Reviewerly/exHarmony huggingface dataset:
Whale Species Classifier Using Dorsal Fin And Lateral Body Characteristic

Most of the time whale classification is a huge problem because of the low availability of the training data. The researchers working on whale species identification have stated that manual recognition of the whales is not that easy because the whales do not surface very often, the only time they surface to the water is when they have to breathe through their blowhole. This is one of the many opportunities of manual recognition used by the researchers to capture an image of the whales. The dataset used in this paper is comprised of the images of whales containing the characteristic of ‘Dorsal Fin and Lateral Body’. In this Paper the classification of ‘Whale Species’ using the images of dorsal fins and lateral body is performed using a pre-trained model Mobile Net v2 based on CNN and ANN. The preliminary classification model made in this paper achieved the test accuracy of 87.15%.
The Necessity of Artificial Intelligence for Smart Environment: Future Perspective and Research Challenges

Smart environment is the intermixing of everydaytasks and services with modern technology and computers. Smart environments are the way of the future, and this trend can be extensively seen in the ever-growing involvement of the latest technology like Artificial Intelligence (AI) in everyday life. In the last few decades, the field of computer science known as artificial intelligence has become more popular, it is mainly con-cerned with building smart devices and applications which are able to carry out tasks that are typically thought to require human intelligence. Despite notable developments on the potential of AI and its features, the impact of this technology on everyday scenarios is rather unexplored. Conceptually it is claimed by research that AI can either offer a way to improve human skills and make life easier or could possibly pose a risk to human jobs. The main aim of this study is to comprehend the effect of artificial intelligence on the life of common people through a user’s perspective by understanding the current trends and pace of integration into the lives of the public. To achieve this, a qualitative investigation was undertaken using the Grounded Theory methodology, choosing several artificial intelligence specialists from different industries and universities.
Classification of Breast Cancer Diagnosis Using Machine Learning Algorithms

In the developing world, cancer is one of the major causes of death. Breast cancer has the largest share among all types of cancer among women in India [1] and worldwide [2]. Early diagnosis of this can help the patient to increase their chances of survival, as it can provide much needed treatment on time. This paper focuses on classification of breast cancer into benign and malignant using various machine learning methods namely Logistic Regression, Decision Trees, Random Forest, K Nearest Neighbor, Extreme Gradient Boosting and Support Vector Classifier on the basis of Breast Cancer Wisconsin (Diagnostic) dataset. We propose a comparison between these implementations and evaluate them on various parameters. This survey also analyses the effects of dimensionality reduction using Principal Component Analysis and Recursive Feature Elimination on a few of these algorithms.
An Effective Sentiment Analysis Classification for Opinion Prediction using Machine Learning Schemes

In the blooming of web technology, the rise of electronic commerce websites is rapidly increasing. Users started expressing their views similar to text reviews or comments. This research deals with the identification and extraction of the exact opinion of the customers across reviews, feedbacks, or comments. Pre-processing techniques are used to clean the dataset and the machine learning algorithm like k-nearest neighbor (KNN) is used as a solution to this problem, to have a good evaluation of sentiments (Positive, Negative, Neutral). In accumulation, the precision of the algorithm highly depends on the value of k and distance metric for classification. In our work we used k value of 6 and euclidean distance equation for classification of the KNN algorithm. The proposed KNN algorithm has been applied on publicly available standard review dataset repository, to show the exact nature and features based on customer’s opinions, and the accuracy of determining the feasibility of the k-nearest neighbor algorithm of 98% is predicted.
Implementing Health Records using Knowledge Graphs and BlockChain

Nowadays, a huge amount of patient records are being maintained, including medications, tests, treatments, immunisations and doctor details. However, having some of these records in papers is only possible. So, there is a growing interest towards digitalising healthcare records and maintaining healthcare data electronically. Healthcare data maintenance is essential to create a comprehensive view of patients and thereby improve patient engagement and health outcomes and suggests proactive measures to counter the rising health issues. The blockchain concept is implemented for this purpose, using which the authenticity is verified. It is done with the help of a high-level object-oriented programming language known as solidity. Furthermore, this process creates a smart contract for healthcare data. Thus, it provides a structure of data with inherent security qualities. In addition to the Blockchain, health records can be visualised in a graph known as a Knowledge Graph. Knowledge Graph is written in Neo4j browser using graph query language Cypher. This graph connects distinct quantities to represent corresponding data in a network.
Remote Health Prediction System Using Machine Learning Algorithms

The “Remote health prediction System employing Machine Learning Algorithms” uses machine learning in healthcare. Better diagnostic and treatment decisions require better medical facilities. Machine learning helps clinicians analyse massive and complex medical data. This information helps doctors treat patients. Thus, machine learning in healthcare improves patient happiness. This project integrates healthcare machine learning functions. Illness prediction using machine learning predictive algorithms can make healthcare smarter than diagnosis. Machine Learning Algorithms and libraries will build this project. Due to its rich library and usability, Python is recommended. syntax. We used different categorization approaches to train Machine Learning models and then chose the best model that avoided overfitting and underfitting and had low variance and bias.
Optimizing Virtual Machine Downtime in Live Migration using the Least Frequently Used Service-based Pre-copy Algorithm

Cloud computing has been considered the most reliable and safe technology among many cloud users. Virtual machine migration is a crucial component of cloud computing technology. A Virtual machine is transferred from one host machine to another during the migration process, which is accomplished by repeatedly copying the memory pages available in the virtual disk. The time required for the movement of secondary storage, main memory, process states, and network connectivity, as well as the maintenance of their accuracy, must be considered for the efficient migration of virtual machines from one physical host to another in the cloud service provider. The most robust mechanism for system maintenance, resilience system, balancing the load of the service provider, and power-saving is the live migration of virtual machines between the nodes in the data center. So many services are running in the virtual machines during live migration; thus, it needs to take place with the least amount of downtime possible to prevent service degradation. The memory pages in the virtual storage must be copied to the other host using the least frequently used service-based pre-copy algorithm to optimize the downtime. The effectiveness of the suggested solution was evaluated in comparison to OpenNebula and OpenStack, two cloud computing platforms. The least frequently used service-based pre-copy algorithm reduces the amount of virtual machine downtime during live migration.
Sentiment Analysis of Twitter Reviews using SVM, ANN and CNN Models

One of the most critical factors that an E-commerce considers when it comes to enhancing its customer experience is the availability of feedback. Through this study, we present an approach which can analyse the tweet characters to maximize the experience of the customers. The tweet features were mined using the word embedding and the n-gram approach. A classification model capable of identifying tweets in either positive or negative categories was proposed by SVM and ANN architectures. A CNN-based model was also developed to classify the tweets. The results of the study revealed that the model that was most accurate was the one that was used for analyzing the tweets. The results of the study revealed that the CNN-based model was more accurate than the SVM and ANN models when it came to analyzing the tweets. The results of the study also showed that the model was able to identify interesting associations between the various categories of tweets.
Hate Speech Analysis using Supervised Machine Learning Techniques

Hate speech is a serious issue that can have harmful consequences for individuals and groups. In an effort to combat this issue, automated methods for identifying and categorising hate speech in text and other media have been developed using machine learning and deep learning techniques. These methods involve training machine learning algorithms on large datasets of labelled text, audio or visual data, and then using these algorithms to identify and classify new, unseen examples of hate speech. Machine learning approaches can be effective at detecting and classifying hate speech, but they also have limitations and can be prone to errors, particularly if the training data is biased or if the algorithms are not carefully designed and evaluated. It is important to thoughtfully examine the ethical and social consequences of using machine learning to identify hate speech and to ensure that these methods are transparent, fair, and accountable.
The Comparative Study of CNN models for Breast Histopathological Image Classification

In Recent years, breast cancer has been considered as the prevalent type of cancer and second deadliest cancer. Histopathological image analysis is the gold standard for cancer diagnosis. Since CAD systems take less time compared to traditional approaches it is notable that automated cancer classification will be helpful for reliable and accurate diagnosis. Also CAD models assisted with deep learning techniques produce excellent results for classification of breast histopathological images. Convolutional Neural Network (CNN) models have achieved significant progress in medical image classification tasks including breast histology images. This paper makes a brief comparative study that illustrates performance of CNNs such as VGG-16, ResNet-50 and AlexNet for breast histological image classification. The study used BreakHis dataset for its model comparison. Further the paper illustrates application of data augmentation methods for the dataset as it improves the model accuracy. Also we extend our study to transfer learning methods and cross-check the results after using pre-trained models for breast histology image classification.
Human Activity Recognition For Analysing Fitness Dataset Using A Fitness Tracker.

The rise in eminence in the research based on Human Activity Recognition (HAR) due to its extensive scope in its application such as monitoring activities of daily life, fitness, security, and many more areas, is no mystery. In this project, we will be concentrating on the application of HAR in mobile health. The demand for HAR has increased in recent times due to the rapidly growing elderly, obese, and overall unhealthy population. We will be dealing with a Smartphone-based HAR system as a fitness tracker that uses high-dimensional sensors to collect fitness datasets and infer a confusion matrix for HAR in different activities to analyze the best fit in this work and predict the most accurate activity. It is crucial to select the appropriate features to support our research. We have used the Random Forest feature selection and the extracted data was analyzed using different classification techniques along with the previously mentioned scheme to generate the most accurate results and find a method that can be used by developers in the fitness tracker industry. Our data is a collection of 30 participants within an age bracket of 19 - 48 years performing six activities namely, Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, and Laying while carrying a waist mounted smartphone with embedded wireless sensors. These activities recorded certain movements, acceleration, velocity, and heart rate which was compiled into readable data and worked upon.
Comparative Analysis of Canny Edge Detection- Based Smart Traffic Control System Using Multi- Type Image Processing

In this paper, Using the Canny Edge Detection Algorithm, we are introducing a method to automate or regulate green traffic signal allocation time based on traffic conditions at the roadside. We are sending the most recent traffic picture to the program to use this strategy, and the application will extract edges. If there is more traffic, then there will be more white-colored edges, and if there is less traffic, then there will be fewer white-colored edges in the uploaded image. Empty edges will be black with a value of 0. We can determine the total amount of traffic by counting the non-zero white pixels; then, based on that information, we can decide how much time to give for the green signal. Green signal allocation time will be longer if there is more traffic, and shorter if there is less.
Web Information Retrieval using Vector Space Model and Docu-Tally Metric

Regardless of the type of data set, it is frequently challenging to sift through the vast amount of data that is available on the Internet as a result of technological improvements. To deal with challenges mentioned above, we have come up with a ranking approach which is computed using NLP and vector space model. The approaches used for information retrieval start with a basic machine learning model and progress to multi-stage architectures and frameworks like language modelling and term matching. The main goal of this work is to use a standard retrieval process to glean insights from large amounts of data, which is the problem we are aiming to solve. The method utilised in the study is latent semantic analysis, which takes advantage of the semantic aspects at play and can be used to glean insights from lengthy texts.
Imbalanced Breast Cancer Prediction Using CNN Through Multi-Net Framework

Cancer remains to be one of the most prevailing diseases in the world in which breast cancer remains unaddressed. There are several methods for identifying breast cancer, but still, breast cancer detection using automated algorithms remains a problem within the literature. Though several automated algorithms are used to identify breast cancer, the one with the highest accuracy needs to be identified. CNN abbreviated as Convolutional neural network, a fragment of a deep learning model, was created to accurately identify breast cancer. There are two objectives in this research where the introductory step is to look into assorted DL methods for cataloguing breast cancer images. Secondly, to identify the best feature extraction model. The proposed system, analysis of different performance metrics results that were obtained for the deep neural networks showed that other factors, such as transfer learning approaches and pre-processing techniques, may have an impact on the model’s ability to achieve improved prediction of breast cancer. Approaches like EfficientNet, Inception V3, VGG-16, and Modified VGG-19 have been used to identify the most effective results for the ImageNet database. In this experiment the experimental design for each model tested on breast cancer histopathology images is carefully analyzed and discussed.
Pc Automation Using Hand Gestures

The human computer interaction platform can be implemented in a variety of different ways because to the fact that webcams and other equipment, such as sensors, are both relatively inexpensive and readily available on the market. The use of gestures as a mode of communication between humans and machines is widely regarded as the most effective approach. The employment of hand gestures as a means of communication between humans and machines or computers is particularly helpful in achieving higher levels of information conveyance. The use of hand gestures as a form of nonverbal communication is something that can be put to use in a variety of contexts. Applications that make use of hand gestures have acquired a variety of alternative methodologies, including those that are backed by sensor technology and computer vision, according to research and survey reports. Research is expanding into new territory, particularly in the areas of pattern recognition and gesture recognition. Gestures of the hand can add a new layer of complexity to the operation of contactless computers. As a result, in this project, we produce software that shows a prototype of a system that is able to automatically recognize gestures and executes pc commands in response to certain motions. This software is shown as part of this project. Python is the language that we are utilizing to construct our solution, and we are taking advantage of integrated modules such as cv2 (OpenCV, or open source computer vision, is a collection of programming functions that is primarily geared toward real-time computer vision.) & Mediapipe (detects hand and fingers data points.)
A comparative analysis between single &amp; dual-handed Bangladeshi Sign Language detection using CNN based approach

In order to help the vast majority of the population learn sign language, specialists today frequently apply machine learning techniques. This project seeks to create a model that can recognize the letters in Bangladeshi Sign Language (BdSL) using a deep learning approach. This study is a comparison between the recognition of single-handed and dual-handed Bangladeshi Sign Language. Dataset, KU-BdSL is selected to train the single-handed BdSL and we selected dataset - BDSL 49 for dual-handed BdSL. We suggested pre-trained models of CNN based approach for the purpose of detection and classification. 30 different alphabets from single-handed BdSL and 36 different alphabets from dual-handed BdSL could be classified and recognized by our CNN models. Three pre-trained CNN models were employed. VGG16 outperformed the others by a wide margin. It correctly identified single-handed gestures with 98% accuracy. Regarding the ability to recognize dual-handed Bangladeshi Sign Language, VGG16 once again outperformed competitors with an accuracy of 90%.
Recognition and Processing of phishing Emails Using NLP: A Survey

Email is preferred by both individuals and businesses as one of the most important forms of communication. Despite the popularity of alternatives like electronic communications, mobile applications, and social networks, email usage and importance have been raising continuously. Phishing is the fraudulent practice of stealing any person’s confidential information, such as financial details, login information etc. In which they look completely authentic and genuine and extremely hard for victim to detect whether the mail is spam or not. Moreover, the number of phishing emails is growing daily. So, handling of phishing emails is a crucial research issue. NLP techniques are useful for training data to detect spam content. In this work we studied leading research areas in phishing email detection and features used in spam emails. Feature extraction and selection plays a key role in phishing email detection. This paper also explains how actually the phishing attack happens and obtained results including information about concepts of natural language processing and working procedure.
Automated Word Prediction In Telugu Language Using Statistical Approach

Automated word prediction commonly called as language modeling is the task of predicting the next Word. Word completion and word prediction phenomenally benefit the disabled users who use physical keyboard or virtual keyboards in desktops and handheld systems. The objective of this paper is to predict the next word prediction and sequence of words prediction in Telugu language sentences with stochastic approach. The N-gram linguistic models such as uni-gram, bigram, tri-gram, maximum likely hood estimation, Laplace and add1 smoothing have used for automatic completion of a sentence with the prediction of appropriate word to save time, keystrokes and misspelling. We have used large data corpus of Telugu language from Telugu Wiki pages, which has words from different domains to predict correct word.
Effective Approaches Of E-Commerce Product Recommendations

One of the well-known applications of recommender systems is the creation of playlists for audiovisual services, online retailers that recommend products, open online content recommenders as well as content recommendations for social networking sites. These programs can be configured to work an individual input, such as numerous inputs that may be discovered on both platforms, including news, books, and searches. Additional factors well-known recommendation systems for subjects such as eateries and dating websites. Journal articles, specialists, colleagues as well as financial services are all researched by recommendation systems. Therefore, this recommendation method, which directly compares users and products, cannot be used with a collaborative filtering model. Content acquisition and quantitative analysis form the basis of a content-based algorithm. Many current recommender systems that use content make recommendations depending on the analysis of textual information, as research on collecting and filtering textual information is advanced.
Emotion Analysis of Tweets

As technology is developing day by day messages, social media platforms became a platform of representing person’s life, thoughts and their behaviour. We all know this developing technology has more freedom to share their thoughts, personal feelings, what they are feeling at the current moment within a single second, within a single sentence on social media. There will be plenty of data available to know about a person behavior and this data can be used for detecting various emotions. Our project Emotion Analysis of tweets itself says that to detect a particular emotion held by a tweet. That is done by analyzing expressed opinions, images, sentiments, and other activities. In our case we took mainly posted opinions or tweets as input to implement our NLP (Natural Language Processing) model. This project is about constructing machine learning models to find which model is predicting emotion of tweets with highest accuracy.