Reviewerly/exHarmony huggingface dataset:
Federated Learning to Preserve the Privacy of User Data

In today’s world, the most pressing problem is the privacy of users’ data. It becomes even more critical when dealing with medical data, which is extremely sensitive. On the other hand, traditional machine learning (ML) algorithms require a single centralized source of data, which frequently compromises data privacy because data must be shared in order for the algorithm to be trained. As a result, federated learning is utilized to train ML algorithms on local private data sets spread across many sites. This also safeguards the privacy of the data. Our suggested method attempts to diagnose two urinary system diseases. We begin by training our model with logistic regression. Then, in order to simulate federated learning, we divided our data set into three sections. Then, each party trains the ML model on its local data set, and all of the updates from the local models are delivered to the trusted aggregator, which averages all of the updates. The averaged model is then sent to all individual parties at the start of each iteration. This entire system aids in the successful identification of two urinary system disorders by utilizing federated learning and distributed data principles in which data is not shared between individual parties and remains in distinct locations, considerably improving the privacy of such sensitive data.
User Feedback Severity Level Identification and Classification through Deeper Analysis of Text

Now a days world is look right on digitalized. Social media is captivating in this digital age through the accessibility of consumer's feedback. The recent work in the field of classification based on comments on social media is gaining appeal on a global scale. Unfortunately, the study does not offer better accuracy in terms of toxic comments. On social media platforms, hateful and abusive language has a detrimental effect on users' mental health and involvement from people from all diverse backgrounds. Automatic methods is most commonly used datasets with categorical labels to detect foul language. The level of offensiveness of comments varies. In NLP we use binary classification like either a comment is offensive or not and leave continues classification. In continues classification one can identify the severity level of comments, can set a threshold, and by using Deep Learning and modeling techniques can directly identify the severity level of comments by considering context. The review of related literature shows that identification of toxicity of user comments can be improved by pre-processing methods, such as deleting null values and anomies from the dataset, to refine the dataset and increase its accuracy by applying different algorithm techniques to make feature more valuables. This research provides analysis of user comments datasets and study's user comments toxicity with different machine learning approaches. First, we need to do pre-processing steps including punctuations, stop words, null entries, and duplicate removal to remove anomalies. After that we need to apply different methods like count vectorizer and bag of words to extract features. After that, we MCPL algorithm applied on these datasets to predicts results. By applying MCPL model on user comments dataset 88.5% accuracy were founded.
Meta Analysis of Human Body Diseases with the Application of Machine Learning

Machine learning in medical applications is one of the focus areas of the researchers these days. Machine Learning with the application of Artificial Intelligence is not only giving solutions to the complex problems but also revolutionised the medical field. The main motive of machine learning is to improve its learning process over time by taking all the relevant data and information in the form of different inputs and observations. This study reviews different medical disease prediction and detection techniques with the help of distinct deep learning & machine learning models. The problems related to medical diseases, like cancer related diseases, heart, lung, thyroid and kidney diseases are being discussed in this article. Detection and analysing of medical diseases is one of the prominent applications of machine and deep learning. Deep learning as a technology offers a huge set of different and innovative tools which are relevant to different issues faced in the field of medical image processing. This study will discuss about the applications of Machine Learning, and then discuss some of the advancements done in different diseases like breast cancer, heart disease, skin disease, kidney disease etc.
Blockchain-Based System for Secure Data Sharing in Cloud Using Machine Learning: Current Researches and Challenges

The adoption of blockchain is accelerating globally. Cloud computing and secure data sharing have emerged as new technologies, owing to current advances in machine learning. Conventional machine learning algorithms need the collection and processing of training information on centralized systems. With the introduction of new decentralized machine learning algorithms and cloud computing (CC), Machine Learning (ML) on-device is now a reality. IoT gadgets may outsource training from cloud computing services to incorporate Artificial Intelligence (AI). Furthermore, these distributed edge frameworks bring additional issues, including data confidentiality and information safety. To overcome these challenges, blockchain has been proposed as a viable alternative. Blockchain, as a distrusted framework has evolved as a revolutionary innovation due to its decentralized, accessible, and secured structure. This system also includes trustworthy automatic scripting and static information recordings. The complexity of blockchain technology and the level of skill needed to work with it is one of its drawbacks. This issue can be successfully resolved by using Machine Learning (ML) frameworks. It can increase the user-friendliness of Blockchain. The existing blockchain-based cloud computing, machine learning, and secure data sharing are summarizedin this research study.
An Adaptive Deep Convolution Neural Network for High Pixel Image Segmentation and Classification

In current technologies, the efficient and enhanced potentialities of computer vision are presented in various competence use cases. The deep learning model is used to detect the group of images, which are embedded with image processing and convolution neural network algorithms. This study focuses on the image augmentation and segmentation challenges that occur while detecting an object by using fast region-based CNN model and RNN. To implement deep learning algorithms, the RNN architecture is implemented with Kernel filtering and tested on specific group object dataset to provide enhanced results when compared to the optimized algorithms. This research study has developed a high object pixel segmentation approach for enhancing computer vision using deep convolution neural learning architecture to detect and classify a group of images by using TensorFlow framework and further test it by using Keras layers for achieving the most effective results.
Role of Blockchain in Manufacturing and Logistics Supply Chain Management

Blockchain is a distributed, decentralized ledger that is digitally managed & records transactions in an immutable format. Blockchain helps build transparency, trust, and detectability, making it an ideal fit for applications where monetary exchanges are the primary activity. Initially, the technology was developed to support transactions, but today the innovation has acquired consideration even in other areas such as retail, manufacturing, healthcare, and government services. The manufacturing industry for centuries has seen work based on their bonding and trust with their stakeholders. As regular manufacturing plants are venturing into gigafactories, the support of several middlemen will complicate the process. Subsequently, this research paper aims to outline blockchain innovation in managing the supply chain and logistics-related difficulties by figuring out and analyzing it. The audit shows how blockchain can change the logistics and supply chain network into safer, lithe, trusted, and straightforward capabilities.
The Impact of Blockchain Technology to Protect Image and Video Integrity from Identity Theft using Deepfake Analyzer

Deepfake is a critical problem that poses a significant threat to the authenticity and integrity of visual media. Although computer vision and pattern recognition have been employed in previous studies, there exist limitations on the quantity of layers that are permissible for use. This article proposes a strategy based on decentralized blockchain to create a secure and reliable system that can determine the authenticity of content in order to tackle this problem. tracing the original video, and safeguarding media integrity from identity theft. The proposed approach employs a combination of technologies including Blockchain, the encryption, media filtering, The proposed approach utilizes the The Convolutional Neural Network, Consensus Algorithm, and SHA-256 Hashing Algorithm are employed to ensure the protection and integrity of the media. Additionally, the implementation of a Deepfake Analyzer is recommended to augment media protection. The proposed method's efficacy is assessed by utilizing an available dataset from GitHub, and the outcomes are extensively examined. This research offers valuable perspectives on how blockchain technology can be leveraged to safeguard the integrity of images, videos, and reduce the spread of misinformation in the media. The proposed Deepfake Analyzer adds an additional layer of protection, ensuring video and image integrity against identity theft.
Comparative Study of Different Optical Character Recognition Models on Handwritten and Printed Medical Reports

Text may be extracted from an image using a method called optical character recognition (OCR). An OCR is mostly used to translate existing paper documents or image data into editable documents. To create an OCR, a large variety of algorithms are needed, and it essentially functions in two stages, such as character and word detection. To retain a document's structure while using a more advanced technique, an OCR additionally focuses. The advancement in OCR has been very promising lately. Different tools have arrived in the market ranging from open-source tools PyTesseract to big established companies releasing their own OCR tools like Amazon's AWS Textract and Google's Google Vision. The accuracy of modern OCR techniques like AWS Textract, Google Vision, and updated versions of Pytesseract haven't been analyzed for different datasets. For complex datasets like a CBC report, there are various parameters and fields in a tabular and unstructured fashion. Every algorithm has a different approach to extracting the text from the image, so it becomes difficult to determine how accurate these models are on such datasets. Based on 2 different datasets, one handwritten and the other printed CBC images, this study performed analysis using the above 3 models by building an end-to-end pipeline to retrieve the necessary fields, thus making it feasible to access the accuracy of these models. Upon testing these models' accuracy, the study concluded that the AWS Textract model was the highest-performing model for both datasets, achieving an accuracy of 95% for the printed dataset and 89.42% for the handwritten IAM dataset after preprocessing.
An Interview with Kalyanmoy Deb 2022 ACM Fellow

Prof. Kalyanmoy Deb has been named one of the 2022 ACM Fellows for "Outstanding Contributions That Propel Technology Today", in particular for his "technical contributions in evolutionary multi-objective optimization algorithms and multi-criterion decision support." Kalyanmoy kindly agreed to answer all our questions, ranging from his seminal contributions to his views and perspectives on evolutionary computation and its role within computing science and artificial Intelligence.
PreFair: Privately Generating Justifiably Fair Synthetic Data

When a database is protected by Differential Privacy (DP), its usability is limited in scope. In this scenario, generating a synthetic version of the data that mimics the properties of the private data allows users to perform any operation on the synthetic data, while maintaining the privacy of the original data. Therefore, multiple works have been devoted to devising systems for DP synthetic data generation. However, such systems may preserve or even magnify properties of the data that make it unfair, rendering the synthetic data unfit for use. In this work, we present PreFair, a system that allows for DP fair synthetic data generation. PreFair extends the state-of-the-art DP data generation mechanisms by incorporating a causal fairness criterion that ensures fair synthetic data. We adapt the notion of justifiable fairness to fit the synthetic data generation scenario. We further study the problem of generating DP fair synthetic data, showing its intractability and designing algorithms that are optimal under certain assumptions. We also provide an extensive experimental evaluation, showing that PreFair generates synthetic data that is significantly fairer than the data generated by leading DP data generation mechanisms, while remaining faithful to the private data.
Automating Library Migrations with Error Prone and Refaster

Software projects use a significant number of external libraries to speed up development and deliver high-quality software in a cost-effective way. Requirements placed on external libraries can change over time and libraries may cease to be maintained or evolve in incompatible ways. Consequently, a library may no longer be fit for purpose and require replacement. Such a library migration is generally performed manually and may be complex, error-prone, and time-consuming, especially for large codebases. It follows that an automated approach to library migrations is preferred. We consider the suitability of Error Prone and Refaster, two Google tools capable of refactoring Java programs, for automated library migration. For validation we consider a migration in a proprietary industrial codebase as well as an actively developed open source codebase. We show that using out-of-the-box Error Prone and Refaster full migration of RxJava 2-referencing files is possible in only 35% and 0% of such files, respectively. To increase the capabilities and be able to migrate more Rx-Java 2-referencing files completely, we create extensions to Error Prone and Refaster. Therefore we propose and create extensions that improve the accuracy with which Refaster templates are matched, as well as the correctness of their replacement operations. We additionally propose a set of Error Prone extensions to facilitate rewriting method return types. Together they enable a new class of rewrite operations especially relevant to library migrations. Lastly, several generalizations and improvements of existing Error Prone plugins are discussed. Together these improvements enable a new class of rewrite operations, especially relevant to library migrations. Using the proposed extensions, we manage to fully migrate an additional 35% of the RxJava 2-referencing files in the industrial codebase, and an initial 39% of such files in the open source codebase.
Statistical Machine Learning

We are living in the golden era of machine learning as it has been deployed in various applications and fields. It has become the statistical and computational principle for data processing. Despite the fact that most of the existing algorithms in machine learning have been around for decades, the area is still booming. Machine learning aims to study the theories and algorithms in statistics, computer science, optimization, and their interplay with each other. This chapter provides a comprehensive review of past and recent state-of-the-art machine learning techniques and their applications in different domains. We focus on practical algorithms of various machine learning techniques and their evolutions. An in-depth analysis and comparison based on the main concepts are presented. Different learning types are studied to investigate each technique’s goals, limitations, and advantages. Moreover, a case study is presented to illustrate the concepts explained and make a practical comparison. This chapterl helps researchers understand the challenges in this area, which can be turned into future research opportunities, and at the same time gain a core understanding of the most recent methodologies in machine learning.
A New Trend in Car Personalization Based on Augmented Reality: A Study

The article points to a new trend occurring in marketing. In the car industry, there is a lot of money and that is why car companies come to the market with new ideas. Augmented reality has become one of the tools to transfer the 3D dimension to the customer. Publicly available applications are being created that allow us to personalize car models according to our own requirements. The customer can choose the type, colour, design and equipment of the car. He can then project the configured car into space using a mobile phone. In the article, we want to present and compare three freely available augmented reality applications from three automotive companies. Each of the evaluated applications has its advantages and disadvantages, but this article points to selected parameters and options for working with the application for the best possible experience. In the end, individual applications of augmented reality are compared. In the article, we wanted to show that applications can be used in marketing to create the first contact with the client.
Digitization, Personalization, and Automation of Business Processes

The rapidly developing era brings with it changed and especially more demanding requirements for businesses. A company that wants to stay in the market must constantly follow new trends and possibilities in order to keep up with the competition and not lose its market position. Digitization of processes is one of the ways that the company will facilitate its operation on the market, thanks to digitization it can better and better sort information about its customers and have the necessary information not only quickly available but also in one place. This article focuses on the new possibilities that businesses have, thanks to digitalization. It points out the reasons why businesses should digitize available data about their customers and what benefits it brings, not only to businesses but also to customers. A customer who receives a “made-to-measure” offer is included in the category of satisfied and especially loyal customers. Subsequent personalization is another step that will make the obtained information more transparent and enable the company to work with the customer in a more targeted manner. The last part of the article is devoted to automation and especially its connection with the company’s customer relationship management (CRM). The benefits from digitization, personalization, and automation of business processes will have a long-term effect for the company, which will be able to focus precisely on a specific customer and his requirements and create a precisely tailored offer for him. The customer will not have to turn to the competition and look for other options.
Artificial Intelligence Usability Evaluation of Entrepreneurial Mindfulness Application

Mindfulness is a condition that entrepreneurs have and is very important because it involves the alignment of mind, soul, and body so as to produce more appropriate intentions, actions, and strategies. The entrepreneurial mindfulness application was developed with the aim that entrepreneurs can find out their mindfulness capacity. To maximize application development, a usability test is needed so that application developers can find out the response of users to the quality of the application. This study aims to conduct usability testing on the entrepreneurial mindfulness application and find out how the system condition is described according to the user so that developers can find out what the shortcomings are in the application. This research will be conducted on applications that have been developed, namely entrepreneurial mindfulness applications. The method is carried out using Neuroresearch and will be carried out through three stages, namely exploratory, explanatory, and confirmatory. Exploratory research and qualitative research are carried out through theoretical studies and gain an understanding related to User Interface and User Experience. Followed by explanatory and confirmatory research as a form of quantitative research to deepen exploratory research by analyzing the variables obtained. The results of the study through confirmatory research showed that most of the respondents gave feedback in a good category with lower bound - upper bound = 63.5051 - 66.0568.
Support Vector Machine based Handwritten Letters and Digits Recognition using Deep Learning

Number recognition can be an important and necessary challenge because handwritten numbers are not similar in size, thickness, position and direction; this method should consider different difficulties to deal with the difficulty of recognizing written numbers. Individuality and assortment in the composition of varieties of different people additionally affect the instance and availability of numbers. It is a process for selecting and composing translated numbers. It's a wide variety of apps, such as programmed arrays, contact points and income related documents, and then beyond. The objective of this work is to implement an algorithmic classification rule for recognizing written numbers. Sometime consequences are probably used various machine learning algorithms like K-means nereast neural networks (KNN), Support vector machine (SVM), KNN and Deep Learning calculations using keras, tensorflow and CNN classifier. The simulation accuracy is 98.70% obtained using CNN and compared with SVM 97%, KNN is 96% and keras as 96%.
Exploring Semi-Supervised Learning for Camera Trap Images from the Wild

Camera traps are an important tool for ecologists in their fight against ever increasing animal extinction. However, the use of these camera traps involves the tedious process of manually labeling the animals in captured images. An added hinderance is that of empty images triggered by wind movement and other stimuli called ghost images. Deep learning techniques have previously been applied to automate this task but have been prevented from being entirely effective due to two problems. Firstly, a lack of labeled data due to the expertise of ecologists being required to perform the labeling and secondly the training data being imbalanced in nature due to the high presence of ghost images and images of common animals. Many semi-supervised learning (SSL) algorithms perform well using very small amount of labelled data however need to be evaluated when trained with imbalance data. This paper explores the performance of FixMatch and a derivative called the Auxiliary Balanced Classifier (ABC) under a variety of data imbalance and proportions of labelled data. The algorithms were evaluated using a in the wild imbalanced dataset from camera traps in addition to benchmark datasets such as CIFAR-10, CIFAR-100 and SVHN. While FixMatch showed a consistent drop in performance as the data imbalance was increased, the algorithm generally outperformed ABC. However, the ABC derivative performed better than FixMatch in cases of very high imbalance.