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Development and implementation of algorithm for image preprocessing and microorganism
The digital revolution has changed most aspects of modern life. Nowhere has the change been more fundamental than in the field of microscopy. Researchers who use the microscope in their investigations have been among the pioneers who applied digital processing techniques to images. Vision is most powerful of the five senses of human being. Digitized visual information provides high impact on the subject. Digital image processing is concerned with the extraction of useful information from images. Visual information from microscopic images of microorganisms is analyzed regularly. This has resulted in a need to understand and implement digital processing on microscopic images. The purpose of this thesis is to bring new digital image processing techniques for the noise removal of microscopic image of microorganisms. The digitized image processing includes image representation; improving image quality by removing noise; and enhancing the quality of microscopic images. -
From Concept to Clinic
Virtual reality (VR) is an innovative technology with various applications in fields such as simulation, gaming, sports, and entertainment. VR technology has extended its reach into the medical industry, leveraging computer-generated information and visuals to simulate real-world sensory experiences. Augmented reality (AR) is an expertise that covers computer-generated virtual objects onto the real world when it is seen through mobile phones, tablets, or AR glasses. VR and AR have started popular in 2016. Autism spectrum disorder (ASD) is a many-sided neurodevelopmental condition and is caused because of difficulties in social interaction, communication, and iterative behavior. ASD is increasingly predominant globally, touching roughly 2 in 100. If there is timely diagnosis and intervention, then there will be fewer problems. In recent years, AR and VR technologies have become one best option and novel means in the analysis and treatment of autism. Controlled environments that can imitate real-life social situations, contribution clinician's critical insights into the perceptions, and interactions of individuals with ASD in relation to their surroundings will be provided by AR and VR. These technologies assist customized intermediations, rendering them extremely adaptable to the distinct requirements of each individual. 2026 Scrivener Publishing LLC. -
Retail landscaping in India - Challenges and strategies /
International Journal in Management and Social Science, Vol. 4, Issue 11, pp. 24-34, ISSN No. 2321-1784. -
The effect of hopeful lyrics on levels of hopelessness among college students
Hopelessness is a product of negative future expectations, negative feelings toward the future, and feeling a lack of control over future improvements. College students are seen to experience hopelessness. This study aimed to reduce levels of hopelessness in college students through an intervention that involved listening to songs having hopeful lyrics. The sample consisted of college students (N = 66), who were randomly assigned to three groups, namely the lyrics-music group, music-only group, and the control group (no intervention). The Becks Hopelessness Scale was used to measure their levels of hopelessness before the intervention and at the end of four weeks. The lyrics-music group and the music group participants were exposed to songs and instrumental tracks, respectively, twice a week, for four weeks. The Wilcoxon Signed-Rank test for related samples was used to analyze the effect of the intervention on levels of hopelessness. The KruskalWallis test was used to analyze the differences across the three groups. Results indicated that the lyrics-music group had a significant decrease in levels of hopelessness after the intervention. However, the music group and the control group showed no significant decrease. There was a significant difference between the three groups with regard to the difference score obtained from pre to post intervention. Thus, the evidence suggests that hopeful lyrics do have an effect on hopelessness and can be seen as differing from the functions of music alone. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
The Development and Feasibility of A Self-Efficacy and Self-Esteem Based Intervention on Music Performance Anxiety Among Majors
Music Performance Anxiety (MPA) has been seen to adversely affect a student s goal-setting with respect to careers in music. Two variables that are closely related to the development and maintenance of MPA are self-efficacy and self-esteem of the student. The aim of this research is to present the experiences of students with MPA and the feasibility of an intervention to help reduce MPA among music majors while focusing on building their self-esteem and self-efficacy. The study followed a mixed method, exploratory sequential design. Ten participants were recruited for the newlinequalitative phase and were interviewed using a phenomenological approach. Data from these interviews were analysed using Interpretive Phenomenological Analysis. Themes that were found in the qualitative phase such as blocks growth of performer , Fear of judgment , lacking confidence in skills , comparison with peers , need for appreciation , pointed toward the need to build self-efficacy and self-esteem among the performers. The themes from this analysis were then used, along with previous research evidence to develop a self-esteem and self-efficacy based intervention. A quasi-experimental design was used to carry out and assess the feasibility of this intervention. The intervention lasted for eight sessions, where the experimental group took part in these sessions that consisted of both theoretical and practical components, which lasted for one hour each. The control group on the other hand did not receive any treatment and were not a part of any of these sessions either. The experimental group that consisted of 13 participants and the control group that consisted of 12 participants were assessed on their MPA, Musical Self-Efficacy and Musical Self-Esteem levels at the start of eight weeks and then at the end of eight newlineweeks. The scale used to assess these variable were the K-MPAI (Kenny, 2009), the newlineMusical Self-efficacy scale (Ritchie and Williamon, 2010), and the Self-esteem of newlinemusic ability scale (Schmitt, 1979) respectively. -
Detection and Robust Classification of Lung Cancer Disease Using Hybrid Deep Learning Approach
Effective lung cancer diagnosis and treatment hinge on the early detection of lung nodules. Various techniques, such as thresholding, pattern recognition, computer-aided diagnostics, and backpropagation calculations, have been explored by scientists. Convolutional neural networks (CNNs) have emerged as powerful tools in recent times, revolutionizing many aspects of this field. However, traditional computer-aided detection systems face challenges when categorizing lung nodule detection. Excessive reliance on classifiers at every stage of the process results in diminished recognition rates and an increased occurrence of false positives. To address these issues, we present a novel approach based on deep hybrid learning for classifying lung lesions. In this study, we explore multiple memory-efficient and hybrid deep neural network (DNN) architectures for image processing. Our proposed hybrid DNN significantly outperforms the current state-of-the-art, achieving an impressive accuracy of 95.21%, all while maintaining a balanced trade-off between specificity and sensitivity. The primary focus of this research is to differentiate between CT scans of patients who have early-stage lung cancer and those who do not. This is achieved by utilizing binary classification networks, including standard CNN, SqueezeNet, and MobileNet. 2023 IEEE. -
Bronchop Neumonia Detection Using Novel Multilevel Deep Neural Network Schema
Pneumonia is a dangerous disease that can occur in one or both lungs and is usually caused by a virus, fungus or bacteria. Respiratory syncytial virus (RSV) is the most common cause of pneumonia in children. With the development of pneumonia, it can be divided into four stages: congestion, red liver, gray liver and regression. In our work, we employ the most powerful tools and techniques such as VGG16, an object recognition and classification algorithm that can classify 1000 images in 1000 different groups with 92.7% accuracy. It is one of the popular algorithms designed for image classification and simple to use by means of transfer learning. Transfer learning (TL) is a technique in deep learning that spotlight on pre-learning the neural network and storing the knowledge gained while solving a problem and applying it to new and different information. In our work, the information gained by learning about 1000 different groups on Image Net can be used and strive to identify diseases. 2023 EDP Sciences. All rights reserved. -
A Novel Energy-Efficient Hybrid Optimization Algorithm for Load Balancing in Cloud Computing
In the field of Cloud Computing (CC), load balancing is a method applied to distribute workloads and computing resources appropriately. It enables organizations to effectively manage the needs of their applications or workloads by spreading resources across numerous PCs, networks, or servers. This research paper offers a unique load balancing method named FFBSO, which combines Firefly algorithm (FF) which reduces the search space and Bird Swarm Optimization (BSO). BSO takes inspiration from the collective behavior of birds, exhibiting tasks as birds and VMs as destination food patches. In the cloud environment, tasks are regarded as autonomous and non-preemptive. On the other hand, the BSO algorithm maps tasks onto suitable VMs by identifying the possible best positions. Simulation findings reveal that the FFBSO algorithm beat other approaches, obtaining the lowest average reaction time of 13ms, maximum resource usage of 99%, all while attaining a makespan of 35s. 2023 IEEE. -
Natural Language Processing in Medical Applications
Medical applications of machine learning are very new, and there are still several obstacles that limit their widespread use. There is still a need to address issues like high dimensionality data and a lack of a standard data schema. An intriguing way to explore the possibilities of machine learning in healthcare is to apply it to the difficult problem of cardiovascular disease diagnosis. At the present day, cardiovascular disorders account for the majority of deaths worldwide. It is often too late to adopt appropriate treatment for many of them because they progress for a long time without showing any symptoms. Because of this, its crucial to get checked up on routinely so that any developing diseases can be caught early. If the sickness is caught early enough, effective therapy can be put into place to stop the progression of the illness. This is done with the intention of analysing data from many sources and making use of NLP to overcome data heterogeneity. This paper evaluates the usefulness of several machine learning methods (such as the Naive Bayes (NB), Transductive Neuro-Fuzzy Inference, and Terminated Ramp-Support Vector Machine (TR-SVM)) for healthcare applications and suggests using Natural Language Processing (NLP) to address issues of data heterogeneity and the transformation of plain text. The implementation, testing, comparison of performance and analysis of the parameters of the algorithms used for diagnosis have simplified the process of selecting an algorithm better suited to a certain instance. TWNFI is particularly effective on larger datasets, while Terminated Ramp-Support Vector Machine is well suited to lesser datasets with a lower number of magnitudes due to performance difficulties. 2024 Scrivener Publishing LLC. -
A Comprehensive Survey on Decoder Design using Quantum-dot Cellular Automata
QCA offer a compelling alternative to CMOS technology, providing benefits such as low power consumption, high speed, high density, and the ability to surpass the nanoscale limitations of CMOS. QCA is increasingly being adopted in VLSI designs as a solution for reducing power consumption and thermal dissipation. This paper analyzes the area, cell count, and latency of different 2:4 decoders to determine the most efficient design based on these factors. Decoders play a critical role in Quantum-dot Cellular Automata(QCA) by enabling efficient data routing, memory addressing, and logic control while minimizing power consumption and reducing interconnect complexity. The study employs a specialized logic gate known as the Toffoli gate, which is renowned for its capability in reversible computing, allowing information processing without data loss. Future advancements in 2:4 decoders using QCA should prioritize optimizing clocking schemes, improving fault tolerance, and developing scalable architectures to address fabrication challenges and enhance reliability in practical applications. The circuits are simulated using QCA Designer software. 2025 IEEE. -
Quantum Machine Learning Models for Enhancing Big Data Analytics
The blistering growth of information in contemporary business is a great challenge to the traditional analytics in the context of speed, accuracy, and scalability. The Quantum Machine Learning (QML) has the chance to provide a ground-breaking solution, based on quantum superposition, entanglement execution to speed up a computational process and increase predictability. The current work proposed a Hybrid Quantum Classical Framework (HQCF) which is a combination of quantum algorithms and conventional machine learning to solve high-dimensional big data analytics. The proposed system shows huge performance improvements over classical foundations - attaining up to 10-percent higher prediction error, and cutting training costs by a factor of 47 percent in various fields such as finance, healthcare, and internet of things sensor information. The hybrid structure features high scalability as well which means that it can process datasets that are up to six million samples and thus it has a high level of scalability and strength. Such quantitative indicators indicate that quantum-enhanced analytic is technologically advanced or progressive to enhance computational-efficiency, generalization-of-models, and real-time ability to make a decision, with large-scale data settings. 2025 IEEE. -
Scalable Quantum Computing Approaches for Next-Generation Edge and IoT Devices
The fast development of Internet of Things (IoT) and edge devices requires secure energy efficient and scalable solutions to computing in highly resource-constrained environments. In the post-quantum future, classical cryptographic and computational algorithms will be restricted in the ability to fulfil stringent performance and security requirements, such as high latency, bandwidth, and security. Such issues might be solved by the framework proposed in this paper which is built upon the ideas of lightweight quantum algorithms, quantum-inspired optimization, and post-quantum security primitives in the form of a scalable quantum-assisted hybrid architecture. the architecture is heterogeneous, supporting a variety of deployment types in the Internet of Things, with modular quantum co-processors and cloud-edge interconnection making them deployable. Experimental measurements are showing 32 per cent less encryption decryption latency than RSA/ECC and PQC baselines, as well as enhanced fault tolerance in noisy settings and scalability in the thousands of devices. These outcomes underscore the technical contributions achieved by the proposed architecture as well as the practical viability of the proposed architecture in next generation IoT systems. The bridge between theoretical advancements and deployment-oriented design is an essential contribution that the study provides in guaranteeing the security, intelligent, and sustainability of the IoT ecosystems based on scalable quantum approaches. 2025 IEEE. -
Drones for Transportation Logistics and Disaster Management
Explore the future of logistics and disaster management with this essential guide to the design, applications, and challenges of integrating advanced drone technology into intelligent transportation systems. Drones are quickly becoming an essential technology for navigating inaccessible areas, especially during emergency situations. However, the implementation of these drones requires strict standards, policies, and procedures. Currently, drones are being used in several industrial and service sectors, extending the possibilities of handling transportation and logistics. The future of transportation is based on unmanned vehicles, and it is important to identify their challenges and futuristic applications. Drones for Transportation Logistics and Disaster Management introduces the essential aspects of the technological advancement of drones, the challenges faced in current practices, and their advanced applications. The book describes future intelligent and resilient transportation systems backed by the Internet of Vehicle Things, the problems of big data analytics, and optimization techniques for in-house supply-chain management. Using a global multi-sector perspective, this volume will comprehensively cover essential components of drone systems, including their modeling, design, and maintenance, making it an essential guide for anyone looking to the future of disaster management. 2026 Scrivener Publishing LLC. -
BLOCKCHAIN-BASED DIGITAL TWINS: Research Trends and Challenges
A digital twin, in simple words, is a virtual model such that it accurately reflects an object or data. The exponential growth in the usage of blockchain, along with the amalgamation of AI and big data, have exploited the potential of the data generated from current industrial practices, giving rise to the digital twin model that can benefit from blockchain technology. This new book, Blockchain-Based Digital Twins: Research Trends and Challenges, addresses the next-generation technology to actualize secure systems, networks, and environments. Blockchain-based digital twins are poised to influence and be influenced by artificial intelligence, machine learning, and the Internet of Things in integrating and implementing smart contracts across industries. This concept is explored in detail in this book, as it introduces and analyzes the challenges and opportunities, details modeling and simulation, and explores the various applications of blockchain-based digital twins. The book discusses the security benefits of blockchain and analyses its use for the future of the Internet. The use of a blockchain-based digital twin model in various industries is also explored, including in the healthcare industry, agriculture industry, and mechanics. Key features: Discusses blockchain-supported digital twin applications Highlights the key benefits of using blockchain-based digital twins Presents a comprehensive review of the state-of-the-art research results for blockchain-based digital twins This comprehensive book, with peer-reviewed chapters, will prove beneficial to researchers and academicians working in the area of development of the blockchain-based digital twin applications. It will also be of interest to industry personnel, policymakers, and system designers working for the transformation of various industries using blockchain technology. 2025 by Apple Academic Press, Inc. -
A new perspective on the genesis of the 2019/2020 Australian bushfire and its atmospheric radiative impacts
Extensive investigations of the genesis and atmospheric radiative impacts of the Australian bushfires of August 2019 to January 2020 (also known as the black summer event) have been carried out using in-situ, multi-satellite, and reanalysis data. We present the observational evidence for the role of total water storage in the initiation of this event. A strong correlation was found between the depletion of the total water storage (sum of surface and sub-surface water storage) caused by the hydrological drought and the burnt area in southeast Australia. Notably, a decadal low of Liquid Water Equivalent Thickness (LWET) going below ?5 cm in December 2019 strongly suggests the crucial role of hydrological drought in the genesis of the black summer event. The hydrological drought provided favorable conditions for intense fire activity during the black summer event and increased the aerosol loading across Australia. The assimilated Aerosol Optical Depth revealed that the impact of the black summer event on the aerosol loading is higher than previously reported. The amplified aerosol backscattering, coupled with the increased surface albedo due to the prevailing drought, led to a significant surge in outgoing shortwave flux and contributed to regional cooling. Along with the increased aerosol loading, it has also been observed that the co-emitted carbon monoxide enhanced the ozone production at 850 hPa, further degrading the air quality. These findings will offer crucial insights for predicting extreme bushfire events and their mitigation policies. 2025 Elsevier Ltd -
Australian bushfire emissions result in enhanced polar stratospheric clouds
Extreme bushfire events amplify climate change by emitting greenhouse gases and destroying carbon sinks. They also cause economic damage, through property destruction, and even fatalities. One such bushfire occurred in Australia in 20192020, and this event injected large amounts of aerosols and gases into the stratosphere and depleted the ozone layer. While previous studies have focused on the drivers behind ozone depletion, the bushfire impact on polar stratospheric clouds (PSCs), a paramount factor in ozone depletion, has not been extensively investigated so far. Therefore, this study focuses on the effects of bushfire aerosols on the dynamics and stratospheric chemistry related to PSC formation and its pathways. An analysis from Auras Microwave Limb Sounder revealed that the enhanced hydrolysis of dinitrogen pentoxide significantly increased nitric acid (HNO3) in the high-latitude lower stratosphere in early 2020. This resulted in an anomalously high areal coverage of PSCs with ice, exceeding 3 standard deviations with respect to background period. Based on Lagrangian backward-trajectory analysis, we find that a predominant fraction (79 %) of the liquidnitric acid trihydrate (NAT) mixture formed via the ice-free nucleation pathway. These NAT particles subsequently acted as nuclei for ice formation, accounting for 95 % of the observed ice PSCs. This rapid conversion from NAT to ice likely contributed to the strong positive anomaly in ice PSC. This highlights the primary formation pathways of ice and liquidNAT mixtures and possibly helps us to simulate PSC formation and denitrification process better in climate models. These findings will contribute significantly to a deeper understanding of the impacts of extreme wildfire events on stratospheric chemistry and PSC dynamics. Author(s) 2025. -
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.