Laparoscopic versus conventional open appendicectomy: a prospective comparative analytical study in a tertiary care set up

Background: Present study outlines the outcomes of laparoscopic appendicectomy compared to open conventional appendicectomy in a tertiary care set up with aim to validate advantages and shortcomings of both procedures.Methods: A series of 80 cases above 18 years of age with clinical diagnosis of appendicitis having Alvarado score of seven and above were studied prospectively under the two groups after proper written consent: Open appendectomy-40 cases, Laparoscopic appendectomy-40 cases. Both groups were compared on grounds of intra-operative complications, additional diagnostic potential, operative time, postoperative analgesia, post-operative complications, length of hospital stay, subjective cosmesis, and return to routine normal activities. Values obtained were statistically analyzed.Results: The median operative time in Laparoscopic Appendicectomy was 58.22 minutes (range 32.68-85.46 min) as compared to open procedure which took 43.65 minutes (30.36-65.48min) (P<0.05). Conversion to open procedure was done in 10% (n=4) of laparoscopic cases. Mean value of postoperative pain by visual analogue scale was low in Laparoscopic Appendicectomy (LA) compared to Open Appendicectomy (OA) (P<0.05). Mean post-operative stay (3.2±0.34 days versus 2.3±0.24 days) and surgical site infection was recorded in 10 patients (25%) in OA group and 5 (13.9%) in LA group (P<0.05).Conclusions: It can be concluded that laparoscopic surgery is safe with greater diagnostic potential for additional pathologies and better Subjective cosmesis . But all these merits were at the price of longer operating time and a specialized set up needed for laparoscopy

Academic Institutional Repositories in India: Global Visibility for an Institution's Scholarly Communication

Institutional Repositories; Open Access Digital Repositories; Academic Institutional Repositories; DSpace; E-Print- ArchiveRecent development in information and communication and web technologies has changed the landscape of information handling activities. These technologies provide a simple, web-based mechanism for researchers to deposit (‘self-archive’) and access their research publications via digital repositories. In this paper we describe the genesis, objective, methodology, contents, advantages, software usages, growth and development of institutional repositories in India

Depression Detection Using Stacked Autoencoder from Facial Features and NLP

Depression has become one of the most common mental illnesses in the past decade, affecting millions of patients and their families. However, the methods of diagnosing depression almost exclusively rely on questionnaire-based interviews and clinical judgments of symptom severity, which are highly dependent on doctors’ experience and makes it a labor-intensive work. This research work aims to develop an objective and convenient method to assist depression detection using facial features as well as textual features. Most of the people conceal their depression from everyone. So, an automated system is required that will pick out them who are dealing with depression. In this research, different research work focused for detecting depression are discussed and a hybrid approach is developed for detecting depression using facial as well as textual features. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Natural Language Processing, Stacked Deep Auto Encoder with Random forest (RF) classifier and fuzzy logic based on multi-feature depression detection system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. The result analysis shows that the developed technique significantly advantages over existing methods

A Survey on Vulnerabilities and Performance Evaluation Criteria in Blockchain Technology

The blockchain technology firstly presented by Haber and Stornetta in the year 1990, and first time blockchain technology used in Bitcoin by Satoshi Nakamoto in 2008. The blockchain technology is truly decentralized technology. In blockchain technology, every block has consisted three main parts that is data, hash block, and the previous hash block. Hash is controls the uniqueness of each block and it is unique for each block. Each block also contains the hash of the previous block; thus, the blocks are connected to each other. A blockchain can divided into three categories public blockchain, consortium blockchain and private blockchain. The proposed paper provided the comparative and analytical review on the blockchain consensus algorithms

Edge Enhancement from Low-Light Image by Convolutional Neural Network and Sigmoid Function

Due to camera resolution or any lighting condition, captured image are generally over-exposed or under-exposed conditions. So, there is need of some enhancement techniques that improvise these artifacts from recorded pictures or images. So, the objective of image enhancement and adjustment techniques is to improve the quality and characteristics of an image. In general terms, the enhancement of image distorts the original numerical values of an image. Therefore, it is required to design such enhancement technique that do not compromise with the quality of the image. The optimization of the image extracts the characteristics of the image instead of restoring the degraded image. The improvement of the image involves the degraded image processing and the improvement of its visual aspect. A lot of research has been done to improve the image. Many research works have been done in this field. One among them is deep learning. Most of the existing contrast enhancement methods, adjust the tone curve to correct the contrast of an input image but doesn’t work efficiently due to limited amount of information contained in a single image. In this research, the CNN with edge adjustment is proposed. By applying CNN with Edge adjustment technique, the input low contrast images are capable to adapt according to high quality enhancement. The result analysis shows that the developed technique significantly advantages over existing methods