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In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.

Swasthavritta Palana is the primary prevention mentioned in classics. By the practice of which an individual always lives with healthiness, a physician should instigate for such practice as because good health is the most desirable objects of human beings. This can be achieved by means of preventing diseases, preserving, and promoting health. The Aahar-Vihar which is beneficial and nutritional to the body and also give the happiness to the mind is known as Pathya and opposite to that is known as Apathya. Acharaya Charaka mentions that the people living in Jangala Pradesha are dry and they have dry food that vitiates Vata and produces Vibanda or constipation. Acharaya Susruta describes as Pitta & Vata Dosha predominance in Parikartika. Hence the proper and effective therapy is required for the treatment of anal fissure which is simple, safe and effective, without any complication, avoidance of incontinence of stool and recurrence at end of therapy. Still there is need of evaluation of certain drugs clinically on various scientific parameters which could be safe, effective, cheap & radially available in the management of Parikartika.

Oral illnesses can develop as a result of poor oral hygiene. Kawala process is mentioned in Ayurveda texts to treat oral illnesses. Chaturjaat and its Mukhgandh Har property is mentioned in ‘Karpuradi Varg’ of Bhavprakash Nighantu. Kawala of Chaturjaat made from Hima process is used here to treat Mukh Daurgandhya and to see its effect in antimicrobial study. Two groups of 30 patients each are included in this study. One group was given Chaturjaat while other 30 were given lukewarm water Kawala. It was seen that Chaturjaat Kawala was more effective in subjective criteria of oral hygiene as well as in antimicrobial study.

A denial-of-service (DoS) attack aims to exhaust the resources of the victim by sending attack packets and ultimately stop the legitimate packets by various techniques. The paper discusses the consequences of distributed denial-of-service (DDoS) attacks in various application areas of Internet of Things (IoT). In this paper, we have analyzed the performance of machine learning(ML)-based classifiers including bagging and boosting techniques for the binary classification of attack traffic. For the analysis, we have used the benchmark CICDDoS2019 dataset which deals with DDoS attacks based on User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) in order to study new kinds of attacks. Since these protocols are widely used for communication in IoT networks, this data has been used for studying DDoS attacks in the IoT domain. Since the data is highly unbalanced, class balancing is done using an ensemble sampling approach comprising random under-sampler and ADAptive SYNthetic (ADASYN) oversampling technique. Feature selection is achieved using two methods, i.e., (a) Pearson correlation coefficient and (b) Extra Tree classifier. Further, performance is evaluated for ML classifiers viz. Random Forest (RF), Naïve Bayes (NB), support vector machine (SVM), AdaBoost, eXtreme Gradient Boosting (XGBoost) and Gradient Boosting (GB) algorithms. It is found that RF has given the best performance with the least training and prediction time. Further, it is found that feature selection using extra trees classifier is more efficient as compared to the Pearson correlation coefficient method in terms of total time required in training and prediction for most classifiers. It is found that RF has given best performance with least time along with feature selection using Pearson correlation coefficient in attack detection.

An unusual metabolic condition is Vata-Rakta. The chronic relapsing and intermittent nature of Vata-Rakta illness makes it extremely distressing. The overall prevalence of hyperuricemia is 25.8%, with a larger percentage of men than women having increased serum uric acid. Vatarakta is the name for the illness that is characterised by Raktadhatu aberration brought on by Vata Dosha morbidity. At the current study, Punarnava Amrita Guggulu was taken twice daily with lukewarm water in doses of 1gm (two 500mg tablets each). Results revealed that Vidaha (Burning sensation) (58.33%), Sandhi Shoola (reduced by 65.71%), Sandhi Shotha (reduced by 54.76%) were all statistically very significant outcomes of the current study. Raga (Redness in joints) (60.71%), Toda (Pricking sensation) (57.95%) and Twakvaivarnya (Discoloration) (61.25%). Sparsha Asahyata (Tenderness in joints) (52.47%) Visphota(58.60%) Sandhi Akunchana Prasarana Vedna(63.49%) Sandhi Vikriti (Deformity Status) (47.14%).

Purpose of the study: This study aims to find the need of performance evaluation by assessing probationer’s attitude for performance improvement and correlation between self-scores and evaluator scores. Background: Performance appraisal is a formal assessment tool to regularly review employee’s job performance and overall contribution to the organization. It is a key aspect of the talent management for a hospital to retain its best talent and provide quality care to its patients. A study conducted mix of performance appraisal methods to recognize the best skills of the probationers and holistic view in making best decisions. Design/Methodology/Approach: The study was conducted in the form of questionnaire (i.e., performance evaluation form). Appropriate methods for preparing the evaluation were identified to cover all the aspects of the Probationers’ skills. 46 probationers were identified from 13 departments. Findings were generated with the help of statistical tools such as Karl Pearson coefficient of correlation and MS Excel. Findings& Suggestions: 24 out of 46 participants were designated with permanent position and remaining was given Performance Improvement Plan and next review will be taken for them after 6 months. Also, the moderately positive relation was found between self-scores and evaluators scores.

In Wireless Sensor Network, there are many tiny sensors that plays vital role in various technologies and cluster head selection is an important issues for data transmission. As cluster heads receives data from sensor nodes and sends data after aggregation to the base station. Appropriate cluster heads selection leads to efficient energy consumption and enhances network lifetime. For solving this issue many solutions has been given considering some attributes but with time meta-heuristics algorithms were now widely used for real world applications. Nature inspired algorithm has seeking wide attention of researchers as it gives the capability to self-learn and has better performance. In this paper we have proposed Dragon Fly algorithm which is inspired by dynamic and static behavior of dragon fly. We have first use LEACH for energy model and also compute multi-attributes of sensor nodes using MADM (multi-attributes decision making) accordingly. After this, then the proposed algorithm NDFMA is applied where Dragon fly algorithm and MADM used for optimized selection of cluster heads. The proposed algorithm has been evaluated in terms of numbers of alive nodes, dead nodes, energy consumption, throughput and number of packets sends. NDFMA method suggested that it performs better than other state-of-the art algorithms. The proposed method is way to compute multi-attributes of sensor nodes for ranking them and selecting optimized cluster heads. NDFMA algorithm has been compared with NBA, LEACH and ESO-LEACH which validates that the new proposed algorithm is performing better than the classical and compared algorithm.

Purpose-The purpose of this exploration study was to comprehend the elements influencing referral of patients with kidney disease to nephrologists and furthermore the perception of specialists and general physicians who come across kidney patients in their practice regarding referral. Methodology- This research paper adopts the empirical cross sectional study method incorporating both qualitative and quantitative method of research to collect data from specialists, physicians and patients. Primary data collection using questionnaire and personal interview and extensive review of published literatures was done. SPSS Analysis (Chi-Square and frequency) was used to find the association of factors to referral timing and perception was understood. Findings- The study finds to have positive association (p value < 0.05) of demographic factors with the referral timing patients with kidney disease and dialysis initiation with age, gender, income, family support and education levels showing positive correlation with early referral to a specialist and appropriate initiation of hemodialysis. Consistency in perception regarding different consulting types in patients and care providers can be seen. Differing perception regarding the referral stage and timing of patients, varied guidelines followed by general physicians affect the treatment and referral timing is found.

This study aims to find the need of performance evaluation by assessing probationer’s attitude for performance improvement and correlation between self-scores and evaluator scores. Performance appraisal is a formal assessment tool to regularly review employee’s job performance and overall contribution to the organization. It is a key aspect of the talent management for a hospital to retain its best talent and provide quality care to its patients. A study conducted mix of performance appraisal methods to recognize the best skills of the probationers and holistic view in making best decisions. The study was conducted in the form of questionnaire (i.e., performance evaluation form). Appropriate methods for preparing the evaluation were identified to cover all the aspects of the Probationers’ skills. 46 probationers were identified from 13 departments. Findings were generated with the help of statistical tools such as Karl Pearson coefficient of correlation and MS Excel. 24 out of 46 participants were designated with permanent position and remaining was given Performance Improvement Plan and next review will be taken for them after 6 months. Also, the moderately positive relation was found between self-scores and evaluators scores. The correlation exists but not a perfect correlation.

We model a macromolecule as an infinitely long Gaussian semi-flexible polymer chain and the conformations of the chain were realized in the nano cube using a cubic lattice. A modified version of the recursion relations is used to calculate the grand canonical partition function of the chain to investigate distinct thermo-dynamical properties of the nano polymer aggregate than corresponding bulk behaviour of the macromolecule. Our analytical estimates on the thermodynamical properties of the macromolecule clearly show that the nano aggregate of the polymer chain has interesting and distinct conformational statistics than its corresponding bulk state, and the method described in the present report can be easily extend to investigate the thermodynamics of the selfavoiding polymer chain in the nano dimensions., 08 Pages

We estimate the polymerization probability of a macromolecule, where the macromolecule is made of distinct monomers; and there are different values of the fugacity for the addition of the monomers in the chain to form an infinitely long linear macromolecule of distinct monomers. The lattice model of the random walk has been used to mimic the conformations of an ideal chain in two and three dimensions, and this ideal chain is the macromolecules of distinct monomers. It has been shown through analytical estimates that the flexible macromolecules may be easily formed than the stiff macromolecules for both two and three dimensional cases. In the case of stiff chain, the ratio of the critical value of monomer fugacity is nothing but the log-log ratio of the Boltzmann’s weight corresponding to the monomers affinity corresponding to its conjugate monomer pair; and it is due to a fact that the stiff chain has small value of the entropy.

The epidemic situation may cause severe social and economic impacts on a country. So, there is a need for a trustworthy prediction model that can offer better prediction results. The forecasting result will help in making the prevention policies and remedial action in time, and thus, we can reduce the overall social and economic impacts on the country. This article introduces a CNN-LSTM hybrid deep learning prediction model, which can correctly forecast the COVID-19 epidemic across India. The proposed model uses convolutional layers, to extract meaningful information and learn from a given time series dataset. It is also enriched with the LSTM layer's capability, which means it can identify long-term and short-term dependencies. The experimental evaluation has been performed to gauge the performance and suitability of our proposed model among the other well-established time series forecasting models. From the empirical analysis, it is also clear that the use of extra convolutional layers with the LSTM layer may increase the forecasting model's performance. Apart from this, the deep insides of the current situation of medical resource availability across India have been discussed.

In Urticaria Vata and Kapha are two doshas that are primarily vitiated and being mixed with pitta (Pittena Saha Sambhooya) spreads internally and Externally (Bahir Aantah Visarpah) and results in to ‘Shittapitta-Udarda-Ko- tha’1. Due to Pitta dosha they create redness, swelling on the skin and moderate to severe itching on the skin. Ayurveda Herbo minerals preparation cures urticaria by balancing Tridoshas in the body. Shittapitta manifests due to exposure to various poisonous materials (allergens), cold environment and intake of Asatmya Ahar Vihar. In modern science, it is compared with urticaria. Hives (urticaria) are red, itchy welts that occur when the skin meets allergen releases histamine and other chemicals. The welts vary in size, appear and then disappear repeatedly as the reaction runs its course. The condition is considered chronic hives if the welts appear for more than six weeks and recur frequently over months or years. Episodes of urticaria may relapse for days, weeks, months or years. In Ayur- veda Herbo-minerals preparation effectively treat Shittapitta and restore healthy skin by balancing Tridosha and Sapta dhatu in the body. Keywords Urticaria, Shittapitta, Dosha, Tridosha, hives

Hypertension is the most common lifestyle disorder today. Systemic arterial hypertension is that the most important modifiable risk factor for all-cause morbidity and mortality. Worldwide one of the major causes of premature death is hypertension and can lead to major health consequences, such as CHD, CHF, peripheral arterial disease, stroke, renal failure, and death. Hypertension may be a chronic and sometimes asymptomatic medical condition during which systemic blood pressure is elevated beyond normal. Fewer than half of those with hypertension are conscious of their condition and lots of others are Modern treatment modalities are effective for the management of hypertension but pose human beings to their unwanted complications on their long-term use. The conventional antihypertensive drugs have many adverse effects & are not well tolerated which led to non-compliance, switching & discontinuation of treatment. There is no direct description of HTN in Ayurveda but based on its clinical presentation and similarity between pathogenesis factors for hypertension can be correlated with Raktagata Vata and it is considered as Tridoshaja Vyadhi. Treatment for hypertension in Ayurveda on the idea of Balancing all three Doshas. So Nidan Parivarajan, proper dietary habits, Yoga, Meditation along Ayurvedic herbs can be beneficial for balancing the state of mind, maintaining as well asreducing blood pressure. The Ayurvedic drug's potency depends upon rasa, Guna, Virya, Vipaka & Prabhava. Also, Acharyas have mentioned this as Prabhava of the drug. Ayurveda has various classical formulations and single herbs like Brahmi, Pushkarmoola, Jyotishamati, Sarpagandha, Saunf, Jatamansi etc. having Hridya, Kaphahara, Vatahara, Balya, Raktaprasadana, etc. properties which are safe and cost-effective too. Keywords: High Blood Pressure, Lifestyle disorders, Raktachapa, Silent killer.

Since ancient times Ashmari is mentioned in Ayurvedic texts. Ashmari is a disease related to the urinary system (Mutravahasrotas). Mutrashmari is described under Asthamahagad (difficult to cure) in Ayurveda. It possesses symptoms such as Jwara (Fever), Basti Pida (Discomfort and pain in the bladder), Aruchi (Anorexia), Mutrakric- cha (Difficulty in micturition), Bastishira Vedana (Pain in Urethra), Mushka Vedana (Pain in testicles) and Shepha Vedana (Pain in the penis) have been described in Ayurvedic classical texts. A 29-year-old female patient visited OPD, with complaints of pain in the right side of the Abdomen, Burning Micturition, Nausea and Vomit- ing. The USG reported calculus measuring 5.3mm in size seen in the right kidney with the normal functioning of vitals. The patient was treated with Chandraprabha Vati 2 BD, Gokshuradi Guggulu 2 BD, Varunadi Kwath 20ml BD on OPD basis. The patient was advised initially follow up after 7 days and later after every 15 days. Along with medication, the patient was also advised to follow diet and lifestyle restrictions. The patient got relief in signs and symptoms and the USG report showed no evidence of calculi in the right kidney. Keywords: Vrikashmari, Chandraprabha Vati, Gokshuradi guggulu, Varunadi Kwath

Amlapitta is the most typical illness in the present era. In a rapidly growing civilization, Amlapitta is the outer- most common disorder within the current society, because of indulgence in incompatible food habits and activi- ties. Materialistic lifestyle provokes people to run behind a busy, tremendous stressful life which is the least con- cern towards proper food habits. "Hurry", “Worry” & “Curry” are the main causes of Amlapitta disease. The us- age of synthetic drugs like H2 receptor blockers & proton pump inhibitors has decreased due to their side effects. The signs and symptoms of Amlapitta are very comparable to Gastritis or Hyperacidity. The ruling features of Amlapitta are Avipaka (indigestion), Urodaha (Burning in the chest), Utklesha (Nausea) Aruchi (Anorexia) and Tikta-Amlodgara (sour and bitter belching) have been described in Ayurvedic classical texts. A 42-year-old male patient came in OPD with the following complaints Tikta-Amlodgara, Urodaha, Utklesha, Aruchi, Praseka. Dur- ing Examination, the vitals are normal. The patient was treated with Sutshekar rasa 1 tab. BD, Avipattikar chura- na 3gm BD, Drakshavaleha 5gm BD on the basis. The patient was advised to follow up every 15th day. Along with medication, the patient was also advised to follow diet and lifestyle restrictions. Keywords: Agni, Amlapitta, Annavahasrotas, Hyperacidity.

Gallbladder carcinoma (GBC) has a dismal prognosis even after curative resection. The objective of the study was to evaluate the effect of adjuvant chemotherapy in patients with GBC undergoing curative resection in a randomized control trial (RCT). A single-center open-labeled prospective RCT was done from January 2012 to June 2018. R0 curative resected GBC patients were randomized in 1:1 to either surveillance alone (control group) or adjuvant chemotherapy (gemcitabine and cisplatin (GemCis group)) for 6 cycles. The primary outcome was disease-free survival (DFS), and the secondary outcomes were overall survival (OS) and toxicity profile. On the evaluation of 362 patients with GBC, 50 patients were enrolled in each control or GemCis group. Per protocol (PP), it comprised 96 patients. The demographic and clinical profile was similar between the two groups except in the lower nodal stage where patients were higher in the control group (p = 0.01). Recurrences were similar between groups (control 44% vs GemCis 56%; p = 0.23). On the intention to treat (ITT), analyses of median DFS (not reached vs. 24 months, p = 0.14) and OS (not reached vs. 31 months, p = 0.10) were similar between groups. On PP, analyses of median DFS (not reached vs. 24 months, p = 0.16) and OS (not reached vs. 31 months, p = 0.09) were similar between groups. The common toxicity profile was hematological followed by gastrointestinal symptoms. Adjuvant GemCis therapy for 6 cycles does not improve DFS or OS than R0 surgery alone patients with GBC. NCT02778308 ( https://www.clinicaltrials.gov )

Healthcare systems around the world are facing huge challenges in responding to trends of the rise of chronic diseases. The objective of our research study is the adaptation of Data Science and its approaches for prediction of various diseases in early stages. In this study we review latest proposed approaches with few limitations and their possible solutions for future work. This study also shows importance of finding significant features that improves results proposed by existing methodologies. This work aimed to build classification models such as Naive Bayes, Logistic Regression, k-Nearest neighbor, Support vector machine, Decision tree, Random Forest, Artificial neural network, Adaboost, XGBoost and Gradient boosting. The experimental study chooses group of features by means of three feature selection approaches such as Correlation-based selection, Information Gain based selection and Sequential feature selection. Various Machine learning classifiers are applied on these feature subsets and based on their performance best feature subset is selected. Finally, ensemble based Max Voting Classifier is proposed on top of three best performing models. The proposed model produces an enhanced performance label with accuracy score of 99.41%.

This paper examined the impact of the lockdown and opening-up of India during the COVID-19 pandemic on eleven Indian stock market indices. Using an event study methodology, we found that the lockdown has adversely affected financial services and consumer durables. We show the pharmaceutical sectors exploited the pandemic-induced panic and created artificial demand for medicine. Therefore, policy makers need to be vigilant regarding prices of pharmaceutical products during the pandemic.

Living beings are subjected to many hazards during their course of life. Owing to high mortality rate, heart disease (HD) is among leading hazards for living being. It is world’s one of the critical disease due to its complex diagnosis and expansive treatment. It has predominantly affected the health care sector of developing as well as developed countries. Inadequate preventive measures, diagnosis shortcomings, inefficient medical support, lack of medical staff and advancements have led to severe impacts on developing countries. The paper exhibits state-of-the-art of various intelligent solutions for HD detection with an empirical analysis of machine learning algorithms on electrocardiogram-based arrhythmia dataset for disease detection. A critical investigation is being performed using eight machine learning algorithms, Support Vector Machine, K-Nearest Neighbors, Random Forest, Extra Tree, Bagging, Decision Tree, Linear Regression, and Adaptive Boosting, under imbalanced and balanced class paradigms. The performance of these algorithms is tested with four metrics namely, precision, recall, accuracy, and f1-score. The empirical analysis presents an interesting insight on the structure of dataset. Initially for binary class balancing problem majority class have more accuracy than the minority class because model’s training dataset is crowded with majority class tuples than minority class. The paper uses Synthetic Minority Over-sampling Technique for data balancing. It has not only increased the overall accuracy of the algorithm but also the individual accuracy of the classes. Hence, the accuracy of the minority class will not be sacrificed.

In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

The electroencephalography (EEG) signal is an essential source of Brain–Computer Interface (BCI) technology implementation. The BCI is nothing but a non-muscle communication medium among the external devices and the brain. The basic concept of BCI is to enable the interaction among the neurological ill patients to others with the help of brain signals. EEG signal classification is an essential requirement for various applications such as motor imagery classification, drug effects diagnosis, emotion classification, seizure prediction/detection, eye state prediction/detection, and so on. Thus, there is a need for an efficient classification model that can deal with the EEG datasets more adequately with better classification accuracy, which will further help in developing the automatic solution for the medical domain. In this paper, we have introduced a hybrid classification model for eye state detection using electroencephalogram (EEG) signals. This hybrid classification model has been evaluated with the other traditional machine learning models, eight classification models (Prepossessed + Hypertuned) and six state-of-the-art methods to assess its appropriateness and correctness. This proposed classification model establishes a machine learning-based hybrid model for the classification of eye state using EEG signals with greater exactness. It is also capable of solving the issue of outlier detection and removal to address the class imbalance problem, which will offer the solution toward building the robotic or smart machine-based solution for social well-being.

In the last couple of years, massive growth has been seen in the Internet of Things (IoT). It is being adopted by all the application areas worldwide and playing a vital role in making smart enviro...