Internet of Things is a revolutionary approach for future technology enhancement: a review

Abstract Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT. A lot of crucial research studies and investigations have been done in order to enhance the technology through IoT. However, there are still a lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc. The main goal of this review article is to provide a detailed discussion from both technological and social perspective. The article discusses different challenges and key issues of IoT, architecture and important application domains. Also, the article bring into light the existing literature and illustrated their contribution in different aspects of IoT. Moreover, the importance of big data and its analysis with respect to IoT has been discussed. This article would help the readers and researcher to understand the IoT and its applicability to the real world

Comparative study of the smear microscopy, with conventional culture in clinically suspected cases of pulmonary and extra pulmonary tuberculosis

Background: Tuberculosis is a significant cause of morbidity and mortality in the Indian subcontinent. A major challenge to clinical microbiology is the detection of Mycobacterium tuberculosis as accurately as possible. Objective: Tthe most important tool in the diagnosis of tuberculosis is direct microscopic examination of appropriately stained specimens for acid- fast bacilli and the gold standard for diagnosing tuberculosis is MTB convention culture on L-J media So, the present study was undertaken to compare smear microscopy by Z – N staining with conventional culture on L-J media, in cases of clinically suspected Pulmonary Tuberculosis and Extra Pulmonary Tuberculosis. Methods: 279 samples were processed within 24 hours of receipt. Samples from non-sterile sites were subjected to decontamination by the modified Petroff’s method. Sterile samples were directly processed as per conventional methods. Smear microscopy was done by Z- N staining and culture was done on L- J media. A control organism in the form of M. tuberculosis H37Rv was also tested with each batch of clinical isolates. Result: Results of smear microscopy and conventional culture of pulmonary and extra pulmonary specimens were compared. 22 and 14 more cases were detected by culture as compared to smear in case of pulmonary and extra pulmonary specimens respectively.Conclusion: From this study we can state that direct microscopic examination of appropriately stained Pulmonary and Extra Pulmonary specimens for acid fast Bacilli is an important tool in the diagnosis of tuberculosis. The Technique is simple, inexpensive and fast .However many Paucibacillary cases may be missed on smear microscopy. Thus specimens from all suspected cases of Pulmonary and Extrapulmonary Tuberculosis should be subjected to conventional culture on LJ media. This is the Gold Standard for Diagnosing Tuberculosis.

Load Flow Analysis with UPFC under Unsymmetrical Fault Condition

This paper addresses the comparative load flow analysis with and without Unified Power Flow Controller (UPFC) for six buses, three phase transmission line under unsymmetrical faults (L-G, L-L and L-L-G) in simulation model. Unified Power Flow Controller (UPFC) is a typical Flexible AC Transmission System (FACTS) device playing a vital role as a stability aid for large transient disturbances in an interconnected power system. The main objective of this paper is to improve transient stability of the six bus system. Here active and reactive power on load bus of the system considered has been determined under different fault conditions. UPFC has been connected to the system and its effects on power flow and voltage profile of test system has been determined with various line data and bus data for six buses, three lines power system and simulation model by using simulation toolbox has been developed. In this work a versatile model is presented for UPFC inherent order to improve the transient stability and damp oscillation. Index Terms – Unified Power Flow Controller (UPFC), Control, simulation, transients, line to ground fault (L-G), double line to ground fault (L-L-G), double line fault (L-L

ANN based short-term traffic flow forecasting in undivided two lane highway

Abstract Short term traffic forecasting is one of the important fields of study in the transportation domain. Short term traffic forecasting is very useful to develop a more advanced transportation system to control traffic signals and avoid congestions. Several studies have made efforts for short term traffic flow forecasting for divided and undivided highways across the world. However, all these studies relied on the dataset which are greatly varied between countries due to the technology used for transportation data collection. India is a developing country in which efforts are being done to improve the transportation system to avoid congestion and travel time. Two-lane undivided highways with mixed traffic constitute a large portion of Indian road network. This study is an attempt to develop a short term traffic forecasting model using back propagation artificial neural network for two lane undivided highway with mixed traffic conditions in India. The results were compared with random forest, support vector machine, k-nearest neighbor classifier, regression tree and multiple regression models. It was found that back-propagation neural network performs better than other approaches and achieved an R2 value 0.9962, which is a good score

Relationship between Size of Cloud Ice and Lightning in the Tropics

The association of lightning flashes with mean cloud ice size over continental and oceanic region in the tropical areas has been analyzed using the observations from various satellite platforms (MODIS, TRMM, and LIS) for the period 2000–2011. We found that frequency of lightning in general is higher over the continental region compared to oceanic region, whereas larger size of cloud ice is observed over the oceanic regions compared to the continental regions. Relationship between lighting and cloud ice size shows similar features over both continental and oceanic regions. For the first time, we show that total lighting increases with increase in the cloud ice size; attends maximum at certain cloud ice size and then decreases with increase in cloud ice size. Maximum lightning occurred for the mean cloud ice size of around 23–25 µm over the continental region and mean cloud ice size of around 24–28 µm over the oceanic region. Based on our observation we argue that the relation between lightning and mean cloud ice size follow the curve linear pattern, and not linear

Analyzing MRI scans to detect glioblastoma tumor using hybrid deep belief networks

Abstract Glioblastoma (GBM) is a stage 4 malignant tumor in which a large portion of tumor cells are reproducing and dividing at any moment. These tumors are life threatening and may result in partial or complete mental and physical disability. In this study, we have proposed a classification model using hybrid deep belief networks (DBN) to classify magnetic resonance imaging (MRI) for GBM tumor. DBN is composed of stacked restricted Boltzmann machines (RBM). DBN often requires a large number of hidden layers that consists of large number of neurons to learn the best features from the raw image data. Hence, computational and space complexity is high and requires a lot of training time. The proposed approach combines DTW with DBN to improve the efficiency of existing DBN model. The results are validated using several statistical parameters. Statistical validation verifies that the combination of DTW and DBN outperformed the other classifiers in terms of training time, space complexity and classification accuracy

SemEval 2023 Task 6: LegalEval -- Understanding Legal Texts

In populous countries, pending legal cases have been growing exponentially. There is a need for developing NLP-based techniques for processing and automatically understanding legal documents. To promote research in the area of Legal NLP we organized the shared task LegalEval - Understanding Legal Texts at SemEval 2023. LegalEval task has three sub-tasks: Task-A (Rhetorical Roles Labeling) is about automatically structuring legal documents into semantically coherent units, Task-B (Legal Named Entity Recognition) deals with identifying relevant entities in a legal document and Task-C (Court Judgement Prediction with Explanation) explores the possibility of automatically predicting the outcome of a legal case along with providing an explanation for the prediction. In total 26 teams (approx. 100 participants spread across the world) submitted systems paper. In each of the sub-tasks, the proposed systems outperformed the baselines; however, there is a lot of scope for improvement. This paper describes the tasks, and analyzes techniques proposed by various teams.Comment: 13 Pages (9 Pages + References), Accepted at SemEval 202