Karunya University

This paper proposes a new version of the classical particle swarm optimization (PSO), namely, new PSO (NPSO), to solve nonconvex economic dispatch problems. In the classical PSO, the movement of a particle is governed by three behaviors, namely, inertial, cognitive, and social. The cognitive behavior helps the particle to remember its previously visited best position. This paper proposes a split-up in the cognitive behavior. That is, the particle is made to remember its worst position also. This modification helps to explore the search space very effectively. In order to well exploit the promising solution region, a simple local random search (LRS) procedure is integrated with NPSO. The resultant NPSO-LRS algorithm is very effective in solving the nonconvex economic dispatch problems. To validate the proposed NPSO-LRS method, it is applied to three test systems having nonconvex solution spaces, and better results are obtained when compared with previous approaches

Machine learning involves artificial intelligence, and it is used in solving many problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns patterns from the existing dataset, and then applies them to an unknown dataset in order to predict the outcome. Classification is a powerful machine learning technique that is commonly used for prediction. Some classification algorithms predict with satisfactory accuracy, whereas others exhibit a limited accuracy. This paper investigates a method termed ensemble classification, which is used for improving the accuracy of weak algorithms by combining multiple classifiers. Experiments with this tool were performed using a heart disease dataset. A comparative analytical approach was done to determine how the ensemble technique can be applied for improving prediction accuracy in heart disease. The focus of this paper is not only on increasing the accuracy of weak classification algorithms, but also on the implementation of the algorithm with a medical dataset, to show its utility to predict disease at an early stage. The results of the study indicate that ensemble techniques, such as bagging and boosting, are effective in improving the prediction accuracy of weak classifiers, and exhibit satisfactory performance in identifying risk of heart disease. A maximum increase of 7% accuracy for weak classifiers was achieved with the help of ensemble classification. The performance of the process was further enhanced with a feature selection implementation, and the results showed significant improvement in prediction accuracy.

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± standard deviation), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis. The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Covid-19 is a rapidly spreading viral disease that infects not only humans, but animals are also infected because of this disease. The daily life of human beings, their health, and the economy of a country are affected due to this deadly viral disease. Covid-19 is a common spreading disease, and till now, not a single country can prepare a vaccine for COVID-19. A clinical study of COVID-19 infected patients has shown that these types of patients are mostly infected from a lung infection after coming in contact with this disease. Chest x-ray (i.e., radiography) and chest CT are a more effective imaging technique for diagnosing lunge related problems. Still, a substantial chest x-ray is a lower cost process in comparison to chest CT. Deep learning is the most successful technique of machine learning, which provides useful analysis to study a large amount of chest x-ray images that can critically impact on screening of Covid-19. In this work, we have taken the PA view of chest x-ray scans for covid-19 affected patients as well as healthy patients. After cleaning up the images and applying data augmentation, we have used deep learning-based CNN models and compared their performance. We have compared Inception V3, Xception, and ResNeXt models and examined their accuracy. To analyze the model performance, 6432 chest x-ray scans samples have been collected from the Kaggle repository, out of which 5467 were used for training and 965 for validation. In result analysis, the Xception model gives the highest accuracy (i.e., 97.97%) for detecting Chest X-rays images as compared to other models. This work only focuses on possible methods of classifying covid-19 infected patients and does not claim any medical accuracy.

The agricultural sector plays a key role in supplying quality food and makes the greatest contribution to growing economies and populations. Plant disease may cause significant losses in food production and eradicate diversity in species. Early diagnosis of plant diseases using accurate or automatic detection techniques can enhance the quality of food production and minimize economic losses. In recent years, deep learning has brought tremendous improvements in the recognition accuracy of image classification and object detection systems. Hence, in this paper, we utilized convolutional neural network (CNN)-based pre-trained models for efficient plant disease identification. We focused on fine tuning the hyperparameters of popular pre-trained models, such as DenseNet-121, ResNet-50, VGG-16, and Inception V4. The experiments were carried out using the popular PlantVillage dataset, which has 54,305 image samples of different plant disease species in 38 classes. The performance of the model was evaluated through classification accuracy, sensitivity, specificity, and F1 score. A comparative analysis was also performed with similar state-of-the-art studies. The experiments proved that DenseNet-121 achieved 99.81% higher classification accuracy, which was superior to state-of-the-art models.

Dimensionality Reduction (DR) is the pre-processing step to remove redundant features, noisy and irrelevant data, in order to improve learning feature accuracy and reduce the training time. Dimensionality reductions techniques have been proposed and implemented by using feature selection and extraction method. Principal Component Analysis (PCA) one of the Dimensions reduction techniques which give reduced computation time for the learning process. In this paper presents most widely used feature extraction techniques such as EMD, PCA, and feature selection techniques such as correlation, LDA, forward selection have been analyzed based on high performance and accuracy. These techniques are highly applied in Deep Neural Network for medical image diagnosis and used to improve the classification accuracy. Further, we discussed how dimension reduction is made in deep learning.

Internet of Things (IoT) plays a vital role in the field of healthcare. The development of smart sensors, smart devices, advanced lightweight communication protocols made the possibility of interconnecting medical things to monitor biomedical signals and diagnose the diseases of patients without human intervention and termed as Internet of Medical Things (IoMT). This paper portrays an overview of Internet of Medical Things based remote healthcare, tracking ingestible sensors, mobile health, smart hospitals, enhanced chronic disease treatment.

This paper addresses a hybrid solution methodology integrating particle swarm optimization (PSO) algorithm with the sequential quadratic programming (SQP) method for the reserve constrained dynamic economic dispatch problem (RCDEDP) of generating units considering the valve-point effects. The cost function of the generating units exhibits the nonconvex characteristics, as the valve-point effects are modeled and imposed as rectified sinusoid components. The hybrid method incorporates the PSO algorithm as the main optimizer and SQP as the local optimizer to fine-tune the solution region whenever the PSO algorithm discovers a better solution region in the progress of its run. Thus, the SQP guides PSO for better performance in the complex solution space. To validate the feasibility of the proposed method, a ten-unit system is taken and studied under three different load patterns. The effectiveness and computation performance of the proposed method for the RCDEDP of units with valve-point effects is shown in general.

Malaria is an infectious disease caused by single-celled parasite of plasmodium group. The disease is more often spread by an Infected Female Anopheles mosquito. In 2017 alone 219 million cases and nearly 435,000 deaths were reported, with more than 40% of global population at risk. In spite of many advanced evaluation techniques for identifying the infection, microscopists at resource constrained regions face challenge in improving the diagnostic accuracy. Deep learning based classification of cell images prevent the wrong diagnostic decisions. This paper focuses on the implementation of Transfer learning based classification of malarial infected cells to improve the diagnostic accuracy. The experimental results show that transfer learning performs well on microscopic cell-images.

<?Pub Dtl=""?> A wind turbine power curve essentially captures the performance of the wind turbine. The power curve depicts the relationship between the wind speed and output power of the turbine. Modeling of wind turbine power curve aids in performance monitoring of the turbine and also in forecasting of power. This paper presents the development of parametric and nonparametric models of wind turbine power curves. Parametric models of the wind turbine power curve have been developed using four and five parameter logistic expressions. The parameters of these expressions have been solved using advanced algorithms like genetic algorithm (GA), evolutionary programming (EP), particle swarm optimization (PSO), and differential evolution (DE). Nonparametric models have been evolved using algorithms like neural networks, fuzzy c-means clustering, and data mining. The modeling of wind turbine power curve is done using five sets of data; one is a statistically generated set and the others are real-time data sets. The results obtained have been compared using suitable performance metrics and the best method for modeling of the power curve has been obtained.

Blockchain has become popular in recent times through its data integrity and wide scope of applications. It has laid the foundation for cryptocurrencies such as Ripple, Bitcoin, Ethereum, and so on. Blockchain provides a platform for decentralization and trust in various applications such as finance, commerce, IoT, reputation systems, and healthcare. However, prevailing challenges like scalability, resilience, security and privacy are yet to be overcome. Due to rigorous regulatory constraints such as HIPAA, blockchain applications in the healthcare industry usually require more stringent authentication, interoperability, and record sharing requirements. This article presents an extensive study to showcase the significance of blockchain technology from both application and technical perspectives for healthcare domain. The article discusses the features and use-cases of blockchain in different applications along with the healthcare domain interoperability. The detailed working operation of the blockchain and the consensus algorithms are presented in the context of healthcare. An outline of the blockchain architecture, platforms, and classifications are discussed to choose the right platform for healthcare applications. The current state-of-the-art research in healthcare blockchain and available blockchain based healthcare applications are summarized. Furthermore, the challenges and future research opportunities along with the performance evaluation metrics in realizing the blockchain technology for healthcare are presented to provide insight for future research. We also layout the various security attacks on the blockchain protocol with the classifications of threat models and presented a comparative analysis of the detection and protection techniques. Techniques to enhance the security and privacy of the blockchain network is also discussed.

OBJECTIVES: The present investigation was aimed to study the antidiabetic and antihyperlipidemic potential of Abelmoschus esculentus peel and seed powder (AEPP and AESP) in streptozotocin (STZ)-induced diabetic rats. MATERIALS AND METHODS: Acute toxicity of AEPP and AESP was studied in rats at 2000 mg/kg dose and diabetes was induced in rats by administration of STZ (60 mg/kg, i.p.). After 14 days of blood glucose stabilization, diabetic rats received AEPP, AESP, and glibenclamide up to 28 days. The blood samples were collected on day 28 to estimate the hemoglobin (Hb), glycosylated hemoglobin (HbA1c), serum glutamate-pyruvate transferase (SGPT), total protein (TP), and lipid profile levels. RESULTS: In acute toxicity study, AESP and AESP did not show any toxicity or death up to a dose of 2000 mg/kg. Therefore, to assess the antidiabetic action, one by fifth and one by tenth dose of both powders were selected. Administration of AEPP and AESP at 100 and 200 mg/kg dose in diabetic rats showed significant (P < 0.001) reduction in blood glucose level and increase in body weight than diabetic control rats. A significant (P < 0.001) increased level of Hb, TP, and decreased level of HbA1c, SGPT were observed after the treatment of both doses of AEPP and AESP. Also, elevated lipid profile levels returned to near normal in diabetic rats after the administration of AEPP and AESP, 100 and 200 mg/kg dose, compared to diabetic control rats. CONCLUSION: The present study results, first time, support the antidiabetic and antihyperlipidemic potential of A. esculentus peel and seed powder in diabetic rats.

Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.

Abstract This review of MRF (magnetorheological fluids or MR fluids) brings out the challenges in methods of preparation, difficulties encountered in storage and use, and possible solutions to overcome the challenges. Magnetorheological fluid in the rheological fluid domain has found use due to its ability to change its shear strength based on the applied magnetic field. Magnetorheological fluids are composed of magnetizable micron-sized iron particles and a non-magnetizable base or carrier fluid along with additives to counter sedimentation and agglomeration. Magnetorheological fluids can respond to external stimuli by undergoing changes in physical properties thus enabling several improved modifications in the existing technology enhancing their application versatility and utility. Thus, magnetorheological fluid, a rheological material whose viscosity undergoes apparent changes on application of magnetic field, is considered as a smart material. Such materials can be used for active and semi-active control of engineering systems. Many studies on the designs of systems incorporating MR fluids, mainly for vibration control and also for other applications including brakes, clutches, dynamometers, aircraft landing gears, and helicopter lag dampers, have emerged over last couple of decades. However, the preparation as well as the maintenance of magnetorheological fluids involves several challenges. Sedimentation is a major challenge, even when stored for moderate periods of time. A comprehensive review is made on the problems confronted in the preparation of magnetorheological fluids as well as sustenance of the properties, for use, over a long period of time. Other problems encountered include agglomeration and in-use thickening (IUT) as well as rusting and crusting. Of interest is the mitigation of these problems so as to prepare fluids with satisfactory properties, and such solutions are reviewed here. The control of magnetorheological fluids and the applications of interest are also reviewed. The review covers additives for overcoming challenges in the preparation and use of magnetorheological fluids that include incrustation, sedimentation, agglomeration, and also oxidation of the particles. The methodology to prepare the fluid along with the process for adding selected additives was reviewed. The results showed an improvement in the reduction of sedimentation and other problems decreasing comparatively. A set of additives for addressing the specific challenges has been summarized. Experiments were carried out to establish the sedimentation rates for compositions with varying fractions of additives. The review also analyzes briefly the gaps in studies on MR fluids and covers present developments and future application areas such as haptic devices.

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the major nosocomial pathogens responsible for a wide spectrum of infections and the emergence of bacterial resistance to antibiotics has lead to treatment drawbacks towards large number of drugs. Formation of biofilms is the main contributing factor to antibiotic resistance. The development of reliable processes for the synthesis of zinc oxide nanoparticles is an important aspect of nanotechnology today. Zinc oxide and titanium dioxide nanoparticles comprise well-known inhibitory and bactericidal effects. Emergence of antimicrobial resistance by pathogenic bacteria is a major health problem in recent years. This study was designed to determine the efficacy of zinc and titanium dioxide nanoparticles against biofilm producing methicillin-resistant S. aureus. Biofilm production was detected by tissue culture plate method. Out of 30 MRSA isolates, 22 isolates showed strong biofilm production and 2 showed weak and moderate biofilm formation. Two strong and weak biofilm-producing methicillin-resistant S. aureus isolates were subjected to antimicrobial activity using commercially available zinc and titanium dioxide nanoparticles. Thus, the nanoparticles showed considerably good activity against the isolates, and it can be concluded that they may act as promising, antibacterial agents in the coming years.

All-trans retinoic acid (ATRA) is an active metabolite of vitamin A under the family retinoid. Retinoids, through their cognate nuclear receptors, exert potent effects on cell growth, differentiation and apoptosis, and have significant promise for cancer therapy and chemoprevention. Differentiation therapy with ATRA has marked a major advance and become the first choice drug in the treatment of acute promyelocytic leukemia (APL). Conversions of 13-cis-retinoic acid and 9-cis-retinoic acid to all-trans-retinoic acid is very rapid. Currently, two distinct families of retinoid responsive nuclear receptors have been identified and characterized: retinoic acid receptors (RARs) and retinoid receptors (RXRs), each of which include three isoforms, α,β,and γ. ATRA is being increasingly included in anti-tumour therapeutical schemes for the treatment of various tumoral diseases such as Kaposi's sarcoma, head and neck squamous cell carcinoma, ovarian carcinoma, bladder cancer, neuroblastoma and has shown antiangiogenic effects in several systems, inhibiting proliferation in vascular smooth muscle cells (VSMCs) and anti-inflammatory in rheumatoid arthritis. This review helps to understand in details about the ATRA and its role on cancer and it is predicted that modulating the activity of ATRA will soon provide novel prevention and treatment approaches for the cancer patients.

There are a slew of elements at work in the composites sector, from people and markets to technology and innovation, that are continually reshaping the industry's structure. For now, composite materials' winning combination of high strength-to-weight ratio continues to propel them into new areas, but other attributes are just as crucial. These properties, which may be customized for unique purposes, result in a completed product requiring fewer raw materials and fewer joints and fasteners, as well as reduced assembly times, thanks to composite materials. To lower product lifespan costs, composites also have demonstrated resilience in industrial applications to temperature extremes as well as corrosion and wear. Polymers, ceramics, and metals can all be used as matrices. Thermoplastic (TP) resin is the second most prevalent matrix type, and it is becoming increasingly popular among composite makers. By melting or softening and then chilling the material, thermoplastic linear polymer chains are generated and may be reformed into shaped solids. It is common for thermoplastics to be offered in sheet or panel form, which may be treated using in situ consolidation processes, such as pressing, to manufacture durable, near-net-shape components without the need for an autoclave or vacuum bag cure. Correcting abnormalities or fixing harm done in service is possible with reformability.

To realize the potential applications of stretchable sensors in the field of wearable health monitoring, it is essential to develop a stable sensing device with robust electrical and mechanical properties in the present of varying external conditions. Herein, we demonstrate a stretchable temperature sensor with the elimination of strain-induced interference via geometric engineering of the free-standing stretchable fibers (FSSFs) of reduced graphene oxide/polyurethane composite. The FSSFs were formed in serpentine structures and enabled the implementation of a strain-insensitive stretchable temperature sensor. On the basis of the controlled reduction time of graphene oxide, we can modulate the response and thermal index of the device. These results are attributed to the variation in the density of oxygen-containing functional groups in the FSSFs, which affect the hopping charge transport and thermal generation of excess carriers. The FSSF temperature sensor yields increased responsivity (0.8%/°C), stretchability (90%), sensing resolution (0.1 °C), and stability in response to applied stretching (±0.37 °C for strains ranging from 0 to 50%). When the sensor is sewn onto a stretchable bandage and attached to the human body, it can detect the temperature changes of the human skin during different body motions in a continuous and stable manner.

The textile industry generates large volumes of effluents on a daily basis, which contains substantial loads of organic compounds, inorganic salts, and suspended impurities. Membrane filtration has become an essential part of advanced treatment plants for dye wastewater treatment. Prevention of membrane fouling is one of the critical objectives for making the overall treatment process commercially viable. Development in this area during the past decade is critically evaluated in this review. Recent developments in the primary, secondary, and tertiary treatment steps in textile wastewater treatment are outlined. The methods employed for measuring, modeling, and understanding membrane fouling processes are discussed. Specific efforts toward fouling control by (a) pretreatment stages of textile wastewater and (b) modifying and optimizing the membrane separation process parameters such as feed composition, hydrodynamic conditions, and membrane properties are then assessed. Fouling related investigations for microfiltration/ultrafiltration membranes, membrane bioreactors, reverse osmosis, and nanofiltration membranes with special focus on textile wastewater treatment are discussed. Recent efforts toward developing new membranes and cleaning processes for fouled membrane activation are also discussed. Pilot plant studies involving membrane separation in combination with other treatment processes are also summarized. Strategies evolved and experiences gained from the industrial scale textile wastewater treatment plants in India are discussed. The authors also summarize the opportunities and the challenges remaining at different stages of industrial textile wastewater treatment units.

Wireless Sensor Networks (WSNs) are spatially scattered independent sensors to track physical objects or monitor environmental data and collectively transmit the data to master station. WSN is deployed in numerous fields such as animal tracking, precision agriculture, environmental monitoring, security and surveillance, smart buildings, health care and so on. This paper presents various applications of WSN with the intention of disseminating various applications of WSN for the better understanding of the research community to apply WSN in further innovative fields.