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"Kumar, M."
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Classification of ECG signal using FFT based improved Alexnet classifier
Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.
Journal Article
Re-emerging Russia : structures, institutions and processes
\"This book examines the evolution, contexts and politics of the structures and institutions that shape contemporary Russia. It analyses the Soviet dissolution, revealing the combination of structural and agency factors. It traces the re-emergence of Russia from a unique perspective that is neither Western nor Eurasian, but specifically Indian, located in the global South. The book looks at key theoretical concepts and practices like democratic centralism that produced an overly centralised and rigid hierarchy within the Communist Party. This book assesses the continuities and changes with the Soviet past and the way the Russian regimes of the past two decades have reinvented and reshaped them. This book provides a multifaceted interpretation of contemporary Russia for general readers and specialists. \"-- Provided by publisher.
Book
Silver decorated CeO2 nanoparticles for rapid photocatalytic degradation of textile rose bengal dye
High quality silver (Ag) decorated CeO
2
nanoparticles were prepared by a facile one-step chemical method. The samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), High resolution transmission electron microscopy (HR-TEM), fourier transform infrared spectrometer (FT-IR), electron paramagnetic resonance (EPR), X-ray photoelectron spectroscopy (XPS), UV–Visible absorption (UV–Vis), photoluminescence (PL) and thermogravimetric analysis. The decoration of Ag on CeO
2
surface was confirmed by XRD, EPR and HR-TEM analysis. Harmful textile pollutant Rose Bengal dye was degraded under sunlight using the novel Ag decorated CeO
2
catalyst. It was found that great enhancement of the degradation efficiency for Ag/CeO
2
compared to pure CeO
2
, it can be ascribed mainly due to decrease in its band gap and charge carrier recombination rate. The Ag/CeO
2
sample exhibited an efficient photocatalytic characteristic for degrading RB under visible light irradiation with a high degradation rate of 96% after 3 h. With the help of various characterizations, a possible degradation mechanism has been proposed which shows the effect of generation of oxygen vacancies owing to the decoration of Ag on the CeO
2
surface.
Journal Article
Sustainable lubrication
\"This book overviews recent advances in the development of lubricants and their usage in different tribological systems, starting from nanoscale contacts up to macroscale assemblies with specific focus on sustainable green lubrication choices including base fluids. Further, it covers advances and optimization of new type of lubrication systems according to their usage in various tribological systems as gears, bearings, micro-electromechanical systems, and production equipment. Furthermore, the few examples and case studies about utilization of synthetic lubricants in bearings, gears, dental and so forth has been included. Features: explores information on the present and future of sustainable lubricants due to its accelerated demands in industries, provides conceptual overview of lubricant application in manufacturing and automobile industries, discusses lubricants used in the micro-electromechanical systems (MEMS), nano-electromechanical systems (NEMS), tribo-systems under extreme conditions and for biomedical applications, and reviews information about various types of additives and their role in lubricants, and their cost effectiveness. This text also includes case studies related to journal-bearing/gear drive systems. Finally, this shortform book is geared towards students and researchers in mechanical engineering, automobile engineering, chemical engineering and chemistry, manufacturing, mechanical, materials and metallurgy\"-- Provided by publisher.
Book
CT scan pancreatic cancer segmentation and classification using deep learning and the tunicate swarm algorithm
Pancreatic cancer (PC) is a very lethal disease with a low survival rate, making timely and accurate diagnoses critical for successful treatment. PC classification in computed tomography (CT) scans is a vital task that aims to accurately discriminate between tumorous and non-tumorous pancreatic tissues. CT images provide detailed cross-sectional images of the pancreas, which allows oncologists and radiologists to analyse the characteristics and morphology of the tissue. Machine learning (ML) approaches, together with deep learning (DL) algorithms, are commonly explored to improve and automate the performance of PC classification in CT scans. DL algorithms, particularly convolutional neural networks (CNNs), are broadly utilized for medical image analysis tasks, involving segmentation and classification. This study explores the design of a tunicate swarm algorithm with deep learning-based pancreatic cancer segmentation and classification (TSADL-PCSC) technique on CT scans. The purpose of the TSADL-PCSC technique is to design an effectual and accurate model to improve the diagnostic performance of PC. To accomplish this, the TSADL-PCSC technique employs a W-Net segmentation approach to define the affected region on the CT scans. In addition, the TSADL-PCSC technique utilizes the GhostNet feature extractor to create a group of feature vectors. For PC classification, the deep echo state network (DESN) model is applied in this study. Finally, the hyperparameter tuning of the DESN approach occurs utilizing the TSA which assists in attaining improved classification performance. The experimental outcome of the TSADL-PCSC method was tested on a benchmark CT scan database. The obtained outcomes highlighted the significance of the TSADL-PCSC technique over other approaches to PC classification.
Journal Article
EEOA: Cost and Energy Efficient Task Scheduling in a Cloud-Fog Framework
Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.
Journal Article
Kerala's economy : crouching tiger, sacred cows
Papers presented at a conference held in 2005 in Thiruvananthapuram, Kerala, India ; articles with reference to Kerala, India.
Book
Soy isoflavones exert beneficial effects on letrozole-induced rat polycystic ovary syndrome (PCOS) model through anti-androgenic mechanism
Context: Soy is the main source of phytoestrogens, which has long been used as traditional food. One major subtype of phytoestrogens includes isoflavones and they are scientifically validated for their beneficial actions on many hormone-dependent conditions.Objective: The present study examines the effect of soy isoflavones on letrozole-induced polycystic ovary syndrome (PCOS) rat model.Materials and methods: PCOS was induced in Sprague–Dawley rats with of 1 mg/kg letrozole, p.o. once daily for 21 consecutive days. Soy isoflavones (50 and 100 mg/kg) was administered for 14 days after PCOS induction. Physical parameters (body weight, oestrous cycle determination, ovary and uterus weight) metabolic parameters (oral glucose tolerance test, total cholesterol), steroidal hormone profile (testosterone and 17β-oestradiol), steroidogenic enzymes (3β-hydroxy steroid dehydrogenase (HSD) and 17β-HSD), oxidative stress and histopathology of ovary were studied.Results: Soy isoflavones (100 mg/kg) treatment significantly altered the letrozole-induced PCOS symptoms as observed by decreased body weight gain (p < 0.05), percentage diestrous phase (p < 0.001), testosterone (p < 0.001), 3β-HSD (p < 0.01) and 17β-HSD (p < 0.001) enzyme activity and oxidative stress. Histological results reveal that soy isoflavones treatment in PCOS rats resulted in well-developed antral follicles and normal granulosa cell layer in rat ovary.Discussion: Treatment with soy isoflavones exerts beneficial effects in PCOS rats (with decreased aromatase activity) which might be due to their ability to decrease testosterone concentration in the peripheral blood.Conclusion: Analysis of physical, biochemical and histological evidences shows that soy isoflavones may be beneficial in PCOS.
Journal Article