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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. -
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. -
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. -
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. -
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. -
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. -
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. -
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. -
Efficient Method for Tomato Leaf Disease Detection and Classification based on Hybrid Model of CNN and Extreme Learning Machine
Through India, most people make a living through agriculture or a related industry. Crops and other agricultural output suffer significant quality and quantity losses when plant diseases are present. The solution to preventing losses in the harvest and quantity of agricultural products is the detection of these illnesses. Improving classification accuracy while decreasing computational time is the primary focus of the suggested method for identifying leaf disease in tomato plant. Pests and illnesses wipe off thousands of tons of tomatoes in India's harvest every year. The agricultural industry is in danger from tomato leaf disease, which generates substantial losses for producers. Scientists and engineers can improve their models for detecting tomato leaf diseases if they have a better understanding of how algorithms learn to identify them. This proposed approaches a unique method for detecting diseases on tomato leaves using a five-step procedure that begins with image preprocessing and ends with feature extraction, feature selection, and model classification. Preprocessing is done to improve image quality. That improved K-Means picture segmentation technique proposes segmentation as a key intermediate step. The GLCM feature extraction approach is then used to extract relevant features from the segmented image. Relief feature selection is used to get rid of the categorization results. finally, classification techniques such as CNN and ELM are used to categorize infected leaves. The proposed approach to outperforms other two models such as CNN and ELM. 2023 IEEE. -
Impact of digital payment trends: Unraveling consumer behavior patterns (preand post-COVID-19)
The COVID- 19 pandemic has accelerated the adoption of digital payments worldwide, fundamentally altering consumer behavior patterns. This study investigates the impact of digital payment trends on consumer behavior, comparing pre-and post-pandemic scenarios. It delves into the factors which drove the shift towards digital payments, exploring the changing preferences and perceptions of consumers. The study examines the influence of demographics, socio-economic factors, and technological advancements on consumer adoption of digital payment methods. It also analyzes the effect of the pandemic on e-commerce, online shopping, and mobile payments, highlighting the emergence of new trends and preferences. The findings provide valuable insights into the evolving landscape of digital payments and consumer behavior, offering implications for businesses, policymakers, and researchers 2025 by IGI Global Scientific Publishing. -
Hybrid Subset Feature Selection and Importance Framework
Feature selection algorithms are used in high-dimensional data to remove noise, reduce model overfitting, training and inference time, and get the importance of features. Features subset selection is choosing the subset with the best performance. This research provides a Hybrid subset feature selection and importance (HSFSI) framework that provides a pipeline with customization for choosing feature selection algorithms. The authors propose a hybrid algorithm in the HSFSI framework to select the best possible subset using an efficient exhaustive search. The framework is tested using the Bombay stock exchange IT index's companies' data collected quarterly for 16 years consisting of 71 financial ratios. The experimental results demonstrate that models created using 12 features chosen by the proposed algorithm outperform models with all features with up to 6% accuracy. The importance-based ranks of all features are generated using the framework calculated using 13 implemented feature selection techniques. All selected feature subsets are cross-validated using prediction models such as support vector machine, logistic regression, KNeighbors classffier, random forest, and deep neural network. The HSFSI framework is available as an open-source Python software package named ''feature-selectionpy'' available at GitHub and Python package index. 2023 IEEE. -
Stock Market Prediction Techniques Using Artificial Intelligence: A Systematic Review
This paper systematically reviews the literature related to stock price prediction systems. The reviewers collected 6222 research works from 12 databases. The reviewers reviewed the full-text of 10 studies in preliminary search and 70 studies selected based on PRISMA. This paper uses the PRISMA-based Python framework systematic-reviewpy to conduct this systematic review and browser-automationpy to automate downloading of full-texts. The programming code with comprehensive documentation, citation data, input variables, and reviews spreadsheets is provided, making this review replicable, open-source, and free from human errors in selecting studies. The reviewed literature is categorized based on type of prediction systems to demonstrate the evolution of techniques and research gaps. The reviewed literature is 7 % statistical, 9% machine learning, 23% deep learning, 20% hybrid, 25% combination of machine learning and deep learning, and 14% studies explore multiple categories of techniques. This review provides detailed information on prediction techniques, competitor techniques, performance metrics, input variables, data timing, and research gap to enable researchers to create prediction systems per technique category. The review showed that stock trading data is most used and collected from Yahoo! Finance. Studies showed that sentiment data improved stock prediction, and most papers used tweets from Twitter. Most of the reviewed studies showed significant improvements in predictions to previous systems. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting Financial Market Volatility Using Regression and Machine Learning Techniques
In standard Simple Linear Regression (SLR), one of the major assumptions is that the error terms have constant variance (homoscedasticity). However, this assumption is frequently violated in many real-world datasets, resulting in inefficient estimates and reduced predictive accuracy. To overcome this shortcoming, we propose a hybrid modeling platform that combines SLR with statistical and machine learning methods. The approach starts with SLR to identify the main linear relationship. Whenever residual diagnostics report the presence of heteroskedasticity, an Autoregressive Conditional Heteroskedasticity (ARCH) model is used to estimate time-varying variance. Such estimated variances are utilized in a Weighted Generalized Least Squares (WGLS) model, which stabilizes the error structure. Finally, to capture any remaining nonlinear patterns, an Artificial Neural Network (ANN) is applied on the residuals of the WGLS model. By layering these techniques, the hybrid framework improves both stability and predictive power. Simulation studies and empirical tests on Apple Inc. stock data confirmed that the hybrid framework yields reduced MAE and RMSE values and greater explanatory strength than individual approaches. 2025 IEEE. -
Tuning the output of the higher plants Circadian Clock
The circadian clock is an ascribed regulator found in the cells of creatures, that keeps biological and behavioral processes in stnc with dailt environmental changes throughout the 24-hour ctcles. When the circadian clock in humans malfunctions or is misaligned with environmental signals, the timing of the sleep-wake ctcle is altered and several circadian rhtthm sleep disorders result. Due to the Earth's rotation on its axis, predictable environmental changes are anticipated bt complex processes. The combined term for these ststems is the circadian clock. The circadian rhtthm regulates photostnthesis and photoperiodism, making it the "primart controller of plant life." The circadian clock is made up of post-translational alterations to core oscillators, epigenetic tweaks to DNA and histones, and auto regulatort feedback loops in transcription. In addition, the circadian clock is cell-autonomous and regulates the circadian rhtthms of distinct organs. Biochemical elements such as photostnthetic products, mineral nutrients, calcium ions, and hormones are used bt the core oscillators to communicate with one another. Arabidopsis is utilized to identift clock-related genes that govern plant growth, germination, pollination, flowering, abiotic and biotic stress responses, and more. The biological ctcles of all species, notablt humans, are undoubtedlt impacted bt other elements, including high altitude and changing ecoststems, in addition to the ones alreadt stated. Although it hasn't tet published ant experimental or scientific evidence to support them, the implication that living things have lives does appear inescapable. Hence, the present studt elaborates on the higher plants related to the circadian clock. The Author(s). -
Influence of Coronavirus Disease 2019 on human biological timekeeping
To stay in sync with environmental cues, the body's metabolic activities must be rhythmic, and these rhythmic functions are known as circadian rhythms, which repeat every 24 h. People's sleep-wake and eating patterns were interrupted as a result of house confinement, making them more vulnerable to noncommunicable chronic diseases during the coronavirus disease 2019 (COVID-19) period. During the epidemic, there was a greater degree of misalignment with this synchronization. The effects of severe acute respiratory syndrome coronavirus 2 (COVID-19) on the human circadian clock are studied in depth. The literature review was conducted fully online, with the website utilized to collect all of the papers from PubMed, and duplicates were handled only in the first phase. Researchers found that individuals of all ages who are pushed to adjust their daily routines shift to the later chronotype, resulting in lifestyle modifications and an altered biological timing system that contributes to noncommunicable chronic illnesses. Chronic illnesses have bidirectional conductance, which means they can be caused by both environmental and self-modification in daily activity, as was the case during the COVID-19 outbreak, which forced people to stay at home. This review comes to the conclusion that fighting the pandemic may be best done by changing medications and focusing on immune health. 2023 Wolters Kluwer Medknow Publications. All rights reserved. -
Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities
The use of intelligence in manufacturing has emerged as a fascinating subject for academics and businesses everywhere. This book focuses on various manufacturing operations and services which are provided to customers to achieve greater manufacturing flexibility, as well as widespread customization and improved quality with the help of advanced and smart technologies. It describes cyber-physical systems and the whole product life cycle along with a variety of smart sensors, adaptive decision models, high-end materials, smart devices, and data analytics. Intelligent Manufacturing and Industry 4.0: Impact, Trends, and Opportunities focuses on Intelligent Manufacturing and the design of smart devices and products that meet the demand of Industry 4.0, manufacturing and cyber-physical systems, along with real-time data analytics for Intelligent Manufacturing. The usage of advanced smart and sensing technologies in Intelligent Manufacturing for healthcare solutions is discussed as well. Popular use cases and case studies related to Intelligent Manufacturing are addressed to provide a better understanding of this topic. This publication is ideally designed for use by technology development practitioners, academicians, data scientists, industry professionals, researchers, and students interested in uncovering the latest innovations in the field of Intelligent Manufacturing. Features: Presents cutting-edge manufacturing technologies and information to maximise product exchanges and production Discusses the improvement in service quality, product quality, and production effectiveness Conveys how a manufacturing companys competitiveness can increase if it can manage the turbulence and changes in the global market Presents how intelligence production is essential in Industry 4.0 and how Industry 4.0 offers greater manufacturing flexibility, as well as widespread customisation, improved quality, and increased productivity Covers the ways businesses handle the challenges of generating an increasing number of customised items with quick time to market and greater quality Includes popular use cases and case studies related to intelligent manufacturing to provide a better understanding of this discipline. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
An Assessment of Farmers' Perception and Adaptive Capacity for Climate Change
In the past decades, various regions in U.P. had experienced severe floods. The effects of climate change also affected agricultural production. This study investigated the farmers' perception of climate change and suggested strategies for mitigating its effects using a primary survey with the help of a pre-structured schedule. Change in rainfall pattern, problems in seed quality, the emergence of new pests and diseases, changes in the crop cycle were the few effects that farmers' perceived due to climate change. Even the most mitigation efforts by the farmers cannot prevent some of the impacts of climate change within the following decades. It makes adaptation a must-have for addressing these impacts. 2022, The Society of Economics and Development. All rights reserved. -
Studies on phase transitions and dielectric properties of biowaste synthesized porous carbon nanoparticlesferroelectric liquid crystal mixture
Ferroelectric liquid crystals(FLCs), an exciting class of liquid crystals(LCs), found potential applications in the display as well as non-display regimes due to their fast response, low driving voltage and nonvolatile memory. The amalgamation of nanoparticles into FLCs has opened up new avenues in the LCs research field by alterations/modification of the existing properties of LCs. In this work, porous carbon nanoparticles (PCNPs) were dispersed into FLC mixture (W206E) and investigated their doping effect on FLCs textural, phase transition temperatures and dielectric studies in planar-aligned cells. Dielectric spectroscopy was carried out in the frequency range of 20 Hz to 10 MHz to explore the frequency as well as the temperature dependent of FLC in the entire SmC* region. The transition temperature of FLC mixture is increased by 4 C in PCNPs doped FLC sample then undoped FLC sample. Nearly 8.42% increase in permittivity is observed. A Gold stone relaxation mode at ?627 Hz was observed at lower frequency. 2024 Taylor & Francis Group, LLC. -
Wave scattering and dynamic stress concentration in piezoelectric half-planes with semi-elliptical notches under SH-wave excitation
This study presents a comprehensive analytical framework for investigating the scattering and dynamic stress response of semi-elliptical notches in piezoelectric half-planes subjected to anti-plane shear (SH) waves. The primary objective is to unify the treatment of notches, cracks, and circular holes within a rigorous wavedefect interaction model while explicitly incorporating piezoelectric coupling and nanoscale surface/interface effects. The methodology employs the complex function method in conjunction with the Helmholtz equation and wavefield superposition theory, leading to an infinite system of equations that rigorously satisfies continuity and boundary conditions; a systematic truncation strategy is then applied to achieve convergent solutions. Results demonstrate that surface/interface effects significantly suppress the dynamic stress concentration factor, particularly under vertical SH-wave disturbance, while resonance peaks become sharper at low modulus ratios and higher piezoelectric constants such as PZT-5H and BaTiO3. Importantly, the formulation naturally recovers classical elasticity results in the absence of piezoelectric effects, providing strong theoretical consistency. Validation is achieved through analytical recovery of benchmark cases (semicircular notch and edge crack), graphical comparisons with established results, and rapid convergence of the truncated system, confirming both accuracy and robustness. The practical implications of these findings extend to structural health monitoring, non-destructive evaluation, and the optimal design of advanced piezoelectric composites, where accurate prediction of defect evolution and stress amplification is critical. While the present work is restricted to semi-elliptical notches under SH-wave excitation in half-plane geometries, the approach is readily extensible to more general defect shapes and mixed-mode disturbances. The novelty of this study lies in its ability to capture piezoelectric surface/interface effects within an exact analytical framework, providing predictive capability for defect-induced stress concentrations and offering a reliable basis for the design and reliability assessment of high-performance piezoelectric materials. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
Computing isogeny on Edwards curves for quantum safe cryptography
In recent years, cryptographic research has seen a surge of interest in post-quantum cryptography driven by the potential threat that quantum computers pose to traditional public-key cryptosystems. Isogeny-based cryptography is a promising method in post-quantum cryptography, relying on the computational challenge of calculating isogenies, which are specific mappings between elliptic curves. The efficiency of isogeny computations is vital for real-world cryptographic applications. However, computing isogenies, especially with large parameters, can be very resource intensive. To overcome this challenge, we purpose an efficient method for computing odd-degree isogenies on certain form of an elliptic curves by employing an auxiliary coordinate. Our work appears to bridge the gap in computational efficiency for odd-degree isogenies, especially in terms of reducing the complexity of the isogeny computations when compared to traditional affine and projective methods. The derived formula is more efficient than affine and projective cases. We also analyse the algebraic complexity of these calculations and compare them to alternative formulae. Additionally, we evaluate the runtimes for isogeny computation across different prime numbers and compare them with other elliptic curve model to check the performance. At last, we suggest potential avenues for future work. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.