Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification

The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%

Recommendation on the Web Search by Using Co-Occurrence

ABSTRACT: In our day to day, the usage of internet and searching the information should be increases rapidly. Because of this, now a days we have facing the problems like whether the retrieving information would be noise free or not and having many confusions with the usage of keywords to get the exact result. To avoid this problem we are going to propose the concepts called Co-Occurrence and recommendation. These two concepts increases the effectiveness and of the result. By using the recommendation concept we have multiple choices to select the desired thing. The web search increases dramatically [1] user search performance leads to large number of confusions. We examine a general expert search problem: searching experts on the web, where millions of web pages and thousands of names are considered. The two main issues are: Web pages might be of untrustworthy and have more noise; the knowledge evidences spotted in web pages are frequently unclear and ambiguous. The skilled search has been studied in different contexts, e.g., enterprises, academic communities. We propose to influence the huge quantity of co-occurrence information to calculate the significance and status of a person name for a query which is given. So this makes the recommendation system the most important and the trust worthiness of the system will be analyzed in the better way. The personalization will be depended based on the individual user process in the web search mainly worked in E-Commerce application

Multi-Parameter Sensor Based Automation Farming

IOT innovation is used in the development of the Smart Farming Tracking the System. An Arduino Uno, a temperature humidity sensor, soil moisture sensor, water level sensor, water pumps, and DC motors strength this system. If the smart farming tracking system turns on, the sensors find the field’s water level and the soil’s moisture level. If the irrigation water stage falls below the level defined for a specific crop grown in the growing area, the irrigation system is going to start to pump water. The IOT warns concerning current level of water, soil moisture stage, and motor beginning will be shown on the LCD panel of the section. We are able to use the pumps by hand via a webpage. The farmers are additionally getting this data via mobile phone. By hitting a system- provided link, the individual using it may firmly prevent the water’s flow within the field. While carried out, the system will assist landowners to preserve suitable soil water and moisture levels, thus boosting yields with little work. The goal of this article is to identify grow illnesses and reduce losses in money. For picture appeal, we suggested an entirely based on deep learning method. We put the three most common Neural Network Designs to the test: Faster Region-based entirely judgment (SVM)Support Vector Machine Region-based entirely (RF) Random Forest method. The method suggested in the research can correctly detect many types of disease and is capable of dealing in complicated situations. In addition, the method may be expanded to recommend fertilizer according to extent evaluation as well as measurement. artificial intelligence (AI) entirely Machine Learning Response to this the combination the issue is a supervised categorization judgment

Semantic-Based Classification of Toxic Comments Using Ensemble Learning

A social media is rapidly expanding, and its anonymity feature completely supports free speech. Hate speech directed at anyone or any group because of their ethnicity, clan, religion, national or cultural their heritage, sex, disability, gender orientation, or other characteristics is a violation of their authority. Seriously encourages violence or hate crimes and causes social unrest by undermining peace, trustworthiness, and human rights, among other things. Identifying toxic remarks in social media conversation is a critical but difficult job. There are several difficulties in detecting toxic text remarks using a suitable and particular social media dataset and its high-performance, selected classifier. People nowadays share messages not only in person, but also in online settings such as social networking sites and online groups. As a result, all social media sites and apps, as well as all current communities in the digital world, require an identification and prevention system. Finding toxic social media remarks has proven critical for content screening. The identifying blocker in such a system would need to notice any bad online behavior and alert the prophylactic blocker to take appropriate action. The purpose of this research was to assess each text and find various kinds of toxicities such as profanity, threats, name-calling, and identity-based hatred. Jigsaw's designed Wikipedia remark collection is used for this

Video Transcript Summarizer

In today’s world, a large number of videos are uploaded in everyday, which contains information about something. The major challenge is to find the right video and understand the correct content, because there are lot of videos available some videos will contain useless content and even though the perfect content available that content should be required to us. If we not found right one it wastes your full effort and full time to extract the correct usefull information. We propose an innovation idea which uses NLP processing for text extraction and BERT Summarization for Text Summarization. This provides a video main content in text description and abstractive summary, enabling users to discriminate between relevant and irrelevant information according to their needs. Furthermore, our experiments show that the joint model can attain good results with informative, concise, and readable multi-line video description and summary in a human evaluation

Authenticated Sharing of Personal Health Records in Cloud

Personal health record (PHR) is an emerging patient-centric model of health information exchange, which stored at a third party, such as cloud providers. However, there have been wide privacy concerns as personal health information could be exposed to those third party servers and to unauthorized parties. Different from previous works in secure data outsourcing, focus on the multiple data owner scenario, and divide the users in the PHR system into multiple security domains that greatly reduces the key management complexity for owners and users. Proposed a novel patient-centric framework and a suite of mechanisms for data access control to PHRs stored in semi trusted servers. Extensive analytical and experimental results are presented which show the security, scalability, and efficiency

Diagnostic Validity of Orthopantomogram Compared to Dual Energy X-ray Absorptiometry Scan in Detecting Osteoporosis

Introduction Osteoporosis is one of the most common and rampant metabolic bone disorders among the geriatric, particularly affecting postmenopausal women. Even though resorption tends to occur more rapidly in bones with a higher proportion of trabecular bone (e.g., vertebrae, pelvis, calcaneus), bones with significant cortical bone content also do undergo resorption, for example, mandible. The dental manifestations that may indicate low-bone density include loose teeth, receding gums, and ill-fitting or loose dentures. Objective To validate the efficacy of orthopantomograms (OPGs) in recognizing bone mineral density (BMD) changes of the mandible using mandibular cortical index (MCI) and substantiate the same with dual energy X-ray absorptiometry (DEXA) scan on femoral neck and spine. Materials and Methods This cross-sectional study comprised 60 geriatric patients of both genders. All the patients were subjected to panoramic radiographs wherever clinically indicated. The visual analysis was done based on the radiographic appearance of the mandibular cortical border and results were compared with DEXA scan reports, followed by an analysis of three grades of MCI and BMD statistically. Results In our study, out of 40 patients in C2 and C3 subgroups, 67% and 20% were normal, respectively. The incidence of osteopenia was 33% in the C2 group and 70% in the C3 group, whereas Osteoporosis was present only among 10% of the population in the C3 group. The difference between the groups are statistically significant (p = 0.01). These findings imply that a progressive link exists between BMD and deteriorating cortical morphology. Conclusion The purpose of this study is that dentists will be able to refer patients to physicians of suspected low BMD, based on incidental findings on panoramic radiographs for further examination. There is a statistically significant correlation present between DEXA and MCI, so the latter can also be used for screening BMD changes