at quality of life within urban cities analysing amenities, infrastructure and assets while also bringing in the discourse around scarcity, disparity, accessibility, sustainability, equity, and well-being. Organized into four major parts, the book reflects on the interconnections between theories and practice and through a multi-disciplinary approach focuses on the aspects of urban environment and planning that makes cities inclusive, safe, resilient, smart, and sustainable. This book highlights the enormous strain on urban areas due to severe scarcity of civic systems and provides an in-depth look into urban concerns and pressing challenges from a global perspective, as well as many planning approaches to solving these problems. This book will be useful to students, researchers and teachers working in the field of urban studies, remote sensing and GIS, planning and sustainability, sustainable development, urban geography, development geography and population geography. This book would also be an invaluable companion to thought leaders, policy makers and industry and other professionals working in the field of urban planning and human development. 2025 selection and editorial matter, Uday Chatterjee, Avishek Bhunia, Jyothi Gupta and Krishnendu Gupta; individual chapters, the contributors. -
Optimizing Diabetes Prediction Models for Enhanced Health Data Processing
Diabetes prediction is crucial for early intervention and personalized treatment. This study uses a multimodal strategy, including prediction algorithms, downsampling, feature engineering, exploratory data analysis (EDA), cross-validation, and classification techniques. EDA is used to understand diabetes-specific features, while downsampling ensures fair representation of instances with and without diabetes. Classification algorithms categorize people into appropriate diabetes risk groups using machine learning. Cross-validation evaluates predictive models in various data scenarios. The study emphasizes the value of specialized methods and domain-specific expertise in diabetes prediction, emphasizing the need for accurate risk assessment in healthcare decision-making and the potential for proactive interventions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Harnessing Insights for Optimizing Healthcare: Disease Prediction and Beyond
This study offers a novel method for developing classification approaches for disease prediction. Exploratory analysis and meticulous data preprocessing were conducted to understand the relationships between symptoms and illnesses. The research involved assessing various machine learning models, including the random forest classifier, through cross-validation techniques, resulting in the identification of a high-performing model with an impressive accuracy rate. In addition, this study incorporates data visualization techniques to gain insights into symptomdisease connections. The studys focus on data visualization and optimization strategies in health demonstrates the potential to transform healthcare by providing precise diagnoses and predicting diseases, ultimately improving patient outcomes. This research underscores the efficacy of data-driven techniques and their integration into recommendation and disease prediction systems, emphasizing the significance of data visualization and optimization strategies in health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Comparative Analysis of Predictive Models to Detect Alzheimers Disease
Alzheimers disease is the most common type of dementia, often affecting people above the age of 60, as all the brain connections and cells themselves start to die, affecting motor, speech and memory, slowing eating away a person once it sets out as it is a non-curable disease as of now. But an early and easy diagnosis may help slow down the process and start treatment, so it is essential to diagnose it quickly. But this disease needs a number of tests and time to determine the diagnosis, and time is of the essence. Various Machine Learning (ML) algorithms are being applied nowadays, with newer methods being trialed every day for the detection of Alzheimers more consistently and easily, but it is essential to apply the most accurate of models and require only the optimum number, and cost efficient tests for reliable diagnosis so this horrid disease could be started the treatment for as soon as possible. This paper is presenting its arguments for various methods of prediction of Alzheimers to improve efficiency of detection, a comparison of models taking into consideration the costs, the accuracy and the true benefit of the test for early tackling of this illness. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Effect of Museum Visit Intervention on Learning and Attitude Towards History
Place-Based Education is an education where learning happens in a place. The place could be museum, garden, palace, library etc. Place-based education is useful for the discipline of History as places are the existing evidence of past historical events. Therefore, students in History discipline can use place-based education for effective learning of history. The study attempts to find out the effect of museum visit intervention on learning and attitude towards newlinehistory. The study brings out how Museums act as an active agency to learn history. The study employed Quasi-experimental model with pre-test, post-test and follow-up post-test. Through purposive random sampling method, the participants were selected from 6th grade students of secondary schools of ICSE board located in the urban area of Kolkata in India. 120 students are included in the study group of the research (control group=60, experimental group=60). For the purpose of measuring museum visit intervention on learning and attitude towards history, researcher made achievement test and attitude scale with 5 point Likert scale was used. newlineThe instruction was provided in accordance with the History Course Curriculum of ICSE newlineboard. Experimental group visited the museum whilst the control group did not visit the newlinemuseum. Data analysis was conducted through SPSS program version 29. The result of the newlinestudy revealed that students of the experimental groups have performed better in comparison to the students of the control groups in learning and attitude towards history. Therefore, there is a recommendation to include museum visits pedagogies in the scope of social studies in History discipline. -
Advanced Botnet Detection Using Hybrid Machine Learning Models
The improvement of computer network systems, cyberattacks that take advantage of system flaws have increased, resulting in significant monetary losses, business interruptions, harm to one's reputation, and legal repercussions. This research examines nine attack types, those are Fuzzers, Shellcode, Generic, Worms, Analysis, Normal, DoS, Exploits, Backdoor, and Reconnaissance. Botnet assaults are attacks in which a single operator controls several networked devices. The research study examines several models, such as Random Forest, XGBoost Classifier, Logistic Regression, and Decision Tree, to improve detecting skills. By utilizing the advantages of both methods, the suggested ERFwXGBoost (Enhanced Random Forest with XGBoost) model, which blends Random Forest and XGBoost, exhibits remarkable performance. Notably, first they analyze the accuracy, then precision value is also measured, third will measure recall value, and then finally F1 score of the ERFwXGBoost model are all impressively 0.98. In addition to outperforming individual models, our hybrid technique offers a reliable and effective way to detect different kinds of botnet attacks. The research emphasizes how well these models work together to boost overall system security against advanced cyber threats and greatly increase detection accuracy. 2025 IEEE. -
Nutrition Analysis: A Data-Driven Approach for Optimizing Individual Dietary Choices
Maintaining good health, avoiding illnesses, and controlling ailments like diabetes, heart disease, and obesity all depend on proper diet. With the use of nutrition analysis, people can better understand their dietary requirements and choose foods that will support a healthy lifestyle. The goal of this studys data-driven approach to nutrition analysis is to maximize each persons dietary selections. Individualized recommendations are made for balanced nutrition by utilizing top-of-the-machine learning techniques to examine food patterns, nutrient consumption, and health effects. Food items are categorized based on their nutritional characteristics, and potential health effects are predicted using algorithms like Gradient Boosting, Multi-Layer Perceptron (MLP), Random Forest, Support Vector Classifier (SVC), Gaussian Nae Bayes (GNB), Decision Tree, Stochastic Gradient Descent (SGD), Linear Discriminant Analysis (LDA), and K-Nearest Neighbors (KNN). These models evaluated the relationship between dietary practices and nutritional needs. The final outcome is a comprehensive system that enables people to make knowledgeable food choices and optimize their nutrition in a way that promotes their overall health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Unleashing human potential: Integrating cognitive behavioral neuroscience into HR strategies
The world of work is transforming, driven by insights from the frontiers of science. Human resource (HR) practices are no longer limited to traditional methods and increasingly incorporate knowledge from disciplines like Cognitive Behavioral Neuroscience (CBN). By understanding how our brains work, we can design HR practices that enhance employee well-being, engagement, and, ultimately, performance. Drawing from neuroscientific research on decision-making, communication, stress, learning, motivation, and workplace design, this chapter delves into the intersection of CBN and HR, offering evidence-based practices that support a thriving workforce. This interdisciplinary approach holds promise for maximizing human potential in the context of the modern workplace. 2024 by IGI Global. All rights reserved. -
Enhancement of Image Classification Using Transfer Learning and GAN-Based Synthetic Data Augmentation
Plastic bottle recycling has a crucial role in environmental degradation and protection. Position and background should be the same to classify plastic bottles on a conveyor belt. The manual detection of plastic bottles is time consuming and leads to human error. Hence, the automatic classification of plastic bottles using deep learning techniques can assist with the more accurate results and reduce cost. To achieve a considerably good result using the DL model, we need a large volume of data to train. We propose a GAN-based model to generate synthetic images similar to the original. To improve the image synthesis quality with less training time and decrease the chances of mode collapse, we propose a modified lightweight-GAN model, which consists of a generator and a discriminator with an auto-encoding feature to capture essential parts of the input image and to encourage the generator to produce a wide range of real data. Then a newly designed weighted average ensemble model based on two pre-trained models, inceptionV3 and xception, to classify transparent plastic bottles obtains an improved classification accuracy of 99.06%. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
An AI Approach to Pose-based Sports Activity Classification
Artificial intelligence systems have permeated into all spheres of our life-impacting everything from our food habits to our sleep patterns. One untouched area where such intelligent systems are still in their infancy is sports. There has not been enough indulgence of AI techniques in sports, and most of the works are carried on manually by coaching staff and human appointees. We believe that intelligent systems can make coaching staff's work easier and produce findings that the human eye can often overlook. Here, we have proposed an intelligent system to analyze the beautiful game of tennis. With the use of computer vision architecture Detectron2 and activity-based pose estimation and subsequent classification, it can identify an action from a tennis shot (activity). It can produce a performance score for the player based on pose and movement like forehand and backhand. It can also be used to understand and evaluate the strengths and weaknesses of the player. The proposed approach provides a piece of valuable information for a player's performance and activity detection to be used for better coaching. The study achieves a classification accuracy of 98.60% and outperforms other SOTA CNN models. 2021 IEEE -
Classification of Soil Images using Convolution Neural Networks
Classification of soil is crucial for the agricultural domain as it is an essential task in geology and engineering domains. Various procedures are proposed to classify soil types in the literature, but many of them consumed much time or required specially designed equipments/applications. Classification of soil involves the accounting of various factors due to its diversified nature. It can be observed that several critical domain-oriented decisions often depend on the type of soil like farmers might be benefitted from knowing the kind of soil to choose crops accordingly for cultivation. We have employed different Convolution Neural Network (CNN) architectures to identify the soil type accurately in real-time. This paper describes the comparative evaluation in terms of performances of various CNN architectures, namely, ResNet50, VGG19, MobileNetV2, VGG16, NASNetMobile, and InceptionV3. These CNN models are used to classify four types of soils: Clay, Black, Alluvial, and Red. The performance of the ResNet50 model is the best with a training accuracy and training loss of 99.47% and 0.0252, respectively compared to other competing models considered in this paper. 2021 IEEE. -
Machine Learning-based Currency Information Retrieval for Aiding the Visually Impaired People
Paper currency is one of the most in-demand and long-established payment modes across the globe. People suffering from visual disabilities often face difficulties while handling paper currencies. Over the years, assisting technology has been rekindling itself to serve the aged and disabled person more aptly. Image processing methods and other sophisticated technologies, like Artificial Intelligence, Deep Learning, etc., can be employed to identify banknotes and fetch other valuable pieces of information from them. This paper proposes a framework that focuses on an integrated approach to retrieving data from the paper currency's uploaded image. The current version of the framework focuses on identifying the authenticity of the paper currency and classifying it according to its value. This work is an initiative to help visually impaired people to use paper currencies without assistance from other individuals and support them in living independently. 2021 IEEE. -
LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population
Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools. 2024 The Authors -
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The Impact and Inheritance of Operating Leverage: A Study with Two Pharmaceutical Companies
International Journal of Management Research and Technology, Vol-7 (2), pp. 145-154. ISSN-0974-3502