Volume 10 | Issue 5 |

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AI: Artificial intelligence CT: Computed tomography DXA: Dual X-ray absorptiometry SKIMS: Sher-i-Kashmir Institute of Medical Sciences Srinagar S.M.H.S: Shri Maharaja Hari Singh Srinagar MRI: Magnetic resonance imaging PET: Positron emission tomography US: Ultrasound

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Sentiment Analysis of the comment textual content from the social media is beneficial for understanding the general public opinion on the product review. The core of sentiment analysis is the text classification task, and distinct words have different contributions to classification. The classification provides that the product is positive or negative primarily based on the comment text provided by the customers of the product. Our proposed system uses the conventional TF-IDF algorithm and generates weighted word vectors. The weighted term vectors are given as input to the BiLSTM to capture the context information effectively. The sentiment is positive or negative of the comment is obtained by feed forward neural network classifier. The system will be tested with the comment text collected by the customer product review from social media, e-commerce site, and the result indicates that the product has the positive or negative reviews.

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Sentiment Analysis; BiLSTM; Machine Learning Algorithms; Product Reviews.

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The exposure or emergence of digitalisation has not only affected our daily life but it has affected legal aspects and legal philosophy also. It has become quite difficult to connect with people without the help of technology because technology is playing a major role in our daily lives. Everyone is depending upon technology for work, entertainment, connecting with different people. It is playing a vital role in the field of law. The researcher shall will try to define types of electronic evidence. Also a close look on guidelines of Legality of Electronic evidence shall be drawn along with challenges that Electronic Evidence faces. An elaborative look shall be given along several Case Laws in Relation to electronic evidence. And finally the necessity and importance of Electronic evidence shall be explained briefly.

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Challenges to the Admissibility of the Electronic Evidence

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This study has been undertaken to investigate the toxicity of wastewater generated by automobile service stations in the Nagpur city. Exhausted engine oil wastewater was collected from some significant automobile service stations of Nagpur city during survey and combined homogenous wastewater was used for the toxicity testing experiments. Poly aromatic hydrocarbons (PAHs), Poly Chlorinated Biphenyls (PCBs), Heavy metals, detergents and other toxicants were tested. Poly aromatic hydrocarbons were detected in this wastewater such as Naphthalene 32?g/l, Fluorene 3.29?g/l, Phenanthrene 20.09?g/l, Anthracene 10.26?g/l, Fluoranthene 2.33?g/l, Pyrene 21.65?g/l, Benz(a) anthracene 1.69?g/l and Benzo(a)pyrene 2.05?g/l. Copper, Cobalt, Iron, Cadmium, Nickel, Zinc and Manganese were 0.279mg/l, 0.069mg/l, 26.047mg/l, 0.021mg/l, 0.04mg/l, 0.530mg/l and 1.344mg/l respectively detected. Wastewater pH was 7.45. Values of NO3?, COD, Oil and Grease in this wastewater generated were reported more than permissible limit of effluent standard. Study also reveals that most of these service stations are lacking basic infrastructure facilities for vehicular washing and subsequent wastewater treatment. Only eleven service stations were found with systematic effluent treatment plants (ETPs). It is imperative to use effective technologies to reduce the water consumption, wastewater generation and to reuse the wastewater after vehicular servicing.

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Automobile service stations, Exhausted engine oil, Effluent treatment plants (ETPs).

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The details of South Dinajpur and the language of women

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The details of South Dinajpur and the language of women

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Machine learning models are used to solve different nonlinear and complex problems. This paper presents three machine learning approaches to design and develop an intelligent rapid battery charger system. Our intelligent rapid battery system can decide the charging current using two inputs i.e. temperature and temperature gradient. For design purposes, we developed Artificial Neural Networks (ANN), K-Nearest Neighbor (KNN) and Multi-Regression based machine learning models for a rapid battery charger system. We trained all three machine learning models in python. For results, we observe that the performance of ANN outperforms KNN and multi-regression machine learning models.

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Machine Learning, ANN, KNN, Multi-Regression

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The book traces the history and development of a mutual organization in financial sector called SWIFT, the society for worldwide interbank financial Telecommunication. Over the last 40 years, SWIFT has served the financial the financial services sector as proprietary communication platform, provided of product and services, standards developer, and conference organizers. Founded to create efficiencies by replacing telegram and telex for international payments, SWIFT now forms a core part of the financial services infrastructure. It is widely regarded as the most secure trusted third-party network in the world serving 212 countries and 10,000 banking organization, securities institution and corporate customers. Through of every phases of its development, SWIFT has maintained the status of industry cooperative, thus presenting an opportunity to study broader themes of globalization.The book traces the history and development of a mutual organization in financial sector called SWIFT, the society for worldwide interbank financial Telecommunication. Over the last 40 years, SWIFT has served the financial the financial services sector as proprietary communication platform, provided of product and services, standards developer, and conference organizers. Founded to create efficiencies by replacing telegram and telex for international payments, SWIFT now forms a core part of the financial services infrastructure. It is widely regarded as the most secure trusted third-party network in the world serving 212 countries and 10,000 banking organization, securities institution and corporate customers. Through of every phases of its development, SWIFT has maintained the status of industry cooperative, thus presenting an opportunity to study broader themes of globalization.

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Research Methodology, Objectives

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In this paper a Corona shape metamaterial antenna consisting of rectangular Split Ring Resonator(SRR) is proposed. The Left Handedness of the unit cell is verified and investigated.The antenna is designed using HFSS software and it is etched on Rogers RT/Duroid 5880 substrate having a thickness of 1.6 mm and the overall dimension of the antenna is 10 x 10 x 1.6 mm3.The antenna achieves frequency band of 25.97 to 27.27 GHz with a return loss of -10 dB and voltage standing wave ratio of less than 2 with a maximum gain and directivity of 5.8dBi and 2.56dB respectively. The efficiency of the proposed antenna is 93.3% at the frequency of 26 GHz which is considerable higher. The proposed metamaterial antenna is better miniaturized, and have frequencies which mainly find applications in 5G applications.

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metamaterial, split ring resonator, HFSS software

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The recent COVID-19 virus outbreak has caused a global health crisis. According to the World Health Organization (WHO), one of the effective protection measures is to wear a face mask in public areas. The COVID-19 pandemic enforced the governments across the world to impose lockdowns to prevent virus transmission. Reports indicate that wearing facemasks at workplace clearly reduces the risk of virus transmission. Therefore, we built a face mask detector with computer vision using Python, OpenCV, Tensor Flow and Keras. In our proposed system we use live video streaming. We train the system to detect the faces of the registered people at a particular workplace. The main aim here is to identify whether a person on the live video stream is wearing a face mask or not. If it detects a person who is not wearing a mask, then it sends an alert message stating the same. This method attains an accuracy of up to 95.77%. We use the mobile net model to detect the presence of masks correctly without causing over-fitting.

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Face mask; Covid-19; deep learning; Keras; OpenCV.

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition

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