Alleviation of Delay in Tele-Surgical Operations using Markov Approach based Smith Predictor

The acceptance of tele-robotics and teleoperations through networked control system (NCS) is increasing day-by-day. NCS involves the feedback control loop system wherein the control components such as actuators and sensors are controlled and allowed to share their feedback over real time network with distributed users spread geographically. The performance and surgical complications majorly depend upon time delay, packet dropout and jitter induced in the system. The delay of data packet to the receiving side not only causes instability but also affect the performance of the system. In this article, author designed and simulate the functionality of a model-based Smith predictive controller. The model and randomized error estimations are employed through Markov approach and Kalman techniques. The simulation results show a delay of 49.926ms from master controller to slave controller and 79.497ms of delay from sensor to controller results to a total delay of 129.423ms. This reduced delay improve the surgical accuracy and eliminate the risk factors to criticality of patients’ health.

Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Malware Detection in Android Apps Using Static Analysis

Frequency of malware attacks because Android apps are increasing day by day. Current studies have revealed startling facts about data harvesting incidents, where user’s personal data is at stake. To preserve privacy of users, a permission induced risk interface MalApp to identify privacy violations rising from granting permissions during app installation is proposed. It comprises of multi-fold process that performs static analysis based on app’s category. First, concept of reverse engineering is applied to extract app permissions to construct a Boolean-valued permission matrix. Second, ranking of permissions is done to identify the risky permissions across category. Third, machine learning and ensembling techniques have been incorporated to test the efficacy of the proposed approach on a data set of 404 benign and 409 malicious apps. The empirical studies have identified that our proposed algorithm gives a best case malware detection rate of 98.33%. The highlight of interface is that any app can be classified as benign or malicious even before running it using static analysis.

Fats and oils adulteration: present scenario and rapid detection techniques

Fats and oils are an essential part of everyday cooking as well as food products development and their purity are a concern since very early in human history. Adulteration of fats and oils is increased day by day throughout the world, a greater extent. Hence an attempt has been taken to review to identify different suitable rapid detection techniques for ensuring food quality and safety. The study was designed on the basis of extensive literature review for collecting relevant scientific evidence from various sources like Google Scholar, PubMed/Medline database, Science direct database, published journal, newspaper and periodicals. Various techniques have been utilized to assess the purity of edible fats and oils but yet they are costly and time-consuming. This study summarized the rapid detection techniques so that a common person can perform at the level of the household so as to have a broad picture of the status of adulteration in his food in case of doubt. Edible fats and oils are reported to be adulterated with other low-price oils. For example, mustard oil adulterated with argemone oil and butter yellow and ghee adulterated with vanaspati ghee. The leading challenges include the lack of market survey, lack of acceptance in the wholesale and retail market because of suspecting its purity, community health problem and decrease in consumer confidence.

A CRITICAL REVIEW OF VATIKA HRIDROGA WITH SPECIAL REFERENCE TO ISCHEMIC HEART DISEASE

Given increasing evidence, most deaths are due to non-communicable diseases; half of them are the cardiovascular disease. Hridaya is moolasthana of pranvavaha and rasavaha strotas. According to Acharya Sushruta, any condition that produces disturbance in the heart is Hridroga. It is classified into five types. Vataja Hridroga is characterized by Ruja in Urah Pradesha (Pain in the chest region). Vatika type seems to have conceived the disease entity correlated with ischemic heart disease. None of the other Cardiac afflictions appears to have been described under Hridroga. The prevalence rate in the younger age group is increasing day by day so, we need to know the detailed knowledge of vatika hridroga

Some Topological Approaches for Generalized Rough Sets and Their Decision-Making Applications

The rough set principle was proposed as a methodology to cope with vagueness or uncertainty of data in the information systems. Day by day, this theory has proven its efficiency in handling and modeling many real-life problems. To contribute to this area, we present new topological approaches as a generalization of Pawlak’s theory by using j-adhesion neighborhoods and elucidate the relationship between them and some other types of approximations with the aid of examples. Topologically, we give another generalized rough approximation using near open sets. Also, we generate generalized approximations created from the topological models of j-adhesion approximations. Eventually, we compare the approaches given herein with previous ones to obtain a more affirmative solution for decision-making problems.

Web Blog Content Curation Using Fuzzy-Related Capsule Network-Based Auto Encoder

The internet content increases exponentially day-by-day leading to the pop-up of irrelevant data while searching. Thus, the vast availability of web data requires curation to enhance the results of the search in relevance to searched topics. The proposed F-CapsNet deals with the content curation of web blog data through the novel integration of fuzzy logic with a machine learning algorithm. The input content to be curated is initially pre-processed and seven major features such as sentence position, bigrams, TF-IDF, cosine similarity, sentence length, proper noun score and numeric token are extracted. Then the fuzzy rules are applied to generate the extractive summary. After the extractive curation, the output is passed to the novel capsule network based deep auto-encoder where the abstractive summary is produced. The performance measures such as precision, recall, F1-score, accuracy and specificity are computed and the results are compared with the existing state-of-the-art methods. From the simulations performed, it has been proven that the proposed method for content curation is more efficient than any other method.

Frontline thrombectomy strategy and outcome in acute basilar artery occlusion

BackgroundNovel thrombectomy strategies emanate expeditiously day-by-day counting on access system, clot retriever device, proximity to and integration with the thrombus, and microcatheter disengagement. Nonetheless, the relationship between native thrombectomy strategies and revascularization success remains to be evaluated in basilar artery occlusion (BAO).PurposeTo compare the safety and efficacy profile of key frontline thrombectomy strategies in BAO.MethodsRetrospective analyses of prospectively maintained stroke registries at two comprehensive stroke centers were performed between January 2015 and December 2019. Patients with BAO selected after MR imaging were categorized into three groups based on the frontline thrombectomy strategy (contact aspiration (CA), stent retriever (SR), or combined (SR+CA)). Patients who experienced failure of clot retrieval followed by an interchanging strategy were categorized as a fourth (switch) group. Clinicoradiological features and procedural variables were compared. The primary outcome measure was the rate of complete revascularization (modified Thrombolysis in Cerebral Infarction (mTICI) grade 2c–3). Favorable outcome was defined as a 90 day modified Rankin Scale score of 0–2.ResultsOf 1823 patients, we included 128 (33 underwent CA, 35 SR, 35 SR +CA, and 25 switch techniques). Complete revascularization was achieved in 83/140 (59%) primarily analyzed patients. SR +CA was associated with higher odds of complete revascularization (adjusted OR 3.04, 95% CI 1.077 to 8.593, p=0.04) which was an independent predictor of favorable outcome (adjusted OR 2.73. 95% CI 1.152 to 6.458, p=0.02). No significant differences were observed for symptomatic intracranial hemorrhage, functional outcome, or mortality rate.ConclusionAmong BAO patients, the combined technique effectively contributed to complete revascularization that showed a 90 day favorable outcome with an equivalent complication rate after thrombectomy.

Critical Scam Warning! FuelFix PRO Must Read Before Buying v1

Fuel Fix Pro Reviews: Rising prices of fuel are one of the major concerns of people all around the globe. The amount of fuel is depleting day by day in the world. Governments of various countries are finding new ways to save fuel to a large extent. Every individual must also save fuel for future generations.

A Survey on Comparison of Performance Analysis on a Cloud-Based Big Data Framework

Cloud computing is offering various IT services to many users in the work on the basis of pay-as-you-use model. As the data is increasing day by day, there is a huge requirement for cloud applications that manage such a huge amount of data. Basically, a best solution for analyzing such amounts of data and handles a large dataset. Various companies are providing such framesets for particular applications. A cloud framework is the accruement of different components which is similar to the development tools, various middleware for particular applications and various other database management services that are needed for cloud computing deployment, development and managing the various applications of the cloud. This results in an effective model for scaling such a huge amount of data in dynamically allocated recourses along with solving their complex problems. This article is about the survey on the performance of the big data framework based on a cloud from various endeavors which assists ventures to pick a suitable framework for their work and get a desired outcome.