Reviewerly/exHarmony huggingface dataset:
An Analysis on Modelling of Kannada Sign language Translator

Data transfer via a media to engage with one another's expressions is called communication. Vocal discourse discussion is the most typical kind of communication. The primary means of communication are restricted to people with vocal impairments. Such people are advised to use sign language, which is a variety of suggested communication methods. For people who cannot speak, sign language is their major form of communication. This language utilizes various images, including finger signs, articulations, and a mix of the two, to communicate data. This framework offers an original technique for deciphering sign activity examination, recognition, and age of a composed depiction in Kannada language utilizing portable applications. Preparing and testing are two significant cycles that are utilized. Every space in the preparation set has 5 video tests, and every video test has a class of words related with it that will be put away in a data set. Pre-processing on the test is finished utilizing the middle channel, the astute administrator for edge location, and the Hoard for include extraction. The text portrayal will at last be delivered in Kannada. The commonplace calculation time is the briefest conceivable and approves the presentation productivity over the customary model with an adequate recognition rate.
Medical Insurance Fraud Detection Based on Block Chain and Machine Learning Approach

With the significant rise in medical costs, the Health Insurance Department's duty of controlling medical expenses has become increasingly vital. Traditional medical insurance settlements are paid per-service, which results in a lot of unnecessary costs. Now a day, the single-disease payment mechanism has been frequently employed to address this issue. However, there is a possibility of fraud with single-disease payments. In this work, the authors have presented a methodology for detecting the health insurance fraud entrenched block chain and Machine learning techniques like Support Vector Machine (SVM) and logistic Regression, that can automatically recognize apprehensive medical records to assure sustainable execution of single-disease payment and reduce medical insurance worker's workload. The authors have also proposed a medical record storage and management procedure based on consortium block chain to assure data security, immutability, traceability, and audit ability. The suggested system may effectively identify fraud and considerably increase the efficiency of medical insurance evaluations, as demonstrated by experiments on two real datasets from two 3A hospitals.
An Exploration on Visualization of Data Utilizing Tableau

The most common technique of analyzing data assembled to draw conclusions concerning the knowledge they contain, a lot of} with the utilization of specialized frameworks and programmes, is data examination. Researchers and professionals often use data analysis techniques and innovations to validate or, on the opposite hand, refute logical models, enabling organizations to form higher business decisions. Information analysis is turning into more and more popular in several fields, together with healthcare. Not solely will illustration play an enormous role in naturally displaying the results of knowledge analysis, however additionally throughout the whole method of collecting, cleaning, Associate in Nursing analyzing, and sharing information. this text outlines an approach for victimization Tableau as a business insight tool to represent and analyses aid data intelligently. Strategies: beginning with making the Tableau Work Space Individual ability 10.3, this analysis illustrates the foremost prevailing model-based technique of comprehending and visualizing Coronavirus data.
Tech-It-Easy : Survey on Applications For Physically Impaired People Using Deep Learning

Physical impairment is and will always be an impediment to daily progression. It is a cruel reality that many tend to ignore. This paper deals with how technology can be used to help people with certain physical/sensory impairments go about their daily lives. Sign language is an important form of communication not only for the people with auditory impairment, but also a tool for those with speech impairment. Sign language is often only understood by experts. Therefore, a sign language interpretation tool is necessary. Whereas, the visually impaired use a system of reading and writing called braille. However, braille is not common among those who are visually acute and it is a hassle to convert every text to braille manually. Our paper explores these two problems. The technology that can change the traditional communication system for the visually impaired people is converting text to audio. It is easy to convert printed text to audio, and it has been successfully done in the past decade. Apart from sign language interpreters, a fairly novel idea is to convert speech to sign language to help impaired people to gain more knowledge and experience. The above-mentioned technologies are expected to help greatly in the normal life of the physically impaired and this project can be further modified to fit any appropriate smart thing.
A Symmetric Encryption Scheme With Multi User Searchable Options in the Cloud Based Applications

Scientists in academia and business have thoroughly studied Encryption Scheme (SE). Whereas many scientific SE methods provide verifiable confidentiality, in order to attain high performance, they typically reveal certain “trans (such as search and access patterns). Yet, a number of infer methods have taken advantage of this leaking, for instance, a query recovering attack that uses foreknowledge to convert transparent question passageways to their matching keywords. But at the other hand, several more pro - posed SE schemes require substantial modification applications, that also tends to make them less convenient, weak in user experience, and complicated to deploy. In this research, we offer IDCrypt, a safe and useful Is the Enclosure (SSE) scheme with demonstrable security power for azure. IDCrypt uses cryptographic techniques to increase SE's security level and search efficiency. We also highlight the key difficulties in safely sharing decrypted data across different individuals and search across several indexes. We suggest a coin search strategy to maintain the search features across several indexes or a key sharing system that combines Personality Crypto (IBE) with Community Encrypted data to solve the aforementioned problems (PKE). Our empirical findings demonstrate that the number of key agreement's expense is rather modest.
Challenges In Cloud Anomaly Detection Using Machine Learning Approaches

Cloud computing is one of the fastest-growing technologies in the present technological world. Massive amounts of transactions, data and the hidden infrastructure of cloud computing systems pose many challenges to the research community. Cloud network security has become an important task in which anomaly detection plays an important role. Machine learning anomaly detection is a special research topic today because cloud services are exponentially growing. in various industries. As the amount of data in the cloud grows, it becomes very difficult or impossible to process it in a timely and error-free manner using only traditional mathematical approaches. Additionally, a vast amount of unstructured data of all types and formats are collected and stored in the cloud, including images, videos, and audio. Anomaly detection is a process to identify suspicious data items in both normal and unexpected events. With new and unknown attacks occurring every day, one must need an approach that can identify unknown behavior in cloud data. This paper provides an overview of setting up an anomaly detection system and issues to consider when collecting data, deploying models, and implementing suitable machine learning techniques. Therefore, this paper focuses on several future research directions that can improve the detection of cloud anomalies.
Construction of Domain Ontology Considering Organic Fertilizers for A Sustainable Agriculture

Agriculture being a prominent occupation of India with its contribution to GDP, effective, prominent technological methodology needs to be adopted for the process of agriculture to be more sustainable and eco-friendlier. Adoption and awareness towards organic fertilizers is a key concept in sustainable agriculture. Agriculture is the most dynamic field with multi-dimensional factors involving in the procedure. To handle the factors involved in the cultivation of a crop a knowledge base with organic fertilizers information and its availability is much needed information for the farmers in adopting them. To achieve this, we built a domain ontology for agriculture by considering factors like soil information, organic fertilizers, diseases, geographical information, crop demand, fertilizers availability. A semantic web helps in serving the ontology to the farmers.
Sentence Patterns based Sarcasm Detection and Classification using Long Short Term Memory Model

Nowadays, an individual's opinion or views are expressed on social media. These opinions could be sarcastic or ironic, which leads to the rise of various sentiments. Most organizations aim to leverage these views in order to improve their business. This gave rise to sentimental analysis. This paper focuses on how sarcasm can be classified using sentence structures. Sentences are extracted from social media tweets. The sentences are divided into three categories: simple, complex, and mixed. A Long-Short Term Memory (LSTM), along with GLoVe embeddings, is used to classify the sentences as sarcastic or non-sarcastic
Breast Cancer Detection based on Deep Neural Network using Multi-Model Features

The disease that causes more threat in a woman's life is breast cancer. Breast cancer is one of the most common types of cancer. Early detection of this type of cancer is crucial for the positive outcome of the treatment process. According to the latest study, India has recorded only a 66.6% survival rate. Various kinds of screening methods for the detection of breast cancer are available. The paper discusses the Deep Neural Network approach of breast cancer detection extracting both shape and texture features. The fused features are applied on the Wisconsin breast cancer dataset for experimentation to differentiate benign and malignant tumours. The approach used multi-model features to classify benign and malignant tumours. The performance of the experimental results is promising in classifying benign and malignant cells.
Sarcasm Detection in News Headlines using Supervised Learning

Nowadays, social media has an enormous amount of news content with a sarcastic message. It is often expressed in the form of verbal and non-verbal. In this paper, the authors aim to identify sarcasm in news headlines using supervised learning. We address this task with the Bag-of-words features, context-independent features, and context-dependent features. Specifically, the authors employ seven supervised learning models, namely, Naïve Bayes-support vector machine, logistic regression, bidirectional gated recurrent units, Bidirectional encoders representation from Transformers (BERT), DistilBERT, and RoBERTa. Our experimental results indicate that RoBERTa achieves a better performance than others.
Detecting Scene Text with Principal Component Analysis Enhanced Image Gradient Auto Encoding

Text is rich in information. Scene text detection is still a challenging problem of machine vision due to variations such as script, font, color, scale, lighting, angle of view and other distortions present in the scene. Scene text reading generally requires high-performance computation platform, large training dataset and longer training process. We have attempted to train our auto encoder based text detector to precisely localize text with minimum training on a small dataset and limited computational resources. The idea involves computation of principal component analysis of image, morphological gradient to enhance text on the scene image and to feed it to a gradient auto encoder neural network to locate possible text components. Scripts belonging to multiple languages can be detect by the proposed detector and it is fairly robust against the variations such as color, lighting, scale, orientation and font. The proposed method is trained with only 167 training images of MRRC dataset. Experiments show that the method achieves an F-measure of 0.76 and 0.77 on MRRC dataset and MSRA-TD500 dataset respectively.
An Adaptive Recommender System Framework for Personalized E-Learning

Due to the gigantic amounts of online learning stuff, e-learning recommender systems are becoming very popular as means of delivering quality content. One of the most common e-learning challenges is to recommend quality of study content to the students. Students have access to a plethora of content or videos for learning various topics, but keeping track of what is relevant to them is a time-consuming task. The present paper focuses on using various recommendation systems and data analytics for personalized learning in an e-learning domain. The paper proposes an adaptive framework for personalized e-learning that solves the problem of previous literature like cold start problem, sparsity problem, over specialization problem etc. The paper also summarized the concepts of eLearning, recommendation systems and data analytics.
Data Privacy through Zero-Knowledge Proofs

With a growing reliance on the Internet, users are required to provide vast amounts of personal information in exchange for using online services. Once given over, the user releases all control and ownership of her/his information. Some users of the Internet rely on strong server-side protections and the assurances that the data will be used ethically in their best interests. Recent legislation actions have focused on limiting the amount of data required and regulating the usage of that data once collected. However, a new subset of cryptography, zero-knowledge proofs, may eliminate the need for users to relinquish their data. These zero-knowledge proofs combined with Blockchain technology could give users complete ownership of their information and provide them with the ability to revoke a third-parties access to it. This survey report is a review the current body of literature relating to this topic with the goal of understanding the feasibility of this solution.
Migraine Prediction Using Deep Learning Model

A migraine is a headache which could reason very terrible palpitate ache, commonly on one part of the head. Symptoms of migraine are nausea, vomiting, and intense sensitivity to brightness and sound. Migraine attacks that may cause for hours to days, and the ache may be so intense that it interferes with each day ventures. Migraine and episodic headache are one of the most common kinds of primary headache. Migraine is considered as a severe health trouble that impacts the pleasant life. Migraines, which have an effect on youngsters and teens in addition to adults, can developed via 4 stages: prodrome, atmosphere(aura), charge and post-drome. Not everybody who has migraines is going via all stages. The paper tries to focus on the migraine prediction by the use of deep learning model. This paper proposes or provides a Machine Learning technique and model to discover the sort of migraine.
Characterization of Dual Multiresolution Analysis by Orthogonality of System of Functions: An application to communication engineering

In this article, we provide some fundamental Fourier series principles that are useful for addressing issues in electronics and communications, as well as the mathematical equations that these notions play in a variety of computing issues, particularly those requiring communication. The Fourier Transform resolves signals or functions into their vibrational modes. It is utilised in image processing, filtering, signal processing, signal analysis, constructing electrical circuits, and resolving differential equations. This paper contains the generalization of the wavelets form by the multiresolution analysis that consists of the sequence of closed subspaces <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\{\mathrm{V}_{\mathrm{t}}:\mathrm{t}\in \mathrm{Z}\}\ \text{of}\ \mathrm{L}^{2}(\mathrm{K})$</tex> . We have discussed the local field of positive charactertistic, moreover using the biorthogonal concept, we showed that the set of MRAs forming the pair are work as dual to each other. Also, the wavelets developed by the help of scaling functions of pair of MRAs, actually satisfy the orthogonal property, in addition they moreover form the orthonormal system.
An Exploration on Robotic Medical Assistant for Patient Monitoring and Health Assessment Using IOT

This paper presents a detailed discussion of a proposed Internet of Things-based medical assistant robot called Aido-Bot, which will be designed and implemented for the benefit of patients who are unable to care for themselves and disabled individuals. From that point onward, an inside and out clarification is given with respect to the essential calculation, engineering, and controlling framework. IoT-Based Medication Reminding and Medication Giving Framework, Programmed Hand Sanitizer, and IoT-Based Physiological boundaries noticing framework (Internal heat level, Heartbeat rate, and Oxygen immersion level) are created as per the proposed strategy. These frameworks likewise incorporate an immediate coordinated server-based specialized strategy and an end client android application keeping up with framework. In spite of the way that the accomplishments portrayed in the paper give off an impression of being productive and predominant, there are still a few weaknesses.
Cancer Detection Using Image Processing and Machine Learning

Cancer is one of the most complex diseases to be ever deal with and deadliest in later stages. Though X- rays, MRI and CT scans are helpful in identifying the diseases, they do not give clear information of what stage, type etc. cancer in medical image analysis is challenging and complex due to typical features of biomedical images. The advent of different image processing techniques has made the job of radiologists easy. Image processing is the most promising technology to analyze images and draw the conclusions. Here is small experimentation of how few image-processing techniques can be used to identify types of cancer. In this research paper a system is developed to detect the four fatal cancers i.e. Lung Cancer, Blood Cancer, Brain Tumor, Breast Cancer. Cancer is detected with different techniques like segmentation, morphological operations, and specific algorithms like watershed segmentation.
User Engagement Analysis of E-Commerce Websites from the Perspective of Eye Tracking

Eye tracking evaluations have been applied to HCI by breaking down the usability of PC UIs. The designers of Internet business PC UIs follow usability rules to execute web site interfaces. The web sites are by and large assessed by utilizing usability rules that give a nittier gritty agenda to web site creators. Clients are further expected to finish a usability poll to assess the client's fulfillment with the UI. New advances are continually being used to further develop web site usability evaluations. Eye tracking permits scientists to see where the client is cantering their consideration on the web site and can give extra data not given by standard usability evaluations. This exploration, utilizing an eye tracker, examines the mental cycles that a fledgling and experienced clients encounters while shopping on the web and lays out whether this cycle enhances usability testing.
Facial Expression Recognition for Compound Emotions using Mobile Net Architecture

The awareness of various categories of facial expression is very crucial in perceptual interfaces, computational models, and every field of life. The previous study recognized facial expressions related to a single felt category (For Instance: the facial expressions of joy when we feel happy). Recent studies have shown the importance of compound expressions category (e.g. feeling happy when we are surprised). The paper discusses the Mobile Net approach to classify the compound facial expression recognition using depth-wise separable convolutions to build light weight deep neural networks which can be used in embedded devices such as mobiles
Harpocrates: Privacy-Preserving and Immutable Audit Log for Sensitive Data Operations

The audit log is a crucial component to monitor fine-grained operations over sensitive data (e.g., personal, health) for security inspection and assurance. Since such data operations can be highly sensitive, it is vital to ensure that the audit log achieves not only validity and immutability, but also confidentiality against active threats to standard data regulations (e.g., HIPAA) compliance. Despite its critical needs, state-of-the-art privacy-preserving audit log schemes (e.g., Ghostor (NSDI ’20), Calypso (VLDB ’19)) do not fully obtain a high level of privacy, integrity, and immutability simultaneously, in which certain information (e.g., user identities) is still leaked in the log.In this paper, we propose Harpocrates, a new privacy-preserving and immutable audit log scheme. Harpocrates permits data store, share, and access operations to be recorded in the audit log without leaking sensitive information (e.g., data identifier, user identity), while permitting the validity of data operations to be publicly verifiable. Harpocrates makes use of blockchain techniques to achieve immutability and avoid a single point of failure, while cryptographic zero-knowledge proofs are harnessed for confidentiality and public verifiability. We analyze the security of our proposed technique and prove that it achieves non-malleability and indistinguishability. We fully implemented Harpocrates and evaluated its performance on a real blockchain system (i.e., Hyperledger Fabric) deployed on a commodity platform (i.e., Amazon EC2). Experimental results demonstrated that Harpocrates is highly scalable and achieves practical performance.
Implementation and Performance Analysis of IDEA in IoT-Based Smart Home Networks

The owners of state-of-the-art residential villas always thrive for feature-rich connected devices to secure personal dwellings to ensure that they are always safely connected with house surroundings and appliances. Despite the luxury of connectedness, smart home devices lead to the risk of data security and several types of security attacks. The advent of IoT has revolutionized the way smart homes function and are secured.[1]. Vulnerable local networks, weak IoT devices and unencrypted data communication between the sensors pose threats to SHNs. Though the various levels of protocols provide necessary security features for IoT -based home networks, the data communication path between the sensors and the gateways at the smart homes is vulnerable and is open to various types of attacks. Therefore, in this paper, we successfully implemented Light Weight Cryptography Algorithm IDEA in Smart Home Networks and analysed the performance of the network.