Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of the Growing Role of Blockchain Technology in the Healthcare Sector

In this research paper the effectiveness of block chain technology is going to be analysed for further explanation. Synchronising the contents of datasets based on different layers of patients is going to be illustrated here. This research paper aims to provide a brief introduction of block chain technology that affects a systematic literature review regarding this. Advantages and disadvantages of block chain technology in the healthcare sector are going to be demonstrated to implement a basic idea of block chain technology. Issues in healthcare organisations can be prevented by block chain technology to understand implemented aspects. Decision-making procedures in healthcare sectors are observed to be enhanced because of block chain technology. Entire system of healthcare sector and its functionalities are going to be defined in the literature review section of it. By doing a systematic review from peer-reviewed articles and journals it can be said that block chain technology is counted as a sustainable goal in the healthcare sector. Traceability and transparency are observed to be some of the greatest objectives related to block chain technology in the healthcare sector. This paper is going to fiend invaluable insights regarding this matter for changing the whole perception of block chain technology. The relative novelty regarding this topic is going to be given for further assessment that directs reader's right into informative resources. This research paper is going to display the role of healthcare practitioners to investigate various aspects of block chain-based information systems by conducting qualitative methods.
Detecting Fake Reviews using Machine learning techniques: a survey

The discovery of fake reviews is one of the major concerns for the E-commerce business-the Consumers on reviews before finalizing their choice for the product. The authenticity of online reviews is influential for e-commerce. Various companies are even filling spammers' pockets to post negative reviews for their rivals. Therefore, techniques for fake reviews detection have been explored in the past decade. On that account, this work provides a comprehensive survey of existing spam detection methods describing its features used for individual as well as group spam detection. A summary of the dataset for updates, products and reviewers. It also concludes the features to be considered while detecting a fake review. It discusses the issues and challenges faced while designing a fake review detection algorithm. In this paper, we aim to overview existing detection approaches in a systematic way, define key research issues, and articulate future research challenges and opportunities for reviewing spam detection. The existing techniques mainly emphasized over supervised machine learning, for which we require a labelled dataset that could work on real-world applications.
Performance Optimization of Autoencoder Neural Network Based Model for Anomaly Detection in Network Traffic

Network anomaly detection it is a major concern and challenging area nowadays although it provides effective and efficient mechanism from different types of attack. To enhance the security, the recent development in technological era leads to various deep learning methods for anomaly detection. Auto encoder is one of them which is better suited for network anomaly detection. In survey of existing Auto-encoder models, performance varies because there is no any specific approach mention the critical impact and importance of performance parameters and detection accuracy. To reduce model bias caused by data imbalance across different data types in the feature set, we use a new data pre-processing methodology in our proposed model that transforms and removes most affected anomaly from the sample input. Our proposed model is based on autoencoder model with five layers, employs the most effective reconstruction error function, which is critical for the model to determine whether a pattern of network traffic sample is normal or abnormal. These unique techniques, together with the ideal model architecture, enable our model to be better prepared for dimension reduction and feature learning to improve the detection accuracy and F1 score. We tested our suggested model on the CIC-IDS dataset, and it beat other similar approaches in detection, with the greatest accuracy and f1-score of 91.12 percent and 92.53 percent, respectively.
Comparative analysis of the performance of Machine Learning and Transfer Learning models in detecting hate on Twitter

As more and more individuals hop on the social media train, there is an exponential increase in hate speech on these platforms, especially Twitter, where there is no moderation present and users can post even Not Safe For Work (NSFW) content. Moderation of content is an impossible task if done manually. One cannot look at all the content and verify if it's safe or not. So, to tackle this problem, one needs the aid of Machine Learning and Transfer Learning. Hence, we have developed and compared three different Machine Learning models in this paper, namely: Logistic Regression model, Simple Neural Network model, and a Bidirectional Encoder Representations from Transformers (BERT) model trained on the same dataset, which would classify the given sentence as a “hate comment” or “not a hate comment,” to ensure a safe environment for all users on the platform.
A Hybrid Approach for Cloud Load Balancing

Unbalanced load has been considered as a main challenge for cloud environment as it affects performance metrics like reliability, makespan, cost, resource utilization, etc. A system that can manage resource workloads is an advantage for clouds. It can be achieved by using heuristic or meta-heuristic optimization techniques. Many techniques have been developed for cloud workload management and scheduling. However, a combination of both the approaches has received far less attention in previous work. A hybrid technique can improve the performance of the system by selecting the best resources from the available ones. This paper proposes a Hybrid Approach for Cloud Load Balancing (HA-CLB) using a combination of heuristic and metaheuristic algorithms for workflow scheduling to reduce makespan and cost while improving the resource utilization.
A Conceptual Analysis on the Impact of Big Data Analytics Toward on Digital Marketing Transformation

The study explores in competitive environment, digital transformation is critical. Many businesses have begun them becomes further by developing new business models, processes, and technology. Most others are already seeing the potential of digital revolution through bold innovation efforts. Directors play an important role in strategy formulation to enhance business outcomes and control the narrative for the future. The process of optimal data usage is a critical undertaking that is still in progress. Businesses want essential insights in order to make smarter decisions faster and execute in real time. According to Gartner's study, by 2020, 25percent of respondents of major firms will be buyers or sellers of data. In general, digital ecosystem actors have begun to acquire accurate information regarding their consumer and now are digitally reforming themselves by quite well their sales and marketing aims. As a result, Big Data has assisted businesses in increasing their Return. Furthermore, it provides practical, workable, and feasible research-driven answers to nearly every business-related challenge.
Face Recognition System Using Computational Algorithms

A face recognition program can be an innovation that can recognize or check an individual from an advanced photograph or video outline from a video source. It is used to find the world safer and more user-oriented. Finding missing persons, retail crime, security identification, identifying accounts on social media systems, or recognition of the drivers of cars are just a few examples. Face recognition is categorized into three steps: face detection, face extraction and face identification. In this work, we create a face recognition application platform using Open-Computer- Vision (OpenCV), with a focus on the system's performance and accuracy. Haar-Cascade was used for the Face Detection, Eigenfaces, Fisherfaces and Local binary pattern histograms were being used for Face or Facial Recognition. After the testing is completed, System is proved to have a friendly interface and it can detect and identify faces in real-time and accurately.
Real time interaction-based Healthcare monitoring during Medical Emergencies with Internet of Things

The most challenging problem in today's technologically advanced society is how to enhance the health of the elderly. Falling is the most frequent cause of serious injury and early mortality among elderly people. As wearable sensors improve their capacity to detect falls, the demand for these devices has grown. This implies that the system has a low social and financial effect on society. This means that automated fall detection systems and vital signs assessment systems are now required for even basic daily tasks carried out by the elderly. Healthcare solution is split into three sections: sensing, querying, and storing amongst others. RF transceivers are employed in the sensing section to detect the presence of tagged patients nearby. An SMS is used to transmit a request to the querying service, which then receives it from the GSM network. In the storage space, the patient information is well organised. According to the results, the proposed method was 96.43 percent accurate, 94.06% precise, 94.62% recall, and 94% Fl-score effective. The system's ability to be tracked through low-cost GSM technology greatly increases the system's use. A multifaceted and flexible system with easy access has been shown. It has the ability to make daily tasks simpler and more efficient as a consequence of its unique design.
Systematic Healthcare Smart Systems with the Integration of the Sensor Using Cloud Computing Techniques

Towards processing efficient technology management in healthcare sectors, the contribution of cloud computing is at once essential. In order to reduce human efforts, cloud computing and its benefits are used immensely for the growth and development of the entire health care sector. Regarding analyzing various advantages, bringing technological changes and improvements in healthcare aspects become easier than before. Today, systemic and smart healthcare approaches promote both the cooperation and exchanges of essential medical records and services. Designing proper sensor-based smart healthcare systems is an essential concern for physicians with the use of cloud computing technology. The process includes cost-effective sharing and storing of essential medical records for a safer and more successful generation of sensor-based smart systems. However, with this innovative use of cloud technology, physicians can easily track any relevant patient's information from a large database. In contrast to that, cloud computing techniques also help doctors to detect and diagnose any type of medical complications in today's clinical sectors for a sustainable future. A quantitative method has been adopted in the research study through conducting survey analysis among 60 participants. In order to analyze the importance of applying cloud computing as one of the innovative technologies along with smart designing of health care systems, these survey results can be highly effective. Moreover, a detailed interpretation, analysis, and discussion have been also conducted successfully after evaluating all the survey results regarding the particular research topic.
Gurmukhi handwritten word conversion using feature extraction using deep learning

Digitalization has resulted in creation of on-line repository off all government documents, which is as-sembled in fraction of seconds. But in the various fields of handwritten manuscript such as for a postal documentation it's considered to bit difficult task to recognize the handwritten city names to reach its destination location. In this study, recognition system for Gurmukhi hand-written names is presented. The features of the Gurmukhi script are discussed prior to actually empha-sizing on the features of word recognition system works on Gurmukhi. The handmade, printable, and digital characters and words identification for Punjabi language are taken into account for clarity and although includes the conversion of normal handwritten data into grey scale images that can be easy for the system to recognize. After a full analysis of all relevant articles, a detailed as-sessment of the feature extraction phase is performed, along with a histogram of the results.
Face Identification using visible eye region via vanilla CNN and Siamese Networks

Human faces being highly dynamic, are extensively studied in the field of pattern recognition, computer vision and artificial intelligence. Moreover, identification of faces using a part of it still remains an understudied domain. Detection of faces using just uncovered eye images can be a boon for surveillance and security especially in times of Covid-19 when most people are advised to cover their faces in a pub-lic space. In this paper we present a system, which identifies the person's face using the visible eye region namely the eyes and the forehead portions of the per-son. The model is trained over basic convolution net-work and the classification is done using Siamese net-works. The classification accuracy is measured using the dis-similarity score which calculates the euclidean distance between the converted feature vectors of the eye regions. The regions which are similar have neg-ligible dissimilarity score.
Optimised Ensemble Machine Learning Model for IRIS Classification

Machine Learning is one of the hottest topics in the field of research and industry as in its new techniques and methodologies are developing every breathing hour. The pace and the quality of this sector makes maintaining up with the new techniques little difficult, even for the specialists; and undoubtedly challenging for the beginners. It's a typical knowledge that gathering various views and inputs improves the method of higher cognitive process and so permits correct results. A term pronounced, Ensemble Learning within the fields of machine intelligence (AI) and machine learning (ML) is understood to enhance any call system's disputable abilities, efficiency, power (demonstrable strength) and of course it's accuracy.
Blockchain Integration with end-to-end traceability in the Food Supply Chain

Food supply chain is a complex but necessary food production arrangement needed by the global community to maintain sustainability and food security. For the past few years, entities being a part of the food processing system have usually taken food supply chain for granted, they forget that just one disturbance in the chain can lead to poisoning, scarcity, or increased prices. This continually affects the vulnerable among society, including impoverished individuals and small restaurants/grocers. The food supply chain has been expanded across the globe involving many more entities, making the supply chain longer and more problematic making the traditional logistics pattern unable to match the expectations of customers. Food supply chains involve many challenges like lack of traceability and communication, supply of fraudulent food products and failure in monitoring warehouses. Therefore there is a need for a system that ensures authentic information about the product, a reliable trading mechanism. In this paper, we have proposed a comprehensive solution to make the supply chain consumer centric by using Blockchain. Blockchain technology in the food industry applies in a mindful and holistic manner to verify and certify the quality of food products by presenting authentic information about the products from the initial stages. The problem formulation, simulation and performance analysis are also discussed in this research work.
Detection of Morphed Face, Body, Audio signals using Deep Neural Networks

Deepfakes have been a hot topic in the field of deep learning. It is typically used to alter the face or body of a person to create a fake image or video. With the rise of internet, the number of fake content especially deepfakes have increased exponentially. There have already been cases of these causing conflict and hatred among people. To keep this misinformation regulated, there needs to be a way to distinguish deepfakes from the rest. We therefore have come up with a model to classify deepfakes from pristine, accurately and quickly, so that anyone can upload an image/video to know whether it is genuine or not. The parameters taken into consideration for classifying deepfakes are face, audio and body language. The model for face consists of MMOD-CNN Face detector for pre-processing the input, which is then passed on to a Temporal Convolutional Network (TCN) to predict. For audio deepfake detection, audio converted into a spectrogram is passed to a ResNet50V2 followed by a TCN to predict. The Body Language model uses a vanilla TCN to predict if its a deepfake video or not.
Sentiment Analysis on Comments in Bengali Language Using Text Mining &amp; Machine Learning Approach

In the modern era, people are highly engaged in the virtual world to express their feelings and opinions. Social platforms and news portals are the precious media for expressing views on any sector nowadays. Thousands of information are being added to social media sites every second. During this research, some specks of data were collected and analyzed for extracting public sentiment. Sentiment Analysis is the process to identify computationally and categorize opinions expressed in a piece of text significantly to determine the author's attitude towards a specific topic. It is one of the most renowned research areas in Data Science. There is much research done on sentiment analysis in various sectors for the English language and increasing. Still, works for the Bengali language are limited to Bangla corpus and Bangla micro-blogging. So, this research has aimed to apply Sentiment Analysis to the Bengali language in which the public can express their opinions and emotions in their endemic words. However, it is not easy to apply it to the Bengali language because of the complex grammatical structure of Bangla. This paper represents the method of preprocessing the dataset using the NLP and applying Machine Learning approaches to classify test data. The process starts with removing unnecessary words and then using the TF-IDF vectorizer for feature extraction and applying cosine similarities, LSA and SVD for feature selection to form a meaningful and optimized feature vector to undertake the experiments. Then an analysis was performed to compare different machine learning approaches to the extracted information. Finally, the proposed model categorizes the document into five classes, namely negative, near negative, neutral, near positive, and positive, with profound accuracy, which might further be applied on similar types of datasets.
IFVSNet : Intermediate Features Fusion based CNN for Video Subtitles Identification

This paper presents a method to determine the presence of subtitle in a video frame through intermediate features fusion-based Convolutional Neural Network (CNN) called the IFVSNet via partial transfer learning process on pre-trained VGG13. The intermediate features from all the convolutional blocks of the CNN are fused by concatenation after each max-pooling layer. The features to be fused are of the same dimension. They are then flattened and passed as an input to the subsequent fully connected layers, ending with the sigmoid layer. Binary cross-entropy is used as the objective function. The model is evaluated on the Film Videos Dataset (FiViD) [21], which is composed of video frames with and without subtitles in Arabic languages. We obtained an average accuracy of 98.83% which can be taken as an effective result. We have also conducted other experiments by fine-tuning the VGG13 model, another by using VGG13 as the features extractor and Long Short Term Memory (LSTM) as the classifier, and lastly, with a 3-layers CNN trained from scratch. It is observed that the performances of these three models are lower than the proposed methodology. The class-wise analysis of the proposed method has also demonstrated that the model is quite strong in distinguishing the classes present in the dataset and achieved a precision, recall, and F-score of above 98%.
Auto-ML Web-application for Automated Machine Learning Algorithm Training and evaluation

Data Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. Data science is related to data mining, machine learning and big data. Data science is a concept to unify statistics, data analysis and their related methods in order to understand and analyze actual phenomena with data. It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, domain knowledge and information science. A challenge faced by Data scientists is the strict timeline and tough schedules. This makes way for the scope of an application that can work as an auto-ML app and do most of the tasks by itself and yet, involve the data scientist at each step for modifications. This paper describes an approach with some applications of the web-app that is a demo to the whole idea proposed above. This web-application plots correlation matrices of features that lays a foundation for auto-feature selection and then trains the models over the data-set using a predefined template that, plots a comparison table of the models and performs automated model training and provides the best model fit to make predictions.
Vehicle Counting using Computer Vision: A Survey

The growth of vehicles on the roads of urban areas generates an increase in traffic density and congestion. In this situation, most cities in modern countries are starting to implement smart solution for solving this issue, which includes the traffic management systems. And inside the traffic management system, the vehicle counting becomes an important functionality since it allows knowing the traffic density and because the data it delivers allows planning and improving the structure of the roads within cities. Many of current vehicle counting solutions contemplate the use of sensors and radars; but considering that surveillance systems based on video cameras have currently been implemented on most roads within a city, those systems are being evolved to use of computer vision for counting vehicles. In this circumstance, this work reviews different previous works developed in this area and identifies the technological methods and tools used in those works; in addition, this work also presents the trends in this area. The most relevant articles were reviewed, and the results were summarized in tables and figures. Trends in the used methods are discussed in each section of the present work.