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Predictive Analysis of Sleep Disorders Using Machine Learning: A Comprehensive Analysis
The diagnosis of sleep disorders often relies on subjective patient reports, sleep diaries, and potentially cumbersome polysomnography (PSG) tests. However, these methods have limitations such as subjectivity, sleep diaries require meticulous effort, and expensive PSG tests are expensive, resource-intensive, and may not accurately capture sleep patterns in a non-clinical setting. Sleep disorders pose significant health risks and can impair overall well-being. Predictive analysis plays a crucial role in identifying individuals at risk of developing sleep disorders, enabling timely interventions and personalized treatment plans. In this paper, a comparative analysis of regression and classification models for sleep disorders prediction using machine learning (ML) techniques on insomnia and sleep apnea are discussed. Through extensive experimentation and comparative analysis, XGBoost and AdaBoost demonstrated as the most effective predictive models for insomnia and sleep apnea. AdaBoost and XGBoost classifiers are displaying 93.49% and 92.73% respectively. It is therefore possible to draw the conclusion that AdaBoost and XGBoost are doing well based on the findings as a whole, as indicated by the results. Our findings contribute to advancing the understanding and application of ML techniques in sleep disorder prediction, paving the way for more accurate and timely diagnosis based on ML techniques and personalized interventions in clinical practices. 2024 IEEE. -
Enhancing Security and Resource Optimization in IoT Applications with Blockchain Inclusion
The rapid proliferation of Internet of Things (IoT) devices has ushered in a new era of connectivity and data-driven applications. However, optimizing the allocation of resources within IoT networks is a pressing challenge. This research explores a novel approach to resource optimization, combining blockchain technology with enhanced security measures, while addressing the critical concerns of time and energy consumption. In this study, we propose a resource allocation framework that leverages the transparency and immutability of blockchain to enhance data integrity and security in IoT applications. The blockchain-based method is utilized to identify the malicious users in the IoT applications. The proposed method is implemented in MATLAB and performance is evaluated by performance metrics such as the probability of detection, false alarm probability, average network throughput, and energy efficiency. The proposed method is compared by existing methods such as Friend or Foe and Tidal Trust Algorithm. To further optimize this process, we introduce a Hybrid Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), a powerful optimization technique designed to minimize time delays and energy consumption in IoT environments. Our findings demonstrate the effectiveness of the proposed approach in achieving resource efficiency, reducing time and conserving energy within IoT networks. 2023 IEEE. -
STOCHASTIC BEHAVIOUR OF AN ELECTRONIC SYSTEM SUBJECT TO MACHINE AND OPERATOR FAILURE
A stochastic model is developed by assuming the human (operator) redundancy in cold standby. For constructing this model, one unit is taken as electronic system which consists of hardware and software components and another unit is operator (human being). The system can be failed due to hardware failure, software failure and human failure. The failed hardware component goes under repair immediately and software goes for upgradation. The operator is subjected to failure during the manual operation. There are two separate service facilities in which one repairs/upgrades the hardware/software component of the electronic system and other gives the treatment to operator. The failure rates of components and operator are considered as constant. The repair rates of hardware/software components and human treatment rate follow arbitrary distributions with different pdfs. The state transition diagram and transition probabilities of the model are constructed by using the concepts of semi-Markov process (SMP) and regenerative point technique (RPT). These same concepts have been used for deriving the expressions (in steady state) for reliability measures or indices. The behavior of some important measures has been shown graphically by taking the particular values of the parameters. 2024, Gnedenko Forum. All rights reserved. -
Crossing Worlds and Resisting Power: Fantasy and Metaphorical Borders in Srivatsa and Karunatilaka
The act of crossing and navigating physical, cultural, and symbolic borders has a profound impact on the shaping of identities, with a focus on the dynamics of resistance and power. By analyzing, through the lens of Critical Discourse Analysis (CDA), the experiences of characters portrayed in select Fantasy Fiction [The Spice Gate by Prashanth Srivatsa (2024); and The Seven Moons of Maali Almeida by Shehan Karunatilaka (2022)] in navigating borders-whether literal or metaphorical-the chapter studies how these crossings challenge fixed notions of identity and belonging. The protagonists of the chosen Fantasy works stem from both highly marginalized communities: the lower castes in India, and the more universally discriminated LGBTQIA+ people. Drawing on interdisciplinary perspectives pertaining to gender, sexuality, caste, oppression, and resistance, the chapter examines the act of border-crossing to illuminate the interplay between Self and Other, belonging and alienation, while confronting structures of power and oppression. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Exploring Music, Art, and Literature in Conflict Narratives Using Fantasy Fiction: War Through a Cultural Lens
The study of war has long been confined to disciplines such as history, political science, and journalism. However, with the evolution of modern conflicts and the shifting global landscape, it is imperative that scholars also explore more unconventional or often overlooked mediums, including literature, art, and music-not just to study how civilians respond to war and the trauma it leads to, but also the intricacies of war itself. This study examines war and conflict narratives through a cultural lens, using Critical Discourse Analysis of Letters of Enchantment (2023), a fantasy duology by Rebecca Ross. Fantasy fiction, once perceived as an escapist genre, has recently developed into a medium capable of exploring serious sociopolitical themes, including warfare, propaganda, and trauma. This study explores the ways in which the duology portrays war through themes of media influence, artistic expression, censorship, and music suppression, drawing parallels with real-world historical and contemporary conflicts. 2026, IGI Global Scientific Publishing. All rights reserved. -
A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of Smart Cyber-Physical Systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. The interconnection of the Internet of Things (IoT), artificial intelligence (AI), and big data analytics considering their impact and the creation of sustainable enhancing the quality of public services. 2026 by IGI Global Scientific Publishing. All rights reserved. -
A cyber-physical systems and the smart city vision: A comprehensive guide
The process of urban areas' transformation into smart cities with the help of smart cyber-physical systems (SCPS) is one of the most defining trends of modern urbanism. It requires a multifaceted perspective of smart cities, thereby evaluating the facets of SCPS intently concerning the complexities of their integration in urban structures while exploring their influence that transcends the domains of social sciences and economics, which has become crucial. In this context, smart cities are constructed as integrated systems at the crossroads of the digital and the physical: they sustain, facilitate, and improve the performance of the city's functions and living environment. The importance of technological environments in orientation and close consideration of SCPS reveals the functions in gathering data, immediate analysis, and decision-making processes of urban management. 2025, IGI Global Scientific Publishing. -
From Belief to Bandwidth: Navigating Freedom of Religion or Belief in the Age of Algorithms
As our lives become increasingly intertwined with digital technology and way people experience and express religion is also continuously evolving. This one chapter explores how idea of FoRB is evolving in digital world over course of time, where smartphones, social media, algorithms have become everyday tools for faith, connection, control. While digital spaces can open up new opportunities for interfaith dialogue, spiritual exploration, community- building, they can also pose serious risks. Issues like online hate speech, digital surveillance, content moderation, algorithmic bias often challenge free expression of belief. This chapter will have a closer look at how FoRB operates in online environments by combining insights from international human rights law, digital sociology, case studies from around world. We here focus on examining how free belief truly is in age of internet, considering what it takes to protect that freedom when technology both connects and controls. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Blockchain-based node authentication algorithm for securing electronic health record data transmission
The advent of Internet of Things (IoT) technologies in healthcare has heightened risks to Electronic Health Records (EHRs), including authentication vulnerabilities and data privacy concerns. This study proposes a novel blockchain-based node authentication algorithm for IoT healthcare, integrating Hyperledger Fabric, Homomorphic Encryption, and Recurrent Neural Networks (RNN). Employing a dual-layer security approach, the methodology utilizes a challenge-response mechanism and dynamic key exchange to ensure tamper-proof data transmission. Encrypted processing preserves confidentiality, while machine learning enhances anomaly detection accuracy to 99.01%, achieving a security rate of 99%. Comprehensive evaluations demonstrate significant improvements in efficiency, scalability, and robustness, addressing latency and computational overhead challenges. By fusing blockchains immutability with intelligent encryption and authentication, this solution revolutionizes EHR protection in IoT environments and scalable healthcare data management. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
A Novel Blockchain-Integrated Deep Learning Framework for Securing Smart Healthcare Communication Networks
With the rapid expansion of intelligent medical equipment and their interconnectedness through the Internet of Things (IoT), addressing safety issues in the communicating system has become increasingly critical. A learning mechanism is proposed for an intelligent healthcare-based communication system that uses blockchain for secure network communication and incorporates a data evaluation layer based on cloud which actively segregates and ranks transactions into three main categories: Good, Moderate, and Malware. Fog servers are utilized to route the communicating nodes via Rician and Rayleigh channels. The learning mechanism employs a deep neural network to instruct and classify categories, thereby improving the blockchain layer's decision-making process. This paper introduces several significant contributions, such as the development of a secure blockchain framework for user authentication and a protected digital ledger for communication. Additionally, it incorporates a cloud-driven data analysis layer combined with a neural network to improve training accuracy and category classification. The developed algorithm surpassed the existing works in terms of quality of service (QoS) parameters with low latency, bit error rate (BER), higher signal to inference plus noise ratio (SINR), packet delivery ratio (PDR), true detection rate (TDR), false detection rate (FDR), and throughput. Also, a thorough comparison of consensus mechanisms like practical Byzantine fault tolerance (pBFT), proof of work (PoW), Raft, and Paxos is done to ensure which consensus helps optimize the proposed system in terms of security and fault tolerance with low latency and energy-efficient operations. It also establishes a secure and efficient communication network for smart healthcare, aimed at enhancing the overall quality of life for individuals. 2025 Wiley Periodicals LLC. -
Role of Graph Convolutional Neural Networks (GCNN) in Computer Vision Applications
Graph Convolution Neural Networks (GCNNs) are an important concept in advancing computer vision by transforming the understanding and modeling of graph-structured data. They have a unique capability to capture intricate relations along with the visual content that goes beyond the traditional and usual convolutional neural networks, it also empowers computers to observe and interpret the complex interconnection between the elements in images, which enhances the depth and nuance of visual dentata analysis. As a revolutionary study in computer vision, GCNNs are poised to transform various industries by unleashing new frontiers in the visual information domains analysis and interpretation. Their multifaceted applications promise to reshape the landscape of computer vision. 2026 Scrivener Publishing LLC. -
Localizing and Classifying Kannada Texts Using a YOLO-Based Approach
Extracting handwritten characters from the scanned documents is a critical step due to the inherent complexities of various writing styles, inconsistent alignments, multi-touch scenarios, and overwriting characters. Expanding upon the real-time object detection capabilities of YOLOv8 (You Only Look Once), the current paper presents an experiment utilizing a dataset of 2000 handwritten images. This dataset combines the standard dataset (Chars74K) with the custom dataset featuring multi-touch handwritten text, encompassing both individual characters and character combinations that form words. The annotations were created using the Roboflow application and exported to a yaml (yet another markup language) file. The hybrid dataset was split into training, validation, and testing sets. The evaluation process yielded an accuracy of 96.8% at a threshold of 0.5 for recognizing and classifying the characters. The result suggests a positive correlation between training dataset size and model accuracy. Further, fine-tuning the hyperparameters could increase the accuracy upto 98.4%. Additional experiments were conducted to compare YOLOv8 and Detectron2 with Faster R-CNN. The results demonstrated that YOLOv8 offers substantially faster inference times, while Detectron2 with Faster R-CNN exhibited marginally higher accuracy in few classes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Enhancing Kannada Handwritten Text Processing: A Deep Learning Approach to Optimized Recognition and Segmentation
Digitalization ensures that information is available in diverse regional languages, empowering more cultures and perspectives to be heard and understood. One of the regional languages considered for empowering information access is the Handwritten Kannada document. Extracting text from these documents requires overcoming several obstacles, such as deciphering diverse handwriting styles, accommodating inconsistencies in character size, and the presence of multiple touches between characters. The present paper explored recognizing and segmenting Kannada handwritten characters using a deep learning model, specifically YOLOv8. While YOLOv8 is primarily known for real-time object detection, the paper suggests its potential for character detection tasks. The model achieved a promising mean Average Precision (mAP) of 96.8% at a threshold of 0.5 on a hybrid dataset consisting of 2476 images and 95.0% on character segmentation. This experiment adds to the growing body of research exploring YOLOv8s capabilities beyond traditional real-time object detection and instance segmentation. 2025 The Authors. Published by Elsevier B.V. -
Synergistic Hybrid Segmentation for Handwritten Kannada Word Recognition Addressing Deep Learning Challenges
The handwritten Kannada script has an intricate aksharas that are formed by combining consonants, vowels, and ottus. These complex combinations pose significant hurdles for automated text segmentation. The inherent diversity in handwriting styles, coupled with prevalent character overlap, multi-touch connections, varied curve structures such as upper open curve(OC), upper closed curve (CC), and the highly condensed nature of Ottaksharas, routinely blurs character boundaries, leading to severe segmentation errors that propagate and compromise overall recognition accuracy. A hybrid approach that customizes adaptive traditional methods like vertical pixel count, to identify true character gaps in handwritten Kannada characters could effectively manage character overlap, or segment multi-touch characters or Ottaksharas. This pre-processing stage can allow subsequent deep learning models to recognize this segmented character. This will avoid significant hurdles: immense data requirements for pixel-level annotations, high computational costs for dense prediction, and significant architectural complexities for precise boundary delineation and handling connectivity. Given these constraints, particularly with less-resource language like Kannada, scaling deep learning models will lead to ever erroneous recognition. This paper argues that modified traditional approaches, by directly embedding customized knowledge and leveraging targeted feature engineering, can offer a computationally efficient and data-lean alternative. This strategy enables more robust segmentation for complex Kannada characters, providing a practical pathway for automated handwritten text processing in such linguistic domains. 2025 IEEE. -
A Comparative Study of LGMB-SVR Hybrid Machine Learning Model for Rainfall Prediction
Weather forecasting is a critical factor in deter mining the crop production and harvest of any geographical location. Among various other factors, rainfall is a crucial determining component in the sowing and harvesting of crops. The aim and intent of this paper is to analyze various machine learning algorithms like LightGBM and SVR, and develop a hybrid model using LightGBM and SVR to accurately predict rainfall The hybrid model implements both LightGBM and SVR on a preprocessed dataset and then combines the predicted values of the results through an ensemble model which considers the average of these values based on a predefined weight. The weight of the model is determined by considering various combinations, and the result with the least error is taken into consideration for that particular dataset. The study shows that the hybrid model performed better than LightGBM and SVR individually, and produced the least root mean square error yielding a more accurate prediction of rainfall. 2021 IEEE. -
Broadband Spectral Properties of MAXI J1348-630 using AstroSat Observations
We present broadband X-ray spectral analysis of the black hole X-ray binary MAXI J1348-630, performed using five AstroSat observations. The source was in the soft spectral state for the first three observations and in the hard state for the last two. The three soft state spectra were modeled using a relativistic thin accretion disk with reflection features and thermal Comptonization. Joint fitting of the soft state spectra constrained the spin parameter of the black hole a * > 0.97 and the disk inclination angle i = 32.9 ? 0.6 + 4.1 degrees. The bright and faint hard states had bolometric flux a factor of ?6 and ?10 less than that of the soft state respectively. Their spectra were fitted using the same model except that the inner disk radius was not assumed to be at the last stable orbit. However, the estimated values do not indicate large truncation radii and the inferred accretion rate in the disk was an order of magnitude lower than that of the soft state. Along with earlier reported temporal analysis, AstroSat data provide a comprehensive picture of the evolution of the source. 2022. National Astronomical Observatories, CAS and IOP Publishing Ltd. -
Hybridization of Texture Features for Identification of Bi-Lingual Scripts from Camera Images at Wordlevel
In this paper, hybrid texture features are proposed for identification of scripts of bi-lingual camera images for a combination of 10 Indian scripts with Roman scripts. Initially, the input gray-scale picture is changed over into an LBP image, then GLCM and HOG features are extracted from the LBP image named as LBGLCM and LBHOG. These two feature sets are combined to form a potential feature set and are submitted to KNN and SVM classifiers for identification of scripts from the bilingual camera images. In all 77,000-word images from 11 scripts each contributing 7000-word images. The experimental results have shown the identification accuracy as 71.83 and 71.62% for LBGLCM, 79.21 and 91.09% for LBHOG, and 84.48 and 95.59% for combined features called CF, respectively for KNN and SVM. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting
In this work, we propose a Graph?Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not?very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to?learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as?well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop?yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for?short-term and long-term forecasts. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A Stochastic Method for Optimizing Portfolios Using a Combined Monte Carlo and Markowitz Model: Approach on Python
The main of the study is to comprehend how the mean variance efficient frontier method may be used in conjunction with Markowitz portfolio theory to produce an optimal portfolio. The study uses daily observations 8 pharma companies closing price namely Auropharma, Granules, Glaxo, Lauruslabs, Pfizer, Sanofi and Torntpharma. Further, Nifty pharma index is considered as benchmark index to check the performance of the chosen companies. The study chosen the reference period from 2020 to 2023 and required data has been extracted from the National Stock Exchange (NSE). This research is based on implementing a stochastic method for efficient portfolio optimisation employing a blended Monte Carlo and Markowitz model. In order to forecast the price of these indices in the future and to determine the likelihood of profit or loss while investing in a portfolio of stocks representing the aforementioned indices, the study also uses Monte Carlo simulation. The study involves two algorithms, namely the deterministic optimisation algorithm, which uses Markowitz Portfolio Theory, and the probabilistic optimisation algorithm, which uses Monte Carlo simulation. The study employed correlation matrix to find the exist relationship between the chosen companies and benchmark index. Also, expected return and volatility has been identified with the help of standard deviation using Python. The study found that the NIFTY Pharma index offers a higher return of 14.35. In addition to this, NIFTY Pharma portfolio's volatility is considerably higher. The study concludes that the NIFTY pharma portfolio is more suitable for those investors who have an appetite for risk. 2024 R. Mallieswari et al., published by Sciendo. -
A Stochastic Method for Optimizing Portfolios Using a Combined Monte Carlo and Markowitz Model: Approach on Python
The main of the study is to comprehend how the mean variance efficient frontier method may be used in conjunction with Markowitz portfolio theory to produce an optimal portfolio. The study uses daily observations 8 pharma companies closing price namely Auropharma, Granules, Glaxo, Lauruslabs, Pfizer, Sanofi and Torntpharma. Further, Nifty pharma index is considered as benchmark index to check the performance of the chosen companies. The study chosen the reference period from 2020 to 2023 and required data has been extracted from the National Stock Exchange (NSE). This research is based on implementing a stochastic method for efficient portfolio optimisation employing a blended Monte Carlo and Markowitz model. In order to forecast the price of these indices in the future and to determine the likelihood of profit or loss while investing in a portfolio of stocks representing the aforementioned indices, the study also uses Monte Carlo simulation. The study involves two algorithms, namely the deterministic optimisation algorithm, which uses Markowitz Portfolio Theory, and the probabilistic optimisation algorithm, which uses Monte Carlo simulation. The study employed correlation matrix to find the exist relationship between the chosen companies and benchmark index. Also, expected return and volatility has been identified with the help of standard deviation using Python. The study found that the NIFTY Pharma index offers a higher return of 14.35. In addition to this, NIFTY Pharma portfolio's volatility is considerably higher. The study concludes that the NIFTY pharma portfolio is more suitable for those investors who have an appetite for risk. 2024 R. Mallieswari et al., published by Sciendo 2024.