Reviewerly/exHarmony huggingface dataset:
Yolov5 AI Deep Learning model driven Nuclear Pleomorphism Grading on Breast Cancer Pathology WSI for Nottingham Cancer Grading

Breast cancer is the second largest cancer caused in the world due to the uncontrollable growth in breast cells. Nottingham Grading is the internationally acceptable system to grade breast cancer. Nuclear pleomorphism is one of the breast cancer biomarkers for computing Nottingham grading. Pathologists grade nuclear pleomorphism on breast cancer glass tissue slides using a conventional microscope which is time consuming and has considerable inter-observer variability between pathologists. The paper proposed an Artificial Intelligence (AI) deep learning model to grade grade1, grade2, grade3 nuclear pleomorphism on breast cancer whole slide images (WSI). The proposed Yolov5 model is trained and tested on 1,30,000 WSI tiles having around two lakh annotations. The accuracy of the model is mAP 0.89. The proposed model saves the time and reduces the workload of the pathologist and also helps them to produce accurate results.
Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system.
Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events.
Domain Classification for Marathi Blog Articles using Deep Learning

Nowadays the exponential growth of online content, particularly in the form of blog articles is tremendous, the need for effective techniques to automatically categorize them into relevant domains has become increasingly important. To overcome the challenges the domains like natural language processing (NLP), machine learning (ML) and deep learning (DL)are being working as booster effect to emerge out with solutions. In this proposed system methodology-based NLP and DL domain the long short-term memory (LSTM) classifier for domain classification and compared the existing multiclass classification techniques with having accuracy around 94% and 91% by long short-term memory (LSTM) model using two different data sets one is Marathi new article and another one Financial article data set. The proposed model is being compared with multiple other models like naïve bayes (NB), XGBoost, support vector machine (SVM) and random forest (RF). The final estimated result achieved is best combination of dataset and deep learning algorithm LSTM.
Ensemble-Based Machine Learning Approach for Real-Time Person Counting in an Instant Attendance System

Real-time attendance systems have become indispensable in various domains, including educational institutions and workplaces, as they automate attendance tracking and improve efficiency. This paper introduces a robust real-time attendance system that combines OpenCV and the You Only Look Once (YOLO) model. By integrating computer vision and deep learning techniques, the system achieves accurate and rapid face detection and recognition. Our proposed system utilizes OpenCV, a powerful computer vision library, to capture video streams from cameras. The YOLO model, a cutting-edge real-time object detection algorithm, is employed to identify and localize faces within the video frames. Thanks to YOLO's efficiency, the system ensures real-time processing, enabling seamless attendance recording. To enhance accuracy, the system employs a two-step approach consisting of face detection and face recognition. During the face detection phase, the YOLO model detects bounding boxes around faces. Subsequently, the system matches these detected faces against a pre-existing database of enrolled individuals using face recognition techniques. To improve performance, transfer learning techniques are applied to fine-tune the YOLO model on a diverse dataset containingvarious face images. This adaptation process ensures high precision and recall rates, even in challenging conditions such as varying lighting and occlusion. Experimental results demonstrate the effectiveness of the proposed real-time attendance system, achieving a high accuracy rate suitable for practical applications. Its real-time performance allows for seamless integration into existing attendance management workflows, resulting in time savings and improved administrative processes. The core intention of this paperwork is to develop a GUI page using Python programming, which will display the number of students who are present and number of students who are absent along with the number of students when section details are entered manually. In addition to this, pictures of the students who are present are displayed on the framework. It is called the real-time face detection which is very beneficial for managingacademic activity.
A New Approach for Product Quality Check Based on Social Networks Opinions Analysis

In this paper, we aim to enhance the relevance of e-commerce web sites by a prior quality checking of proposed products. This checking is done by analyzing social networks and YouTube videos comments.&#x0D; To achieve this objective, we have broken down the work into a few steps. The first one consists in scraping text from social networks groups and storing it in a NoSQL graph database. Each scraped word is linked to one or many reactions that are coming from the social network. Therefore, we can utilize the database as a knowledge source that associates each set of terms with a specific type of reaction: positive, negative or neutral. Afterwards, we use a TF-IDF based filtering method to keep only relevant words and eliminate those which are a connected to all reactions. The advantage of this stage is the presence of a knowledge source that can be used for product quality checking.&#x0D; In the e-commerce web site, data are coming from multiple e-commerce websites. The latter, offer products without quality checking. Concretely, we aim to allow users to check quality by a simple check button which call an implemented web service using human reactions and comments. After evaluating our approach, we have obtained an accuracy of 0,75. This result means that our method gives a three quarter of chance to have a good product.
Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance.
Simulation Modelling of Cloud Mini and Mega Data Centers Using Cloud Analyst

Cloud Computing has now become a base technology for various other technologies including Internet of Things, Big Data Technologies and many other technologies, the responsibility of Cloud become critical in case of real time applications where the cloud services are required in real time. Delay in the response from Cloud may lead to serious consequences even loss of lives where the processes data from cloud must reach within predefined time interval. The performance of Cloud has experienced delays with the current infrastructure due to multiple issues in Traditional Cloud Network Model. The Paper suggests a proposed architecture Cloud Mini Data Centers simulated using Cloud Analyst to minimize the delays of Cloud Service delivery. The paper also simulate traditional cloud Network model using Cloud Analyst and provides a comparative study of both models.
Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch.
An Android-based Image Steganography Approach to Data Communication Security using LSB and Password-based Encryption

The study's ultimate goal is to develop an Android app dedicated to Image Steganography, a technique for concealing sensitive information behind seemingly innocuous photographs. This information hiding technique is essential because of its many potential uses. Symmetric encryption, asymmetric encryption, and steganography are all brought together in this method. The initial picture is encrypted using a symmetric technique in this procedure. After that, the encrypted picture is quietly placed using a least significant bits Steganographic approach to conceal the secret key that was encrypted using an asymmetric algorithm. The steganography algorithm's simple but effective security mechanism is a major benefit. Integrating a secret message into a seemingly benign source makes it very difficult to detect the hidden message without prior knowledge of its existence and the appropriate decryption algorithm. The privacy of the concealed data is protected by this feature. The planned Android app would provide a user-friendly interface for using picture steganography methods, making it simple for anybody to choose commonplace photos and secret messages for obfuscation. Both the original picture and the concealed message will be kept secret thanks to the app's robust encryption methods. The hidden message will be seamlessly included into the regular picture using advanced steganographic methods like the least significant bits approach. The creation of this software is motivated by a desire to provide an easily accessible solution for people who want safe communication through hidden messages or encrypted photos. The robust encryption and steganographic technologies, paired with the straightforward interface, will enable users to effectively protect sensitive information. This work will strengthen the area of data security and highlight the need of sophisticated encryption and steganography in modern digital communication.
ElegantYOLO: An Agile and Real-time Vehicle Detection System for Live Environments

Vehicle detection plays a crucial role in traffic monitoring systems, but it often faces challenges like occlusion, vehicle size, and shape variations. Existing systems struggle with misinterpreting parts of other vehicles as actual vehicles, leading to losses and distorted shapes. To address these issues, a new approach called ElegantYOLO is introduced in this study. ElegantYOLO combines elements from LittleYOLO-SPP and ShortYOLO-CSP while modifying baseline network layer depths using the improved Ghost Module Extended connection method to reduce computational costs. The model's learning capabilities are improved by incorporating spatial attributes through the concatenation of Spatial Pooling blocks. The study employs the Alpha-IoU as the bounding box loss function, minimizing the disparity between predicted and ground truth boxes. This enhances vehicle detection accuracy and robustness. Additionally, the study uses the slicing-aided hyper inference (SAHI) technique, which allows the lightweight backbone network to capture more detailed vehicle information by processing higher-resolution images. Through extensive testing on various datasets such as PASCAL VOC 2007, 2012, and MS COCO 2014, the proposed model not only excels in detecting small vehicles but also demonstrates improved detection accuracy across different environmental conditions. The performance of ElegantYOLO surpasses both LittleYOLO and ShortYOLO by achieving an almost 10% higher mean average precision (mAP). Specifically, the model achieves outstanding results on PASCAL VOC and COCO datasets, with mAPs of 96.45% and 79.28%, respectively. Moreover, the proposed technique significantly enhances accuracy while reducing detection time.
Detection of HIV by using Rough Set and Homotopy Analysis Method

The significant objective of this research is to recognize how to calculate the classification process using rough set theory (RST) for the Human immunodeficiency virus infection and acquired immune deficiency syndrome (HIV &amp; AIDS) symptoms dataset. RST has a multi-dimensional concept with multiple approaches. In this paper, our main objective is to find the symptoms of (HIV &amp; AIDS) using basic RST and Homotopy Analysis Method (HAM) to validate our claim using statistical techniques. We prefer RST &amp; HAM over other soft computing techniques and Mathematical Modelling as both RST and Homotopy Analysis (HAM) because RST can handle vague and imprecise data efficiently, and HAM is a suitable technique for finding analytical solutions. We have used the chi-squared test to validate our claim.
Learning to Detect Human Emotions in Digital World by Integrating Ensemble Voting Classifiers

Due to the expansion of world of the internet and the quick acceptance of platforms for social media, information is now able to exchange in ways never previously imagined in history of mankind. A social networking site like Twitter offers a forum where people may interact, discuss, as well as respond to specific issues via short entries, like tweets of 140 characters and fewer. Users may engage by utilizing the comment, like and share tabs on texts, videos, images and other content. Although platforms for social media are now so extensively utilized, individuals are creating as well as sharing so much information than shared before, which can be incorrect or unconnected to reality. It is difficult to identify erroneous or inaccurate statements in textual content autonomously and find emotions of people. In this paper, we suggest an Ensemble method for sentiment and emotion analysis. Different textual features of actual and Emotion and sentiment have been utilized. We used a publicly accessible dataset of twitter sentiment analysis that included total 48,247 authenticated tweets out of 23,947 of which were authentic positive texts labeled as binary 0s and 24,300 of which were negative texts labeled as binary 1s. In order to assess our approach, we used well-known (ML) machine learning techniques, these are Logistic Regression (LR), AdaBoost, Decision Tree (DT), SGD, XG-Boost as well as Naive Bayes. In order to get more accurate findings, we created a multi-model sentiment and emotion analyzing system utilizing the ensemble approach and the classifiers stated above. Our recommended ensemble learner method outperforms individual learners, according to an experimental study.
Modelling of Intelligent Object Detection and Classification using Aquila Optimizer with Deep Learning on Surveillance Videos

Object Detection (OD) in surveillance video is the way of automatically detecting and tracking object classes of interest within the video recording. It includes the application of a Computer Vision (CV) technique to analyze the video frame and identify the classes of objects or the presence of specific objects. Various OD techniques are used to find objects within the footage video. This algorithm analyzes the visual feature of the frames and employs Machine Learning (ML) approaches namely Deep Neural Network (DNN), to detect and track objects. It is worth mentioning that the accuracy and performance of OD in surveillance video depends on factors including the choice of algorithms and models, the availability of labelled training data, and the quality of the video frame for the specific object of interest. This study introduces a new modeling of Intelligent Object Recognition and Classification by employing Aquila Optimizer with Deep Learning (IODC-AODL) approach in Surveillance Video. The goal of the IODC-AODL technique is to integrate the DL model with the hyperparameter tuning process for object detection and classification. In the proposed IODC-AODL approach, a Faster RCNN method is enforced for the process of OD. Next, Long Short-Term Memory (LSTM) networking approach is implemented for the object classification process. At last, the AO approach is enforced for the optimum hyperparameter tuning of the LSTM network and it assists in improving the classifier rate. A widespread simulation sets are performed to exhibit the superior performance of the IODC-AODL approach. The experimental result analysis portrayed the supremacy of the IODC-AODL algorithm over other models.
Imputation Techniques in Machine Learning – A Survey

Machine learning plays a pivotal role in data analysis and information extraction. However, one common challenge encountered in this process is dealing with missing values. Missing data can find its way into datasets for a variety of reasons. It can result from errors during data collection and management, intentional omissions, or even human errors. It's important to note that most machine learning models are not designed to handle missing values directly. Consequently, it becomes essential to perform data imputation before feeding the data into a machine learning model. Multiple techniques are available for imputing missing values, and the choice of technique should be made judiciously, considering various parameters. An inappropriate choice can disrupt the overall distribution of data values and subsequently impact the model's performance. In this paper, various imputation methods, including Mean, Median, K-nearest neighbors (KNN)-based imputation, Linear Regression, Miss Forest, and MICE are examined.
Performance of Machine Learning Models in Predicting Sentiments of Post-Covid Patients

With the widespread use of social media platforms, sentiment analysis of user-generated content has become a crucial task in understanding public opinion and trends. In this paper, we compare the performance of three popular machine learning models, namely Random Forest, Support Vector Machine (SVM), and Logistic Regression, in predicting sentiments of post-COVID patients on social media tweets. The study utilizes a dataset of labeled tweets representing positive, negative, and neutral sentiments. The preprocessing of textual data involves tokenization, stop-word removal, and conversion to lowercase to create a suitable input for the models. We utilize Term Frequency-Inverse Document Frequency (TF-IDF) vectorization to transform the text data into numerical features. The sentiment labels are converted to numeric representations for model training and evaluation. The three machine learning models are trained and evaluated on the dataset using metrics such as accuracy, precision, recall and F1-score. The evaluation results are presented and analyzed for each model, providing insights into their strengths and weaknesses in predicting sentiments. The experimental results demonstrate that Random Forest achieves the highest accuracy and F1-score, closely followed by SVM, while Logistic Regression performs slightly lower in comparison. However, all three models exhibit strong predictive capabilities, and their performances vary depending on the specific sentiment class. The findings provide valuable information for researchers and practitioners seeking to employ sentiment analysis in social media monitoring and other related applications. Overall, this study contributes to the understanding of the capabilities of Random Forest, SVM, and Logistic Regression models in sentiment analysis of social media tweets, and offers valuable insights for selecting the most suitable model for specific sentiment prediction tasks.