for providing the fund to carry out the work.

The Tor network is an encrypted network that allows anonymous access to the Internet for its users. The Tor network also hosts hidden services which constitute the infamous dark Web. These hidden services are used to carry out activities that are otherwise illegal and unethical on the surface Web. These activities include distribution of child pornography, access to illegal drugs, and the sale of weapons. While Tor hidden services provide a platform for uncensored ventures and a free expression of thoughts, they are outnumbered by grey activities taking place. In this paper, we have collected the addresses of about 25,742 hidden services and analyze the data for 6,227 available services with the help of custom-made crawler in Python. We analyzed the dataset and manually classify the data into 31 different categories to identify the nature of content available on the dark Web. The results indicate that a large share of hidden services provide illegal content along with a large number of scam sites. Non-English content was also studied and categorized. Russian was the leading language of the dark Web after English and hidden services having forums and blogs were predominantly present over other content.

Over time, textual information on the World Wide Web (WWW) has increased exponentially, leading to potential research in the field of machine learning (ML) and natural language processing (NLP). Sentiment analysis of scientific domain articles is a very trendy and interesting topic nowadays. The main purpose of this research is to facilitate researchers to identify quality research papers based on their sentiment analysis. In this research, sentiment analysis of scientific articles using citation sentences is carried out using an existing constructed annotated corpus. This corpus is consisted of 8736 citation sentences. The noise was removed from data using different data normalization rules in order to clean the data corpus. To perform classification on this data set we developed a system in which six different machine learning algorithms including Naïve-Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbor (KNN) and Random Forest (RF) are implemented. Then the accuracy of the system is evaluated using different evaluation metrics e.g. F-score and Accuracy score. To improve the system' accuracy additional features selection techniques, such as lemmatization, n-graming, tokenization, and stop word removal are applied and found that our system provided significant performance in every case compared to the base system. Our method achieved a maximum of about 9% improved results as compared to the base system.

Introduction:We conducted a prospective study to find out change in limb length (lengthening/shortening) after total knee arthroplsty in unilateral and bilateral total knee arthroplasty (TKA) because few literature is available regarding limb length discrepancy in TKA in comparison to total hip arthroplasty. Limb length discrepancy (LLD) may lead to low back pain and gait abnormalities. Material and methods: We divided 58 patients into two groups: Group A (28 patients) includes patients who underwent unilateral TKA and Group B (30 patients) includes patients who underwent bilateral TKA. We assessed the patients clinico-radiologically in terms of limb length (supine position), deformity, Sagital-flexion deformity/extensor lag, coronal -varus/valgus, range of motion, knee stability, patellar tracking and Hip-knee-ankle angle preoperatively and postoperatively. Results: In group A, mean limb length difference (operated limb gained length as compared to unoperated limb) was 1.11 cm. Out of 22 patients (78%) in whom limb length discrepancy was present, only 7 patients (31%) having limb length discrepancy perceived it. In group B, mean limb length difference was 1.03 cm. Fourteen patients (47%) in group B had LLD but none of them perceived it. Clinically 22 patients (78%) in group A and 14 patients (47%) in group B had LLD. Radiologically 25 patients (89%) in group A and 22 patients (73%) in group B had LLD. Out of the 7 patients who perceived LLD, all had LLD radiologically too. Conclusion:We reckoned that limb length discrepancy is more common in unilateral TKA. Limb length discrepancy of 2 cm or more is perceived by the patients operated for unilateral TKA. But in bilateral TKA, none of the patient perceived LLD because in this group LLD was less than 2 cm. Limb length discrepancy may leads to dissatisfaction of the patients and poor functional outcome.