Reviewerly/exHarmony huggingface dataset:
An Efficient Image Based Mammogram Classification Framework Using Depth Wise Convolutional Neural Network

Architectural Distortion is the third most concerning sign of abnormal areas on a mammogram. It's challenging to diagnose architectural distortion (AD) using mammograms because of the condition's delicacy, fluctuating imbalance on mammary mass, and small size. The use of computer algorithms for the early identification of aberrant ADs areas in mammography might aid radiologists and clinicians. Classification performance is negatively impacted due to star-shaped structural defects in ROI recognition, noise reduction, and object localization. This method uses computer vision to automatically filter out background noise and pinpoint the precise placement of items inside complex patterns. This study used computer vision techniques to investigate the potential for identifying mammography with geometric deformation inside ROIs. The researcher proposed a computer-aided diagnostic approach that utilizes machine training to analyze architectural deformation in digital mammography for the purpose of identifying breast cancer. Image preprocessing, enhancement, and pixel-by-pixel segmentation are only some of the four components of the proposed mammography classification system. Architecture-based distorted region-of-interest (ROI) identification, deep learning and machine learning network training for malignant/benign ROI classification in Alzheimer's disease.
Social Perspective of Suspicious Activity Detection in Facial Analysis

Technology is demanded on to curb crimes, especially image recognition, which can be used to detect suspicious activities. Image, object, and face recognition along with speech identification can be used as great tools to achieve this target. The machine lerning algorithm gave immense capabilities to detect faces, objects, and speech to identify malicious activities, and with several epochs, the accuracy can be enhanced. The chapter applies the various ML algorithms on real-time video data to increase the accuracy and gets satisfactory results in this social cause of utmost importance.
Lack of Efficiency on Robotic Hectoring Recognition over Online Social Media Networks using Novel Support Vector Machine Algorithm Comparing Lasso Algorithm

This study points to enhance the efficiency of detection to alleviate the impact of cyberbullying using machine learning classifiers by comparing the Novel SVMalgorithm and the Lasso algorithm. Materials and Methods: The Novel SVMalgorithm sample size of 184 and the Lasso algorithm test size of 184 with G power (0.6) in order to anticipate the efficiency %, were examined several times. The Novel SVM algorithm maps the prediction by using its labels. Results: The Novel SVM algorithm has better accuracy (88.90%) in comparison to the Lasso algorithm efficiency (80.00%). The outcomes that had a significant value p=0.001 (p<0.05) demonstrates the statistical significance of two groups. Conclusion: The Novel SVMalgorithm considerably performed greater than the Lasso algorithm.
Privacy Preservation of Business Forecasting Using Homomorphic Encryption

Data privacy is very much essential in this digital world. Data privacy prevents the information of an organization from fraudulent activities such as hacking, phishing, and identity theft. Machine learning is an emerging technology. But a huge amount of data is required for training the Machine learning model. When an organization wants to analyze their profit rate it has to send its data to third party which may reveal organization's business tactics or sensitive data. Hence, there is always a risk of data privacy. So, privacy preserving is used. Privacy preserving prevents data leakage from machine learning algorithms. There are many privacy preserving machine learning strategies which are used for data privacy. Homomorphic Encryption is one such technique. In homomorphic encryption, the data to be fed to train the machine learning model is encrypted. The encrypted data is then fed to the machine learning model. The machine learning model performs the required computation and returns the result in encrypted form, which on decryption returns the required output
Allocation of cloud resources based on prediction and performing auto-scaling of workload

Cloud Computing allows remote access to allocated services from anywhere in the world through the internet for end users. Interpretation and analysis of real-time data are one of the most challenging tasks for cloud analysts. The determination of the correct amount of resources required to match the world is difficult. On the other hand, the large configuration makes the resource underutilized, resulting in huge economic costs. In the current decade modeling and analyzing time series data across different fields has attracted researchers in cloud computing. To overcome huge economic costs allocation of cloud resources based on prediction and performing autoscaling of workload has been proposed. This prediction analysis can avoid losses such as service unavailability, maximum energy consumption, and customer loss. When the demand is large, more resources are requested from the cloud service provider to complete the task before the deadline. When the demand is less the idle resources are released. Based on predicted values, we can reduce the workload by performing autoscaling (horizontal & vertical) in the allocation of resources.
Blockchain Open Source Tools: Ethereum and Hyperledger Fabric

In recent times, blockchain has received a lot of exposure due to its increased usage in many fields. Blockchain is a decentralized and open ledger that contains information on transactions. It works as an underlying technology for cryptocurrencies like bitcoin and others. It eliminates the double-spending problem by implementing the digital signature mechanism. Blockchain technology has evolved to address the confidentiality and other technical issues that come with using distributed databases. It offers a decentralized environment for processing the transactions, establishes trust among the nodes participating in the network, and also removes the use of central third-party authority. The blockchain contains information on every transaction and is completely change-resistant. It offers peer-to-peer networks in which all nodes are able to validate the transactions happening in the network using a consensus mechanism. This is a very revolutionary technology that has been developed and widely used in a variety of fields. But still having some challenges like security, privacy, storage, scalability, latency, higher consumption of energy, and others. In this paper, the two popular open-source tools for blockchain technology are presented: Ethereum and Hyperledger Fabric. Ethereum is public and permissionless platform whereas Hyperledger Fabric is private and permissioned. The Transaction life cycle of Ethereum and the architecture of Hyperledger Fabric are discussed.
Enhancement of Recommendation Systems through Collaborative filtering to assure Customer Requirement

Online advertising is the most popular and significant marketing tool in today's world. Traditional advertising systems failed to meet the needs of varied users, since they display ads without considering the individual's characteristics. Now-a-days ads are displayed based on the previous search history of activities of the users, but, human mind change most frequently and the ads are being displayed regardless of the user's emotion. In this paper, we introduce an improved advertising system that predicts the user's sentiment based on the genre of the content being viewed and queues ads accordingly that uses both Collaborative and Content based Filtering that paved way for Hybrid Recommender System. This will enhance the user experience and the quality of the advertisements and advertising will be improved.
Machine Learning Designed identification on cervical cancers in patient

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods.
Analysis of Visualization Tools for Team Sports

Visualization is a major topic in sports and other professions that deal with performance. All the enthusiastic spectators and an increasing number of athletes across all sports credit visualisation for their better performance and success. Understanding the facts now in existence and the challenges posed by picturing team sports are the main goals of this work. For the time being, team sports visualisations do not have any defined criteria, according to my review. Tools and approaches for visualising team sports are examined in this work.
Identify Facial Micro Expression Using Support Vector Machine Compared with Artificial Neural Network to Improve Recall Parameter

Aim: The creation of facial recognition systems, which are crucial in the modern world, is the aim of this research. The Novel Support Vector Machine and Artificial Neural Network are used in this study to build a rapid facial recognition technique in Python. In this research article, we compare the performance of Novel Support Vector Machine and Artificial Neural Network in facial recognition. Materials and Methods: The detailed studies of suggested algorithms are reviewed. The testing was carried out using a publicly accessible face database. Each algorithm is put to the test using ten different photos, each with a varied face expression and lighting. For the SPSS study, nearly 10 samples were taken to evaluate, compare, and understand the accuracy of proposed algorithms. For accuracy prediction, a G power of 80% is used in the SPSS software. The parameters considered are CI and alpha, which were determined as 0.003 (p < 0.05). For SVM group 1, 10 samples are taken and for Artificial Neural Network algorithm group 2, 10 samples are taken to compare the Recall for facial expressions. Result: The results of combining several feature extraction methods and classifiers were given and examined. SVM was shown to have the best accuracy, with a score of 97.01 % Conclusion: Emotion recognition is a promising technique for improving the efficiency of current image-based recognition techniques. In this research project.
Varoka-Chatbot: An Artificial Intelligence Based Desktop Partner

Today, more than ever, our lives have gotten faster and more frantic. Currently, artificial intelligence (AI) is improving our quality of life. We have begun to communicate, interact, and learn online. Intelligent Personal Assistants (IPAs), which enable users to converse through Natural Language Processing (NLP), enable the ultimate luxury of having a companion who always listens for you, acts when necessary, and anticipates your every need. We shall accomplish things in the era of rapidly evolving technology which we have never dreamt of, were possible in the past. A virtual personal assistant (VPA) is what this initiative seeks to develop, with the ability to automate duties and provide services for a person, allowing you to enjoy the luxury of doing so. The purpose of our personal assistant, VAROKA, which is entirely written in Python, is to provide you control over your desktop. The built-in speakers respond verbally to the user's voice request after it has been recorded via the microphone. With the hopeful rise and advent of IPAs, this voice-controlled virtual assistant helped to surpass our expectations by offering full features of employing desktop technology on customers' voice commands.
A Novel Ai Framework for Personalisation and Customization of Product Prices through Bigdata Analytics

Businesses are looking for novel solutions to automate online shopping due to the worldwide rapid growth of online transactions which eventually results in Bigdata. The surviving retail operation of faring channels are not able to obtain a convenient successful collaboration between channels. Therefore, this article comes up with a novel solution of creating an Artificial Intelligence (AI) poweredshopbot. A shopbottracks the price of the product and compares it with the other E-commerce platforms and suggests customers the best platform to purchase the product that they desire to buy. This avoids wasting of additional time for looking up each retailer's price. By using this strategy customers may rapidly compare fares from several vendors for the same goods with the help of shopping bots through bigdata analysis.
A Sheltered CARMA Algorithm Using Cosseted Articulate For Implication Data

In this current scenario, the entire world depends upon the data which they hold over the internet and how these progressions towards the future is monitored. There is only one criterion to fulfill those needs of protecting the data of an individual or a business firm or a well-developing industry. Two steps are extremely mandatory to occupy, one is encryption and the latter one is decryption. To protect data encryption happens and to retrieve the same data decryption takes place. Advanced Encryption Standard- derives the security to ultimate data protection where the processing of this would not be revealed. In AES- no data gets hacked till-date, safety and security are the deepest truth formulated using this algorithm. Edge device data are secured with the AES algorithm using Fog computing by altering the data with encryption and retrieve by decryption. The combined version of the DWT and LSB model form the model. The proposed CARMA model uses a zigzag Matrix and the final results are compared with the original AES.
Robust Smart Face Recognition System Based on Integration of Local Binary Pattern (LBP), CNN and MTCNN for Attendance Registration

Face recognition is one of the most effective image-processing applications and is essential in the technological era. The identification of the facial image is a current problem for authentication purposes, particularly in the case of student attendance. The design of this system aims to digitally replace the outdated method of collecting attendance with handwritten records. The methods now used to take attendance are complicated and time-consuming. Hence, this method is suggested as a solution to all of these issues. The suggested method uses the integrated benefits of Local Binary Pattern(LBP), CNN, and MTCNN. Attendance reports will be created and maintained in excel format following face recognition. The created system is less expensive to install and requires less work.
Diagnosis of Depressive Disorder Based on Facial and Text-Based Features Using Effective Techniques

Depression, which is one of the most common mental diseases in the world, has a significant and detrimental impact on society as a whole. Early detection of depression via the use of automated methods and assessments is very necessary in order to improve one's own physical state. Analysis of people's feelings is quickly becoming one of the most useful techniques we have for spotting signs of depression. Textual and natural language processing methods are used in sentiment analysis, with the end goal of extracting views and feelings hidden within the data. In this research, we examine the use of computers and methodologies for sentiment analysis, which will provide an effective way for diagnosing and monitoring mental illnesses like depression. Specifically, we focus on how these two areas may work together. This project shows and explores prospective techniques for emotional technologies that combine sentiment analysis with the capability of detecting and measuring depression. Additionally, a concept design for an integrated multimodal system for the diagnosis of sadness is given. This system makes use of sentiment analysis and affective computing approaches. This project had the goal of developing an application that could run on several platforms, be hosted in the cloud, and maintain high confidentiality while being independent of its operator. As our training data, we made use of word classifications in conjunction with specified characteristics of human facial motions. The combination of these two strategies results in a performance that is more effective overall. The correctness of the models that were detailed in the step before this one is tested using a variety of different algorithms, and the results of these tests will give us with a rough sketch of the right technique. Additional optimization may bring to a significant increase in accuracy.
Analysis of Breast Cancer Recognition in Histopathological Images using Convolutional Neural Network

The majority of women around the world will be diagnosed with breast cancer in their lifetime, making it the second leading cause of mortality among females. On the other hand, it is feasible to be cured of cancer if it is diagnosed at an early stage and given the appropriate treatment. By enabling patients to obtain timely therapeutic treatment, early breast cancer identification has the potential to significantly enhance both the prognosis and the odds of survival for those who are diagnosed with the disease. In addition, accurate categorization of benign tumors might assist patients in avoiding therapy that is not required. The advent of personalized medicine has resulted in a significant rise in the amount of work that must be done by pathologists as well as an increase in the complexity of digital pathology in cancer detection. Diagnostic protocols must now place equal emphasis on both efficiency and accuracy. Histopathology evaluations have been found to benefit from improvements in efficiency, accuracy, and consistency brought about by the application of computerized image processing technologies, which can also give decision support to assure diagnostic consistency. We demonstrate that convolutional neural networks, often known as CNN, can be an efficient method for identifying breast cancer histopathology images, and we test CNN's effectiveness as a binary predictor in the field of breast cancer diagnosis by using whole slide imaging. The model is trained using the data that can be found in the Kaggle archive. The suggested method is contrasted with other approaches already in use by employing a wide variety of achievement evaluation indicators.
Automated Detection of Breast Cancer using Artificial Intelligence Systems

The second leading cause of death for women worldwide remains breast cancer. In particular as they age, all women have a higher risk of acquiring breast cancer. Populations range in their incidence of various forms of breast cancer, and they also differ in their risks of developing breast cancer in general. Hence, it is crucial to determine the degree of the malignancy as soon as possible. Artificial intelligence may be able to classify breast cancer with competence levels on par with those of a doctor. This paper proposes the detection of breast cancer through a deep learning algorithm-based approach that involves segmentation, feature extraction, and grading. A soft computing technique is developed to categorize tumours as benign or malignant using breast histopathology images. To determine the exact grades of breast cancer, the initiation technique uses K-mean clustering-based segmentation, feature extraction, and classification. The segmented region yielded attributes such as correlation, homogeneity, contrast, LBPs (local binary patterns), and energy coefficients. In order to establish which network is better at recognizing the diagnosis of breast malignancy, the Artificial neural network classifier, deep learning architectures of AlexNet and GoogLeNet are contrasted. The AlexNet classification methodology produces accurate solutions than other methods by obtaining 100% accuracy. The proposed enhanced segmentation and classification techniques may help the automated diagnosis system to reduce the number of erroneous diagnoses and increase prediction performance. It could therefore aid clinicians in obtaining an additional opinion and in the early detection of diseases.
A Hybrid Model for Pupil Detection Using FPGA and CPU by Iterative Circle Fitting

Pupil detection is a critical requirement in security applications, ocular characterization, and automated automotive systems. An increasing number of applications are being developed that use the pupil response as a measurement of cognitive function and physiological stress. This paper proposes a novel approach to pupil detection that integrates an image processing system into the Field Programmable Gate Array (FPGA) hardware of a micro controller. The FPGA is programmed to segment the pupil contour based on the pixel intensities and the CPU is used to run a circle fitting model to predict the coordinates of the pupil. This model is evaluated with a private data set and a public data set, and it outperforms the stat-of-the-art models achieving a pupil segmentation accuracy of 0.9919 and a precision of 0.9930. This model is appropriate for deployment in real-time settings for several security and surveillance applications.
A Robust and Improved sparrow search algorithm for optimization in wireless sensor network

The emergence of swarm intelligence approaches has resulted in the development of a workable theoretical computational approach to the simulation, modeling, and optimization of complex systems. This research proposes using a Modified sparrow search algorithm (MSSA) to optimize the coverage WSN. There are three areas of the algorithm that have been improved. We first use the LHS approach to produce the initial population. To further improve the method's convergence efficiency, we offer new optimization equations based on an adaptively adjusted sine cosine algorithm and the Lévy flight strategy. Finally, a unique mutation disturbance mechanism is implemented at the conclusion of every iteration to maximize the individuals with low fitness in the natives. Experimental findings from 13 benchmark functions reveal that the proposed improved method has benefits in convergence pace, and strength, as shown by the stability of its average value and the shortness of the time it takes to reach the optimal solution. In this study, we provide a state-of-the-art optimization framework for the WSN coverage issue using swarm intelligence algorithms and then analyze the efficacy of nine methods.