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Enhanced Lumpy Cattle Skin Disease Prognosis via Deep Learning Methods
Animal illness is growing in importance. Identification of the illness is important since various diseases may affect different animals, and immediate guidance will be provided. Cows with lumpy skin issues are caused by the Neethling infection. The affection of these diseases causes lasting injury to the cattle's skin. Reduced Poor growth, reversal, milk production, gravidity, and, in severe cases, mortality are the most common adverse consequences of the illness. We developed a deep learning-based architecture that can predict or recognize disease. A deep literacy system is required to identify the microorganism causing the lumpy skin disease. This system collects diverse cattle electronic medical records and uses data analysis to create an intelligent diagnosis system for cattle diseases. It involves text preprocessing to enhance data quality, and the ECLAT algorithm correlates disease names with probabilities, providing tailored treatment plans. The system ensures timely disease treatment, reducing herders' losses and promoting scientific intelligence in animal husbandry. 2024 IEEE. -
A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis
Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. -
Leveraging Deep Learning for Early Detection of Autism Spectrum Disorder in Augmented and Virtual Reality Mental Healthcare Environments
The advent of digital health interventions offers new vistas for the early detection and management of autism spectrum disorder (ASD). This paper explores the innovative application of deep learning algorithms within augmented reality (AR) and virtual reality (VR) environments to enhance the early detection of ASD. Integrating AR and VR in mental healthcare provides immersive, controlled settings in which individuals behaviors and responses can be observed and analyzed in real time. We propose a novel framework that utilizes deep learning techniques to process and analyze the data collected from these digital environments. Our approach leverages facial expression analysis, eye tracking, and behavioral pattern recognition to identify early signs of ASD. By capturing subtle nuances in behavior often overlooked in traditional diagnostic settings, our method offers a non-invasive, engaging, and efficient alternative for early ASD detection. The potential of this technology extends beyond diagnosis, offering possibilities for personalized intervention strategies that can be adapted to each individual's unique needs. Our findings indicate that deep learning in AR and VR environments could significantly enhance the accuracy and timeliness of ASD diagnosis, paving the way for earlier and more effective interventions. This study underscores the importance of interdisciplinary collaboration in developing innovative healthcare solutions, combining the fields of artificial intelligence, psychology, and digital media to improve outcomes for individuals with ASD. 2026 Scrivener Publishing LLC. -
Enhancing Diagnostic Accuracy for Autism with BRCNet: A Novel Approach for Brain Region Segmentation and Classification Using Deep Learning
In the quest to enhance the diagnostic accuracy of neural disorders, particularly autism, this paper presents a novel approach for brain region classification using advanced machine learning techniques. The study utilizes the ABIDE and AAL116 atlas datasets, focusing on segmenting and classifying brain regions from resting-state functional MRI (rs-fMRI) images. We propose a three-stage process. In the first stage, data collection and preprocessing are conducted, where rs-fMRI images are preprocessed into SPM12-NIfTI format. The second stage involves the segmentation of brain regions using a Regularized VNet, resulting in the extraction of AAL116 brain region images, which are then split into training, testing, and validation sets. In the third stage, we introduce a custom-designed BRCNet (Brain Region Classification Network), which discriminates between Autism and Normal classes. Our segmentation methods are rigorously evaluated using metrics such as Dice Score, Recall, and Precision, with the proposed method achieving a Dice Score of 0.985, Recall of 0.962, and Precision of 0.991, surpassing other tested methods like UNet, Active Contour, and Binary Unit. For classification, various methods, including Support Vector Machines (SVM), Decision Trees (DT), and Neural Networks like ResNet, are compared. Our findings demonstrate that ResNet achieves an exemplary performance with an Accuracy of 97.5%, Sensitivity of 96.2%, Specificity of 97.1%, Precision of 97%, and an F-Measure of 97.93%. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Enhancing Early Detection of Alzheimers Disease Through Integrated Deep Learning Models: A Multimodal Diagnostic Approach
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and functional impairment. Early detection is crucial for effective management and intervention. This study explores the effectiveness of an integrated deep learning approach combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the early detection of Alzheimer's disease using multimodal data. A novel deep learning model was developed and validated, integrating neuroimaging data (MRI and PET scans) with clinical data using a decision-level fusion strategy. The study utilized a dataset comprising 1000 anonymized patient records from the Alzheimers Disease Neuroimaging Initiative (ADNI). Models were assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic (ROC) curve (AUC). The integrated model demonstrated superior performance with an accuracy of 95%, precision of 94%, recall of 93%, and an F1-score of 93.5%. The model's AUC was 0.97, indicating excellent diagnostic capability. The proposed deep learning approach significantly improves the early detection of Alzheimers disease by effectively analyzing complex, multimodal data. This model holds considerable potential for clinical applications, providing a robust tool for healthcare professionals to diagnose AD in its early stages. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Phytochemicals and Biological Activities of Ceriops tagal (Perr.). C. B. Rob.
Plants have been used for medicines since ancient times as they serve critical needs and are easily accessible. In recent years, various nations have seen a major increase in the use of plant-based treatments, resulting in a significant rise in the global demand for herbal products. This chapter describes Ceriops tagal, a mangrove species with excellent potential for bioactive components and biological activity. The majority of the distinctive secondary metabolites and their analogs reported in this plant are di-, tri-, and tetra-terpenoids (dolabrane, lupane, oleanane, dammarane, and pimarane), phenolics, and steroids from the hypocotyls, roots, and aerial parts. Various studies reported 97 terpenoids and 14 other metabolites. Many biological activities have already been identified from various extracts, including anticancer, antidiabetic, antioxidant, anti-inflammatory, antibacterial, and neurotrophic activities. In this chapter, we explored the biological potential of C. tagal, particularly its anticancer and neuroprotective activities, and it may be valuable for young researchers looking into the potential drug for chemotherapeutic and neurotrophic properties for the treatment and prevention of cancerous and neurological disorders. Springer Nature Switzerland AG 2025. -
Phytochemicals and Biological Activities of Ceriops tagal (Perr.). C. B. Rob.
Plants have been used for medicines since ancient times as they serve critical needs and are easily accessible. In recent years, various nations have seen a major increase in the use of plant-based treatments, resulting in a significant rise in the global demand for herbal products. This chapter describes Ceriops tagal, a mangrove species with excellent potential for bioactive components and biological activity. The majority of the distinctive secondary metabolites and their analogs reported in this plant are di-, tri-, and tetra-terpenoids (dolabrane, lupane, oleanane, dammarane, and pimarane), phenolics, and steroids from the hypocotyls, roots, and aerial parts. Various studies reported 97 terpenoids and 14 other metabolites. Many biological activities have already been identified from various extracts, including anticancer, antidiabetic, antioxidant, anti-inflammatory, antibacterial, and neurotrophic activities. In this chapter, we explored the biological potential of C. tagal, particularly its anticancer and neuroprotective activities, and it may be valuable for young researchers looking into the potential drug for chemotherapeutic and neurotrophic properties for the treatment and prevention of cancerous and neurological disorders. Springer Nature Switzerland AG 2026. -
Phytochemicals and Biological Activities of Flowers of Clitoria ternatea (Butterfly Pea)
In recent years, many countries have witnessed a significant rise in the adoption of plant-based remedies, leading to a substantial increase in the global demand for herbal resources. This chapter explores Clitoria ternatea, a species with remarkable potential due to its bioactive constituents and diverse biological activities. Most of the unique secondary metabolites and their analogues identified in this plant are anthocyanins, including ternatins, preternatins, cyanidin, and delphinidin, along with phenolic acids, terpenoids, and phytosterols derived from the flowers. Altogether, 32 anthocyanins and 19 additional metabolites have been documented across various studies. Different extracts of C. ternatea have demonstrated a wide range of biological properties, notably antioxidant, antimicrobial, anti-inflammatory, antidiabetic, cytotoxic, and anticancer activities. To support young researchers investigating potential chemotherapeutic agents for the treatment and prevention of cancer and other diseases, this chapter focuses on the biological functions of C. ternatea, with particular emphasis on its anticancer and antidiabetic benefits. 2026 Hosakatte Niranjana Murthy. -
Patriarchal Constraints in Everyday Lives: Gender Roles, Matrilineality, and the Status of Contemporary Khasi Women
The Khasi tribe from Meghalaya in northeast India practices a matrilineal system, which is believed to be more egalitarian than patrilineal systems. The women of the Khasi tribe are often regarded as having a higher status than other women in India. However, despite belonging to a matrilineal society, Khasi women still face challenges in their social lives stemming from patriarchal constructs. This qualitative study examines the social status and subsequent challenges faced by Khasi women in contemporary India. Using in-depth interviews and observations of thirty urban and rural Khasi women in the East Khasi Hills District of Meghalaya, the study reveals how Khasi women experience contradictory and challenging roles, relationship dynamics, and gender stereotypes in their lives. More studies should examine the problems and challenges that Khasi women face in their society despite the benefits of a matrilineal system. 2026 Bridgewater State College. All rights reserved. -
Analysis of an Existing Method for Detecting Adversarial Attacks on Deep Neural Networks
Analyzes the existing method of detecting adversarial attacks on deep neural networks, proposed by researchers from Carnegie Mellon University and the Korean Institute of Advanced Technologies (KAIST) Ko, G. and Lim, G in 2021. Examines adversarial attacks, as well as the history of research on the topic. The paper considers the concepts of interpreted and not interpreted neural networks and features of methods of protection of the types of neural networks considered. The method for protecting against adversarial attacks is also considered to be applicable to both types of neural networks. An example of an attack simulation is given, which makes it possible to identify a sign showing that an attack has been committed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Gamification and Game-Based Learning: A Systematic Review and Comparative Analysis
In the modern world, characterized by the rapid development of technology and digitalization of almost all spheres of life, it is necessary to keep up with the times and gradually introduce information technology into our lives. This will allow us to remain competitive in a changing world, take advantage of new opportunities and improve our quality of life. It is important to understand that information technology is not just a fashion trend, but a necessary tool for successful development and progress. The paper examines the very concept of gamification, the main methods of introducing gamification into education, highlights the advantages of learning with the addition of gamification, and also works on comparing learning with and without gamification elements. The introduction of game elements into the educational process helps to improve the perception of educational material, as well as increase the level of motivation of the students themselves. It is worth noting that the learning process with the addition of game elements helps to improve attention, develop logical thinking, as well as analyze various situations. Gamification can be viewed from several angles. For a teacher, this teaching method will help to capture the attention of children, which will help create a working atmosphere in the classroom. And for students, gamification is a great opportunity to explore really important topics in game mode. They will have an increased interest in learning, which will have a beneficial effect on their further academic performance and learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Machine Learning Research Methods for Identifying Inaccurate Content
Social media, especially when disseminating news, is a valuable information resource. The paper presents methods for detecting fake news, comparing their effectiveness, identifying existing problems, and describes the vectors of further development of this research area. The paper begins with a description of the relevance of the Fake News problem, which clearly describes the negative impact of false news on all spheres of human life. The following is a description of methods for detecting false news, starting from the usual rules of text analysis and ending with complex ML algorithms. In this paper, a comparative analysis of detection methods is carried out, which is based on criteria of efficiency and accuracy. The author identifies the main problems of existing methods related to data quality, changing Fake News formats and the difficulties of automatically determining the reliability of information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Development of a Comprehensive Technology for Analyzing Data on the Used Car Market
In today's information society, organizations face a huge amount of data that requires analysis and intelligent technologies to make informed decisions. In this paper, the authors consider the problem of analyzing the used car market using big and open data technologies. The used car market has characteristics characterized by heterogeneity and dynamic demand depending on the region. This problem is relevant and important not only for companies involved in producing and selling cars but also for potential buyers. The authors developed a comprehensive data analysis technique based on the Python programming language and the K-means clustering algorithm in the research process. In the article, the authors described a comprehensive technology for analyzing the used car market, including various analysis methods, such as prices, offers, and competition. The proposed comprehensive technology includes various tools and programs for collecting, processing, and analyzing data. These methods can be combined into a single system, providing a more complete picture of the market and making more informed decisions. The structure of the study reflects an independent approach to the topic under study based on open data and research by Russian and foreign scientists. It should be noted that the study is based on a large amount of analytical data obtained from reliable sources and tools that confirm the conclusions formulated in this study. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Research of Prospects and Challenges in Artificial Intelligence Technology Teaching and Learning
Recently, knowledge in the field of artificial intelligence in order to modernize various aspects of human activity has played a significant role. The exploration of the opportunities and difficulties associated with the development of artificial intelligence technologies is becoming an important area of research, as it profoundly affects our perception of work, education, medicine and other spheres of existence. New methods of machine learning, deep learning and reinforcement learning are being developed. These technologies are changing our understanding of how machines can learn and adapt to the world around them. The application of artificial intelligence covers many areas, including healthcare, finance, education and industry. In medicine, for example, AI can improve diagnostic accuracy and develop customized treatments. In education, it is possible to create personalized learning plans for each student. While in industry, artificial intelligence technologies are able to optimize production processes and increase business efficiency. However, despite the potential benefits associated with learning artificial intelligence technologies, there are serious challenges that require careful analysis. These challenges include ethical dilemmas, such as issues of algorithm transparency and responsibility for making principled decisions. Data security and privacy are also among the key aspects that require innovative approaches to AI technology training. The main purpose of the research is to deeply analyze the prospects and challenges in the field of artificial intelligence technology training, provide a comprehensive understanding of the current state of this field, identify key areas of development and propose practical strategies for effectively overcoming challenges. Taking into account both positive and negative aspects, it is necessary to have a meaningful look at the future of artificial intelligence technology education, taking into account social, ethical and technical aspects. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Gamification and Game-Based Learning: A Systematic Review and Comparative Analysis
In the modern world, characterized by the rapid development of technology and digitalization of almost all spheres of life, it is necessary to keep up with the times and gradually introduce information technology into our lives. This will allow us to remain competitive in a changing world, take advantage of new opportunities and improve our quality of life. It is important to understand that information technology is not just a fashion trend, but a necessary tool for successful development and progress. The paper examines the very concept of gamification, the main methods of introducing gamification into education, highlights the advantages of learning with the addition of gamification, and also works on comparing learning with and without gamification elements. The introduction of game elements into the educational process helps to improve the perception of educational material, as well as increase the level of motivation of the students themselves. It is worth noting that the learning process with the addition of game elements helps to improve attention, develop logical thinking, as well as analyze various situations. Gamification can be viewed from several angles. For a teacher, this teaching method will help to capture the attention of children, which will help create a working atmosphere in the classroom. And for students, gamification is a great opportunity to explore really important topics in game mode. They will have an increased interest in learning, which will have a beneficial effect on their further academic performance and learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Software Package for Detecting Anomalies in User Authentication
Anomaly detection is a very important tool for various applications such as intrusion detection, fraud, malfunction, system health monitoring and event detection in IoT devices. Recently, user authentication has become an extremely popular topic in information security research environments. The definition of user authentication is formulated as the process of verifying the identity declared by the user for a system object. Authentication is a method used to distinguish between true or false authentication requests. There are many methods used to authenticate a user that can identify valid users in protected resources. This article discusses various methods for analyzing abnormal user behavior in information systems, namely such methods as machine learning, neural networks, hybrid methods. Based on the analysis of system logs in the Astra Linux operating system, a software package has been developed to identify anomalies when trying to authenticate users. 2025 IEEE. -
Growth, survival and haemato-biochemical profiles of the freshwater catfish, Pangasius sutchi (Fowler, 1937) fingerlings fed with Tinospora cordifolia leaf extract supplemented diet
The present study documents the possible effects of Tinospora cordifolia leaf extract supplemented diets on growth, survival and haemato-biochemical profiles of the catfish, Pangasius sutchi fingerlings. P. sutchi fingerlings were fed with formulated diets, supplemented with four different concentrations of T. cordifolia leaf extract (viz. 100, 200, 400 and 800 mg kg-1 of feed) for 45 days. Fingerlings fed with basal diet served as control. Various parameters of serum biochemical and haematology such as serum total protein content, albumin content, globulin content, albumin globulin ratio, glucose, erythrocytes count, leucocytes count were evaluated along with growth parameters. The results indicated that Specific Growth Rate (SGR), Feed Conversion Ratio (FCR), Protein Efficiency Ratio (PER), survival and Haemato-biochemical profiles such as total serum protein, albumin, globulin, albumin globulin ratio and serum glucose were high in the fingerlings fed with T. cordifolia leaf extract supplemented diets, irrespective of dosage, compared to control. Among the four concentrations of T. cordifolia leaf extract used, 400 mg/kg of feed group showed increased growth, survival and enhanced the health status of P. sutchi fingerlings. 2020, Egyptian Society for the Development of Fisheries and Human Health. All rights reserved. -
Micro grid Communication Technologies: An Overview
Micro grid is a small-scale power supply network designed to provide electricity to small community with integrated renewable energy sources. A micro grid can be integrated to the utility grid. Due to lack of computerized analysis, mechanical switches causing slow response time, poor visibility and situational awareness blackouts are caused due to cascading of faults. This paper presents a brief survey on communication technologies used in smart grid and its extension to micro grid. By integration of communication network, device control, information collection and remote management an intelligent power management system can be achieved 2022 IEEE. -
Power Line Communication Parameters in Smart Grid for Different Power Transmission Lines
In an electrical power system smart grid is a network that renewable energy sources along with smart devices. Communication capabilities of the conventional grid can be improved by the inclusion of superior sensing and computing abilities. Device control, remote management, information collection, intelligent power management is achievable by using communication networks. Wired communication technology is used because of its advantages like reliable connection, free from interference, and faster speed. In this paper, the data communication parameters have been analyzed using Power Line Communication (PLC) with various lengths of transmission lines. An orthogonal Frequency Modulation scheme is used to obtain the minimum BER.MATLAB Programming has been carried out and the results have been compared with the standards and found to be satisfactory. 2021 IEEE. -
Design of digital filters for multi-standard transceivers
This paper addresses on three different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area, delay and dynamic power consumption. The proposed decimation filter architectures reflect the considerable reduction in area and dynamic power consumption without degradation of performance. The filter coefficients are derived from MATLAB, the filter architectures are implemented and tested using Xilinx SPARTAN FPGA.First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the two different encoding schemesi.e. Canonic Signed Digit (CSD) and Minimum Signed Digit (MSD) are used for filter coefficients and then the architecture performances are tested.The results of CSD and MSD based architectures show a considerable reduction in the area and power against the conventional number system based filter design implementation. The implementation results reflect that considerable reduction in area of 47.89% and dynamic power reduction of 28.64% are achieved using hybrid architecture. 2015 School of Electrical Engineering and Informatics. All rights reserved.