Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Smart Glove for Visually Impaired People

The visually handicapped have a difficult time locating things of everyday use because of their impairment. With the use of Deep Neural Networks (DNN) and YOLO version 5, the purpose of this study is to develop a smart glove with the intention of directing the hand of a person who is vision impaired to the desired object while the individual is inside. The micro-vibrating motors included within the smart glove are designed to direct the user's hand. The palm of the glove has a camera that connects to a Universal Serial Bus (USB), and this camera sends real-time footage to the system so it can be processed. Additionally, it includes a camera and a microphone integrated right into it. The user issues audio commands to the system, telling it to identify the item of their choosing. After that, the camera employs DNN in order to detect the item. The process of tracking the object will start after it has been tagged. The micro-vibrating motors vibrate in a manner that is proportional to the relative location of the camera in relation to the object in order to steer the user's hand in the desired direction. The ability to differentiate between identical items based on colour is an extra capability that has been introduced.
Survey on Collaborative and Content-based Recommendation Systems

In recent times information available on Internet is growing at the exponential rate. This makes searching correct information very difficult and it is challenging to perform the same within shortest amount of time. In such context there is need of recommender systems which can help in information filtering and promoting the information which is likely as per corresponding user’s interest. Recommender systems have become popular in widespread applications and many researchers are exploring it to make them more efficient and effective. Though recommendations systems are being used for a quite long time, many research challenges and issues in the design of effective recommendation systems are yet to be addressed in an effective manner. This paper discusses research challenges, opportunities and possible applications of recommender systems. Recommendation systems are categorized into collaborative and content-based filtering. This paper briefs working of both types of recommender systems and also discusses research challenges within them. The collaborative filtering approach suffers from many drawbacks, including data sparsity, gray sheep, cold start problem, and scalability. The content-based filtering approach suffers from reciprocity, sparsity and limited content analysis issues. Also, future research directions in collaborative filtering and content-based recommendation systems discussed. Various application domains have listed out where recommendation systems can be improved, such as healthcare, agriculture, etc. The paper describes possible future extensions in all these applications. Overall this paper will act as a guide for those who are interested in doing research in the recommendation system.
Data Security and Privacy Issues in Cloud Environment

Cloud Computing is a computing paradigm where the user store data, access data, and deliver services through the internet, many people are opting for the cloud for storing the data which makes working on things easier and also reduces the maintenance of the hardware. Security and privacy are two of the cloud computing industry’s most difficult issues. There are more chances of cloud servers being hacked, which give access to data to an unauthorized user, and the data can contain sensitive information which is why many users, or many organizations hesitate to migrate to the cloud. To protect and provide security to the data, one should apply some encryption techniques to the data before transferring them to the cloud. For providing security many cryptographic techniques are used, some of the encryption techniques are Data Encryption Standard, Advanced Encryption Standard, Rivest-Shamir-Adleman, and Attribute-based. The primary goal of adopting encryption techniques is to give security to the data in the cloud, which is enhanced by using an encryption algorithm. This study compares the different encryption techniques along with their advantages and drawbacks
Smart Patient Records using NLP and Blockchain

Text summarizing is the process of condensing text such that extraneous information is deleted and only the most important information is extracted and presented in the most understandable manner. Due to the exponential growth of social media data, it is now essential to assess this text in order to extract statistics, particulars and utilize it to the best benefit of several requisitions, applications and implementations. In recent years, the natural language processing and text mining communities have been more interested in the problem of automated summarizing, chiefly on the topic of opinion summarization. In society, views are essential for making decisions. A person or corporation depends on the advice and opinions of others while making choices. In this study, we provide a graph-based technique for creating summaries of repeated points of view and integrating the claims using sentiment analysis. The resultant summaries are carefully crafted to convey the text’s core and are based on abstraction.
ParPER: A Partitioned Prioritized Experience Replay in Multi-Agent Setting of Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) these days are used for modelling numerous real life applications. It is mostly used for modelling such scenarios in which there is very limited information of the environment. Off-policy Reinforcement Learning and Off-policy MARL makes use of buffer to store the experiences or transitions of every agent and later samples it for training purpose. Prioritized Experience Replay (PER) proposed a way to sample transition by the magnitude of the error a transition has during training. The more the error, the more is the probability of that transition of getting sampled for the next training. This method, though effective, encounters a problem. A transition which has low error, might never get sampled, though it is important for the model for that particular transition to be sampled to learn optimal policies. We propose Partitioned PER (ParPER) where we partition the memory into fixed length size and sample equal transitions from them independently. We test our proposed method on Multi-Agent Particle Environment (MPE) and the results were found to be promising.
Wireless IoT Security Management Enhancement and Optimization using Various Elements

The old security measures are failing because of the exponential rate at which the technology environment is expanding, effectively outlawing modern technologies. Similarly, to that, Industry 4.0 needs modern, smart solutions to improve security and productivity. This assertion should be taken into consideration since traditional security methods cannot always safeguard rapidly created information and numbers. This is true because both the technology being targeted and the active and passive attack methods are developing quickly. The Internet of Things (IoT) will have 75 billion connected devices by 2025, which will undoubtedly produce massive amounts of data that must be safeguarded and protected at all costs as it travels around the globe. Failure to do so may result in undesirable circumstances. IoT is a part of Industry 4.0, hence an Industry 4.0 solution would be the ideal option for addressing the numerous IoT difficulties. To determine the optimal criteria for securing IoT, this article compares many elements of the fourth technological revolution, including Cloud Technology, Data Science, Cognitive Domains, and Blockchain. In addition, this study also suggests a hybrid method for enhancing and maximizing the security angle of IoT. Using the hybrid work id POW with Watch dog Mechanism added this work.
Detection and Diagnosis of Fetus Ultra Sound Images using Deep Learning Classification Methods

This research work proposes detection and diagnosis of Down syndrome using soft computing and deep learning algorithm. The detection module of the proposed DS classification system consists of Gabor transform, feature extractions and Convolutional Neural Networks (CNN) classification methods. The suggested system’s diagnosis module uses the ANFIS classifier to categorize abnormal foetus images into ‘‘Early’’ cases and ‘‘Advanced’’ situations. This approach uses CNN classification methodology to identify and segment the NT region. This methodology divides each pixel in a foetal picture into normal and abnormal pixels based on the derived feature set. Then, morphological segmentation method segments the region belonging to NT in classified abnormal fetus image
Suspicious Activity Detection using LRCN

The massive rise in criminal activity in urban and suburban regions and suspicious behavior in public places pose a considerable risk to people’s safety and social order. It is vital to minimize those activities. Previously, humans used to monitor these activities manually, which is a very strenuous task. So, this article focuses on constructing a model for behavior recognition understanding and classifying the incredulous activity using artificial intelligence and deep learning concepts based on the LRCN model by combining CNNs and LSTMs. These concepts are helpful in learning visual features from video frames and to transfer a sequence of image embedding into class labels, probabilities, and sentences. As a result, this model has the benefit of both convolutional and recurrent neural networks. The resulting model detects suspicious activities that fall under 13 different categories, including bullying, firing, attack, robbery, blast, violence, extortion, gunshot, filching, annexation, and rifling. The proposed innovation categories the events using the LRCN model in python with Keras and tensor flow. This model collects frames from the videos and compares them to trained models with accessibility and high functionality with an accuracy of about 91.54929% making the model practically possible in solving an ample number of cases with limited resources.
Abusive Comment Detection in Social Media with Bidirectional LSTM Model

Social media is becoming more vulnerable to problems like personal attacks and undesirable behaviour like cyberbullying. Online abusive language includes abusive comment, indiscriminate slang, harsh language, and vulgarity, which serve as an instrument for very intense and cruel cyber abuse. The abusive comments on other people’s thoughts and beliefs can be hurtful and demeaning to those who express their views on social media. Manually reviewing each comment takes a significant amount of time and effort to determine which comments to delete. Therefore, an automatic process of identifying and preventing negative posts would not only save time but also give users comfort on social media platforms. In this work, a bidirectional long short term memory (BIS TM) model is implemented to identify abusive Twitter comments by classifying them into offensive and non- offensive comments. And the effectiveness of machine learning based abusive comment detection models with TF-IDF and Word2vec text representation techniques are compared with the proposed BLS TM model. The performance of abusive comment recognition models are evaluated using 10-fold cross validation with various performance metrics such as AUC, precision, recall, fl score, and accuracy. The experimental results showed that the best performance was yielded by the deep learning model BLS TM on the offensive language identification dataset(OLID).
Sentiment Analysis of Product Reviews by using Naive Bayes and Vader Models

Online web forums, blogs, and various social media platforms are now used by people to exchange ideas. Internet users occasionally offer reviews and thoughts on various products, brands, and their services. Not only do these product reviews enhance the quality of the product, but also have an impact on consumer purchase choices. Therefore, product review analysis is a well-liked platform where consumers can quickly become aware of their needs. Using the NRC emotion vocabulary, which can be divided into eight fundamental emotions and two sentiments, the six most popular items and consumers are chosen based on the plain text review. The eight different emotion groups in this research, are compared by using word clouds. In this study, 256,059 users and 74,258 products are followed for ten years, and 568,454 reviews of exquisite food are collected. The findings demonstrate how sentiment analysis may be used to pinpoint consumer habits and reduce risks in order to satisfy customers.
Analysis of Ethical Issues Associated with Wearable Medical Devices

Medical practice and the delivery of healthcare are changing as a result of the advancement and widespread use of medical wearable technology. The daily vast amount of personal data those results, privacy disclosure, and other ethical difficulties associated with interests are gradually brought to light, though. This paper summarises and analyses the ethical issues that exist in the entire process of health medical wearable equipment serving humans, including the equipment itself, data collection, transmission, management, and data use, from the perspective of medical ethics. It then suggests appropriate solutions and countermeasures from the perspectives of respecting autonomy, informed consent, privacy protection, medical optimization, and beneficial principles. To fully utilize the potential of health care wearables in the field of medical care, advance the development of new intelligent medical care, and contribute to the creation of a healthy India, the concepts of “prevention before disease, disease prevention, and change prevention,” and “medicine.
Augmented Reality for Advanced Home Applications

Augmented Reality is a revolutionary technology that is already being used in various technologies like Video Games, Simulations and Internet of Things (IoT). The use of Augmented Reality can also be done to take control of devices and appliances, specifically Smart Devices or ones equipped with Internet of Things Technologies and Tools. This paper explores the possibility of using Augmented Reality in conjunction to Internet of Things and presents an implementation of it on a Prototype with a Light Bulb using various AR tools, Cloud and IOT technologies like Arduino Uno Board. AR technology will bring various benefits including increased security via the required security target image for the light bulb to work and they are explored in this paper along with the limitations of using Augmented Reality (AR) with further improvements and suggestions. The possibility of combining Augmented Reality (AR) or Mixed Reality (MR) with Internet of Things (IoT) to create better solutions for control devices, where home automation is explored and also further improvements and limitations are suggested.
Robust Extreme Learning Machine based Sentiment Analysis and Classification

In recent times, S entimentalAnalysis (SA) acquires important attention in the process of decision making, primarily implied for the classification and extraction of the sentiments exist in the online reviews posted by the user. SA could be assumed as a sentiment classification (SC)issue where the online reviews experiences classification into negative and positive polarities based on the words available in the online reviews. This study focuses on the design of Robust Extreme Learning Machine Based Sentiment Analysis and Classification (RLM-SAC) model. The presented REIM-SAC model majorly aims to determine the nature of sentiments exist in the input data. Primarily, the input data is thoroughly pre-processed to get rid of unwanted data, which helps in enhancing the classification accuracy and minimizing the computational complexity. In addition, the presented REIM-SAC model applies ELM model to allocate proper class labels to it. To adjust the parameters of the ELM model, comprehensive learning particle swarm optimization (CLPSO) technique was used. The performance assessment of the RELM-SAC model is experimented with using benchmark database and the outcomes are scrutinized under numerous aspects. The simulation outcomes pointed out that the RELM-SAC method has obtained improved outcomes than other models.
Designing the Prediction Protocol based on Sparrow Search Optimization Algorithm with Vibrational Auto Encoder in E-Healthcare

Since knowing the likelihood of contracting an illness is crucial to maintaining a healthy lifestyle, disease prediction systems have made significant contributions to people’s daily lives. New data mining methods have allowed for the development of illness forecasting models. In particular, Single-Layer Perceptron may be used to extract useful information for building a disease prediction system from the mountain of medical data created daily. Many obstacles, such as information security and prediction efficiency, may restrict the practical application of the illness prediction system, despite its considerable promise. Therefore, determining the illness and safeguarding the data are the primary goals of the study. Many forms of preprocessing, including resizing, augmenting, and min-max based normalisation, are used to the raw data acquired by IoT devices. This process culminates in the use of a classifier based on the sparrow search optimization algorithm (SSOA) and the vibrational auto encoder (VAE) to distinguish between unhealthy and healthy occurrences. Finally, the Smartphone notifies the staff and administration in the event of a sickness incident.
Smart Attendance System using Dlib Pre-Trained Neural Network

Recognizing and remembering various people is the most frequent task, which the human brain performs. With regard to this, the process of attendance becomes one of the hectic tasks, which requires subsequent modernization. The spread of COVID- 19 is also drastically increasing and are pushed to the situation of wearing mask the entire time. This brings in a situation of misidentifying the individuals and are also prone to impersonation in many official gatherings such as exams, meetings, etc. This cannot be decreased by unmasking their face in this pandemic situation just for the purpose of verification as it may lead to increase in COVID risk. Here, this research study implements a contactless face recognition system with a simple and smart database, which can take in any form of data as per the convenience. This system solves the above problem by making the face recognition smart using Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) classifier. The main task of the system is to recognize the user’s face (live) and automatically mark the time of recognition directly in the Google sheet along with the alphabets of P(Present), A(absent) or L(late) according to the given time range. This system makes effective use of google sheet for easy share ability, accessibility, and error free management. This can be used for number of purposes such as exam centers, schools, colleges, companies, hospitals and various other places in order to verify the people (contact less).
Software Risk Ranking Assessment Model using Fuzzy Decision-Making Trial and Evaluation Laboratory

To ensure the quality of the software development process by developing an effective risk management framework. Project development, programs, and organizational activities are strongly influenced by the presence of hazardous events. Project success can be attributed to prioritizing project risk. Therefore, non-eliminable software risks must be given significant priority for the full success of the project. In this study, a risk assessment in a software project is conducted by evolving with a fuzzy basis. The life of software development includes a number of key software programs that greatly support the decision-making process. The most important and inevitable major software projects are identified and gradation by using methods the decision-making is unclear, make decisions, and laboratory evaluation (fuzzy DEMATEL) to assess the risk of the project. NASA’s data, 93 projects is used to carry out experimental validation. The results obtained show that project implementation is achieved by a set of factors called software risk factors. The experimental results showed improvements in risk assessment of the project application.
Object Detection Mechanism using Deep CNN Model

Object detection and tracking are important roles in computer vision algorithms. Detecting and tracking objects have many challenges like detecting objects efficiently in both indoor and outdoor circumstances and avoiding occlusion. Many traditional methods are ineffective in these circumstances. Recently, deep learning methods are preferred over traditional machine learning models due to its increased efficiency and accuracy. Object detection is a key topic in image processing and is used for detecting identifying and locating objects by drawing the bounding boxes to those objects. Object detection has various applications in various fields like security medical field and defense. This article discusses about the implementation of object detection for video and digital images and the detection of objects by web camera for higher accuracy and real-time implementation. Using a Convolutional Neural Network (CNN) result in the disadvantage of generating many feature maps for preventing occlusion increasing the future maps doesn’t increase the performance. So, an advanced and better neural network model is required for solving those problems. This research work has implemented object detection to analyzed digital image/video by using a web camera and deep learning method to achieve better performance with a comparatively high accuracy than existing methods.
Texture-aware gray-scale image colorization using a bistream generative adversarial network with multi scale attention structure

Various methods based on deep neural networks have been proposed to generate color images from gray-scale images, meanwhile, Generative adversarial networks (GANs) are also gradually applied to image colorization. However, the existing methods are texture-unaware, resulting in dullish color and color bleeding artifacts in the output images. This paper attempt to integrate a novel texture-aware bistream GAN into the conventional encoder–decoder structure for image colorization. In this study, the proposed bistream feature extraction module (BSFEM) and the feature boosting module (FBM), extract the global and local features from two parallel encoders and fuse them via a novel hybrid attention structure, this novel structure could emphasize the importance of certain channels and locations of features that may potentially benefit image colorization. In addition, the texture colors can be better recovered though the proposed multi-scale feature attention module (MSFAM). The quantitative experiments demonstrate that, compared to the state-of-the-art approaches, the proposed method has improved the PSNR and SSIM metrics by 18% and 8% respectively. Moreover, the qualitative results show that this method is capable of producing visually pleasant color images especially in terms of recovering texture details and eliminating color bleeding along the edges. The source code and data are available online at https://github.com/JarryZang/Image-Colorization-.