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COSMOLOGICAL DIAGNOSTICS OF BIANCHI TYPE-II BARROW HOLOGRAPHIC DARK ENERGY UNIVERSE; [????????I??? ?I????????? ????????I???? ?????? ?????I? ?????I?? ?????? ???? ?I???I-II]
In this paper, we investigate a Bianchi type II anisotropic cosmological model in the framework of Barrow holographic dark energy, considering both the Hubble horizon and GrandaOliveros scale as infrared cutoffs. To obtain exact solutions of the Einstein field equations, we assume a suitable relation between the metric potentials. Using Hubble cosmic chronometer data, we constrain the model parameters and obtain the best-fit values b4= ?0.091+0.013 ?0.012and H0= 72.32.7 km s?1Mpc?1The H(z) fit shows excellent agreement with observational data and overlaps with ?CDM at low redshifts, with mild deviations at higher z. The physical behaviour of the model is examined through a detailed analysis of cosmological parameters. The deceleration parameter q(z) reveals a smooth transition from an early decelerating phase to the present accelerating epoch. The equation of state parameter ?deshows quintom-like dynamics, evolving across the cosmological constant boundary and entering the phantom regime, consistent with late-time acceleration. Stability is tested using the squared sound speed vs2, which remains positive in the recent Universe, ensuring classical stability. The ?de?dephase plane indicates that both models lie in the freezing region, corresponding to faster acceleration. The statefinder diagnostics (r,s) and (r,q) further confirm the transition from the standard cold dark matter dominated phase to a de Sitter-like attractor, with trajectories showing clear deviations from ACDM. U.Y.D. Prasanthi, D. Tejeswararao, Diddi Srinivasa Rao, Y. Aditya, D. Ram Babu, 2026. -
A Study on the Selection of Features, Classifiers, and Resampling in Plant Disease Detection from Leaf Images
Computer vision has become an integral part of modern agriculture. One of the key applications of computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study, we evaluate the discriminatory capability of selected texture features in identifying plant diseases from leaf images. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of five different plants. Through extensive experimentation, two classifiersRandom forest and XGBoost are chosen for the evaluation. The class imbalance problem is addressed with a simple resampling. Resampling considerably improves the prediction accuracy. With the raw input images, the best feature as well as classifier depends on the plant type and the quality of the input images. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Machine Learning Models for Apple Disease Detection With Texture Feature Fusion and Feature Selection
Computer vision has become an integral part of modern agriculture. One of the key applications of Computer vision is the automatic detection and classification of plant disease from digital images of plant leaves. In this study we evaluate the discriminatory capability of selected texture features and their fusion in identifying plant diseases from leaf images. Further, the performance of four feature selection algorithms is also evaluated. Texture features are extracted from resized raw images. Experiments are carried out with public data sets of Apple plants. Through extensive experimentation, two classifiers - Random forest and XGBoost are chosen for the evaluation. The feature fusion and feature selection resulted in 85% accuracy. The result is promising as the features are extracted from whole leaf images, without any segmentation. 2025 IEEE. -
Revolutionizing Biodegradable and Sustainable Materials: Exploring the Synergy of Polylactic Acid Blends with Sea Shells
This study explores the mechanical properties of a novel composite material, blending polylactic acid (PLA) with sea shells, through a comprehensive tensile test analysis. The tensile test results offer valuable insights into the materials behavior under axial loading, shedding light on its strength, stiffness, and deformation characteristics. The results suggest that the incorporation of sea shells decrease the tensile strength of 14.55% and increase the modulus of 27.44% for 15 wt% SSP (sea shell powder) into PLA, emphasizing the reinforcing potential of the mineral-rich sea shell particles. However, a potential trade-off between decreased strength and reduced ductility is noted, highlighting the need for a delicate balance in material composition. The study underscores the importance of uniform sea shell particle distribution within the PLA matrix for consistent mechanical performance. These results offer a basis for additional PLA-sea shell blend optimization, directing future efforts to balance strength, flexibility, and other critical attributes for a range of applications, including biomedical devices and sustainable packaging. This investigation opens the door to more sustainable and mechanically strong materials in the field of additive manufacturing by demonstrating the positive synergy between nature-inspired materials and cutting-edge testing techniques. 2024 The Authors. -
Enhanced technique for detection and prevention of phishing on websites
Phishing is a kind of assault where cyber criminals trap individuals to gain access to someone's private data like credit card details, passwords, account details, etc. The false e-mails look shockingly genuine and even the Web pages where clients are requested to enter their data may look legitimate. Forgery of a website is a sort of online assault where the phishing person builds a duplicate of a true authorized site, with the objective of misguiding a client by fishing out data that could be utilized to dupe or instigate different assaults upon the victim. In this paper, a new technique is developed using the combination of CORS, Public Repository technique and Heuristic functions. This technique allows only authorized Domain to replicate the original website. Copyright 2019 American Scientific Publishers All rights reserved. -
Understanding the link between neural activity and immune function: Insights from neuropsychology and psychoneuroimmunology
This chapter investigates the intricate connections between neural activity and immune function, drawing on insights from both neuropsychology and psychoneuroimmunology. A better understanding of the role of the brain and mental health in regulating immunological responses and general wellbeing is the overarching goal. In order to determine the role of neuronal circuits and neurotransmitter systems in controlling emotional reactions and stress, we begin by going over basic ideas in neuropsychology. This chapter delves into the ways in which these brain systems influence immune function, incorporating insights from psychoneuroimmunology. It examines the mediating functions of neurotransmitters and hormones in the immune system's response to stress, among other topics. The consequences of mental health issues on immunological function, including persistent stress, anxiety, and depression, are highlighted in particular. We highlight the two way nature of the connections by discussing how changes in immune function might impact brain activity and mental health. 2025 by IGI Global Scientific Publishing. All rights reserved. -
ADVANCING THE FRONTIERS OF NEUROCOGNITIVE REHABILITATION: Research and Practice Ahead
This chapter explores the expanding horizons of neurocognitive rehabilitation by synthesizing emerging trends in research, technology, and practice. With a focus on translational innovation, it identifies how cutting-edge advancements, such as neurofeedback training (NFT), braincomputer interfaces, and artificial intelligence (AI)-driven diagnostics, are reshaping cognitive recovery pathways. Emphasis is placed on the growing need for culturally and contextually responsive models, particularly in low-resource settings, as well as scalable, tech-enabled delivery methods that enhance accessibility and personalization. The chapter also highlights the critical importance of long-term outcome studies, interdisciplinary collaboration, and workforce upskilling to support sustainable integration of novel tools into routine care. Ethical considerations, including data privacy, informed consent in neurotechnological interventions, and the equitable distribution of emerging therapies, are also explored. As the field advances, the convergence of neuroscience, digital innovation, and individualized care promises to transform neurocognitive rehabilitation from reactive to anticipatory, from standardized to precision-based. Ultimately, this chapter advocates for a global, equity-focused, and evidence-based framework that places individuals, not just their impairments, at the center of rehabilitation science and practice. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
A literature review on friction stir welding of dissimilar materials
Friction stir welding (FSW) employs a tool that does not require any filler materials; frictional heat is produced and performs a solid-state joining method. Severe plastic deformation causes to join similar and dissimilar materials without melting the workpiece at the welding line. Friction stir welding is the most recent friction welded joining processes with the most surprising features when welding various metal alloys, including magnesium, aluminium, copper, and steel. FSW is victorious of all the other conventional welding methods implied in many industrial applications like automobile, aerospace, fabrication, shipping, marines and robotics. It gives high-quality welds, energy input, and distortion are lower, better retention of mechanical properties; it is eco-friendly and can be performed less operating cost. This research work aims at the FSW process in Al-Cu alloys, highlighting:(a) Optimizing the welding process parameters, welding feed rate, tool rotation speed, (b) Evaluation of Electrical Conductance properties of joints, (c) Mechanical properties and metallography characteristics of joints. 2021 Elsevier Ltd. All rights reserved. -
Experimental analysis and RSM-based optimization of friction stir welding joints made of the alloys AA6101 and C11000
In the present study, the evaluation of FSW input parameters on output response ultimate tensile strength (UTS) of the friction stir welded AA6101-C11000 joint is in agreement. The response surface methodology (RSM) was adapted for generating the mathematical regression equation to predict the UTS and to develop the FSW parameters to attain the highest UTS of the FSW joints. The central composite design (CCD) method from RSM with five levels and three factors, i.e., tool rotational speed, feed rate, and tool offset used to conduct and minimize the number of tests. During FSW, base sheet cu (hard metal) was stationed on the advancing side (+1 mm, +1.68 mm tool offset) and the base sheet Al (soft metal) on the retreating side (?1 mm, ?1.68 mm tool offset). The radiography studies were accomplished to inspect the internal flaws of the FSW joints (Al-Cu).The XRD and SEM investigation of the ruptured specimens during the tensile test to evaluate the IMCs phase anatomy and fracture analysis. The maximum UTS value measured during the experimental work was 142.69 MPa at 1000 rpm, 40 mm min?1, and ?1.68 mm tool offset. The highest joint efficiency obtained was 82% compared with the AA6101 UTS value. RSM adapted for this work was 92% accurate and satisfactory. 2023 The Author(s). Published by IOP Publishing Ltd. -
Design and Optimization of Friction Stir Welding of Al-Cu BUTT Joint Configuration using Taguchi Method
Friction stir welding (FSW) is a solid-state welding technique in which the joint quality was predominantly subjected to heat formation throughout the metal welding process. The weld joint produced from FSW was better than the other fusion welding process. In this research, the base plates AA6101 and C11000 of 5 mm thickness were joined using the hardened oil-hardened non-shrinkable steel(OHNS) tool by the FSW method. The design of experiment (DOE) was used to optimize the input parameters such as tool rotational speed (rpm), feed rate (mm/min), and tool pin offset (mm) on output parameter ultimate tensile strength (UTS). The design of experiment (DOE) was carried out by employing a Taguchi L9 orthogonal array, three factors, and three levels for obtaining a quality joint with good strength. The results of nine trial runs from the Taguchi experimental approach were formulated and analyzed using the statistical tool analysis of variance (ANOVA) using MINITAB 19 software. ANOVA analysis was employed to find the contribution of the input parameters toward the output. The optimized input process parameters will help to create effective weld joints. This study revealed that tool pin offset towards softer metal at medium tool rotational speed would create joints with the highest UTS. Scanning Electron Microscope (SEM) was applied to investigate the structural changes in the FSW of Al-Cu joints. 2022, Books and Journals Private Ltd.. All rights reserved. -
Geo-spatial crime density attribution using optimized machine learning algorithms
Law enforcement agencies use various crime analysis tools. A large amount of crime data has enabled crime analysis. In this paper, the proposed research methodology uses Kernel Density Estimation (KDE) in a Geographical Information System (GIS) to analyze crime-type data. Bangalore and India newsfeeds are considered for experimental purposes. The paper introduces an optimized KDE machine learning algorithm that detects hotspots, estimates a locations crime rate, and identifies point pattern lows and highs. As a result of the experiment, the proposed methodology identified that the bandwidth of the Geographical information system influences the visualization of crime density. The paper also aids in visually determining the appropriate bandwidth for the problem using an optimized KDE algorithm. We had identified a significant correlation between Newsfeed data and Official Government data, both overall Crime and by crime type. The proposed KDE model achieved a predictive performance of 77.49%. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Geospatial crime analysis and forecasting with machine learning techniques
People use social media to engage, connect, and exchange ideas, for professional interests, and for sharing images, videos, and other contents. According to the investigation, social media allows researchers to examine individual behavior features and geographic and temporal interactions. According to studies, criminology has become a prominent subject of study globally, using data gathered from online social media sites such as Facebook, News feed articles, Twitter, and other sources. It is possible to obtain useful information for the analysis of criminal activity by using spatiotemporal linkages in user-generated content. The study refers to the application of text-based data science by gathering data from several news sources and visualizing it. This research is motivated by the abovementioned work from various social media crimes and government crime statistics. This chapter looks at 68 various crime keywords to help you figure out what kind of crime you are dealing with concerning geographical and temporal data. For categorizing crime into subgroups of categories with geographical and time aspects using news feeds, the Naive Bayes classification algorithm is used. For retrieving keywords from news feeds, the Mallet package is used. The hotspots in crime hotspots are identified using the K-means method. The KDE approach is utilized to address crime density and this methodology has solved the difficulties that the current KDE algorithm has. The study results demonstrated equivalence between the suggested crimes forecasting model as well as the ARIMA model. 2022 Elsevier Inc. All rights reserved. -
Crime analysis and forecasting on spatio temporal news feed dataan indian context
Social media is a platform where people communicate, interact, share ideas, interest in careers, photos, videos, etc. The study says that social media provides an opportunity to observe human behavioral traits, spatial and temporal relationships. Based on study Crime analysis using social media data such as Facebook, Newsfeed articles, Twitter, etc. is becoming one of the emerging areas of research across the world. Using spatial and temporal relationships of social media data, it is possible to extract useful data to analyse criminal activities. The research focuses on implementing textual data analytics by collecting the data from different news feeds and provides visualization. This researchs motivation was identified based on relevant work from different social media crime and Indian government crime statistics. This article focuses on 68 types of different crime keywords for identifying the type of crime. Nae Bayes classification algorithm is used to classify the crime into subcategories of classes with geographical factors, and temporal factors from RSS feeds. Mallet package is used for extracting the keywords from the news-feeds. K-means algorithm is used to identify the hotspots in the crime locations. KDE algorithm is used to identify the density of crime, and also our approach has overcome the challenges in the existing KDE algorithm. The outcome of research validated the proposed crime prediction model with that of the ARIMA model and found equivalent prediction performance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. -
AI-Based Yolo V4 Intelligent Traffic Light Control System
With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times. 2022 Boppuru Rudra Prathap et al., published by Sciendo. -
Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness
The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car accidents. Driver drowsiness and weariness are major contributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022 Boppuru Rudra Prathap et al., published by Sciendo. -
Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness
The number of automobiles on the road grows in lock-step with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car ac-cidents. Driver drowsiness and weariness are major con-tributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts. 2022, Industrial Research Institute for Automation and Measurements. All rights reserved. -
A pragmatic study on heuristic algorithms for prediction and analysis of crime using social media data
Advancement in technology and Social media has grown to become one amongst the foremost powerful communication channels in human history and this is where individuals are sharing their perspectives, thoughts, suppositions, and feelings. Law enforcement units are having hard time fighting crime with evergrowing population, regional issues and political con-sequences. The adoption of social media data for crime analysis is increasing day by day. Crime analysis can help use the resources wisely. A crime prediction alerts the department at the right time to focus their staff with better equipment in suspected areas. Crime analysis prevents threats to life and money loss in terms of damage. In recent days, the collection of crime data from different heterogeneous sources becomes a primary step for the crime analysis and prediction. In this paper Overview of Heuristic Based Crime Prediction and Analysis algorithms identified by different authors. Also, various sources of social media used for analysis and prediction are also reviewed in detail. This information can be considered for one of the prominent asset for crime investigation through social media data procedure and also, we had identified the different algorithms and research gaps of that algorithms with related to crime analysis and prediction. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Geospatial crime analysis to determine crime density using kernel density estimation for the indian context
Crime is the most common social problem faced in a developing country. Crime affects the reputation of a nation and the quality of life of its citizens. Crime also affects the economy of the country, increasing the financial burden of the government due to the need for expenditure in the police force and judicial system. Various initiatives are taken by law enforcement to reduce the crime rate. One such initiative, real-time accurate crime predictions can help reduce the occurrence of crime. In this paper, a crime analytics platform is developed, which processes newsfeed data analysis for different types of crimes and identify crime hotspots using Kernel Density Estimation method. This system enables criminologists to understand the hidden relationships between crime and geographical locations. Interactive visualization features are available that enable law enforcement agencies to predict crime. 2020 American Scientific Publishers. -
Twitter sentiment for analysing different types of crimes
Online social media like a twitter play a vital role as it helps to track the Spatialoral on social media data with respect crime rate. With the very fast evolving of users in social media, sentimental analysis has become an excellent source of information in decision making. Twitter is one of the most popular social networking site for communication and a primary source of information. More than 150 million users publish above 500 million 140 character TWEETS each day. Tweets have become a basis for product recommendation using sentimental analysis. This paper explains the approach for analyzing the sentiments of the users about a particular crime event tweets posted by the active users. The results so obtained will let you know about the change in the public opinion about the crime events whether it's positive or negative and to find out emotions on different types of crimes. 2018 IEEE. -
Polarity detection on real-time news data using opinion mining
Sentimental Analysis or Opinion Mining plays a vital role in the experimentation field that determines the users opinions, emotions and sentiments concealing a text. News on the Internet is becoming vast, and it is drawing attention and has reached the point of adequately affecting political and social realities. The popular way of checking online content, i.e. manual knowledge-based on the facts, is practically impossible because of the enormous amount of data that has now generated online. The issue can address by using Machine Learning Algorithms and Artificial Intelligence. One of the Machine Learning techniques used in this is Naive Bayes classifier. In this paper, the polarity of the news article determined whether the given news article is a positive, negative or neutral Naive Bayes Classifier, which works well with NLP (Natural Language problems) used for many purposes. It is a family of probabilistic algorithms that used to identify a word from a given text. In this, we calculate the probability of each word in a given text. Using Bayes theorem, they are getting the probabilities based on the given conditions. Topic Modeling is analytical modelling for finding the abstract of topics from a cluster of documents. Latent Dirichlet Allocation (LDA) is a topic model is used to classify the text in a given document to a specified topic. The news article is classified as positive or negative or neutral using Naive Bayes classifier by calculating the probabilities of each word from a given news article. By using topic modelling (LDA), topics of articles are detected and record data separately. The calculation of the overall sentiment of a chosen topic from different newspapers from previously recorded data done. 2020 The authors and IOS Press.