Machine Learning Approach for Prediction of the Online User Intention for a Product Purchase

The deployment of self-learning computer algorithms that can automatically enhance their performance via experience is referred to as machine learning in ecommerce and is a crucial trend of the retail digital transformation. Machine learning algorithms can be unambiguously trained by analysing big datasets, identifying repeating patterns, relationships, and anomalies among all of this data, and creating mathematical models resembling such associations. These models are improved when the algorithms analyse ever-increasing amounts of data, providing us with useful insights into specific ecommerce-related events and the links between all the variables that underlie them. A tool that has been quite effective in studying current affairs, predicting future trends, and making data-driven decisions. The present work investigates the implementation of machine learning algorithms to predict the user intention for purchasing a product on a specific store's website. An Online Shoppers Purchasing Intention data set from the UC Irvine Machine Learning Repository was used for this investigation. In this study, two classification-based machine learning algorithms i.e. Stochastic Gradient Descent (SGD) algorithm and Random Forest algorithm were used. SGD algorithm was used for first time in prediction of the online user intention. The results showed that the Random Forest resulted in the highest F1-Score of 0.90 in contrast to the Stochastic Gradient Descent algorithm

Potable Water Identification with Machine Learning: An Exploration of Water Quality Parameters

In this research, we aim to determine the water potability using three machine learning classification algorithms: decision tree, gradient boosting and bagging classifier. These algorithms were trained and tested on a dataset of water quality measurements. The outcomes of the experiment showed that the gradient boosting algorithm achieved the highest F1-score of 0.78 among all the algorithms. This indicates that the gradient boosting algorithm was most effective in correctly identifying both the safe and contaminated water samples. The results of this study demonstrate that gradient boosting is a promising approach for determining water potability and can be used as a reliable method for water quality assessment

Resource Allocation Energy Efficient Algorithm for H-CRAN in 5G

In today's generation, the demand for data rates has also increased due to the rapid surge in the number of users. With this increasing growth, there is a need to develop the next fifth generation network keeping in mind the need to replace the current 4G cellular network. The fifth generation (5G) design in mobile communication technology has been developed keeping in mind all the communication needs of the users. Heterogeneous Cloud Radio Access Network (H-CRAN) has emerged as a capable architecture for the newly emerging network infrastructure for energy efficient networks and high data rate enablement. It is considered as the main technology. Better service quality has been achieved by developing small cells into macro cells through this type of network. In addition, the reuse of radio resources is much better than that of homogeneous networks. In the present paper, we propose the H-CRAN energy-efficient methods. This energy-efficient algorithm incorporates an energy efficient resource allocation management design to deal to heterogeneous cloud radio access networks in 5G. System throughput fulfillment is elevating by incorporating an efficient resource allocation design by the energy consumption model. The simulation results have been demonstrated by comparing the efficiency of the introduced design with the existing related design

Real time COVID-19 facemask detection using deep learning

The COVID-19 pandemic is producing a global health pandemic. According to the World Health Organization (WHO), the utmost effective protection is to wear a face mask in crowded regions/areas. During this pandemic, it is compulsory for every person to wear a mask and maintain social distancing. In the field of Image Processing, Convolutional Neural Networks (CNNs) have risen to prominence as the most common type of image realization/recognition model. Our project's purpose is to research and assess Machine Learning (ML) technologies for identification and recognition of people wearing face masks in any pre-recorded videos, photos, or in actual-time (real- time) circumstances. Our project aims to create a real-time Graphics User Interface based Automated Facial Recognition as well as Mask Detection System. The algorithms used in the proposed methodology are Principal Component Analysis (PCA) and the HAAR Cascade Algorithm. Finally, the result is indicated by a “GREEN” color rectangle box, which would be drawn around the section of the face, which indicates that the person on the camera is wearing a mask, or a “RED” color rectangle box, which indicates that the person on the camera is not wearing a mask. This model achieves 99% accuracy

Heart health prediction using web application

It is said that the “heart is health” heart acts as a valve that pumps oxygenated and deoxygenated to and from the lungs its one of the most vital organs of the body and very sensitive and vulnerable as well. It has been observed that nowadays due to changes in lifestyle, irregularities, carelessness and extensive workload are the main reason for heart attacks in many people below 40 years of age and they are prone of are getting heart attacks, at least 25% of Indians and 1 out every 5 heart attack cases are aged below 40, in addition, this percentage reaches till 75% if we consider people age threshold id 50. &nbsp

A survey on human activity recognition using CNN and LSTM

Human Activity Recognition (HAR) plays a massive role inside the everyday life of people due to its capability to investigate tremendous high-degree data approximately human interest from wearable or stationary devices. An extensive quantity of studies has been completed on HAR and numerous techniques primarily based on deep gaining knowledge of and device analyzing were exploited through the research community to categorize human sports. The principle motive of this evaluation is to summarize contemporary works based on a big range of deep neural networks structure, mainly convolution neural networks (CNNs) for human hobby reputation. The reviewed systems are clustered into 4 classes relying on the use of input devices like multimodal sensing gadgets, smart phones, radar, and vision devices. This assessment describes the performances, strengths, weaknesses, and the used hyper parameters of CNN architectures for every reviewed device with an evaluate of available public statistics property in addition, a communicate with the cutting edge demanding situations to CNN based HAR systems is furnished. sooner or later, this assessment is concluded with a few functionality future guidelines that might be of outstanding assistance for the researchers who would really like to make contributions to this subject

Automated medical image classification using deep learning

Medical imaging is extremely important in the domain of medicine. Image classification is now utilized to distinguish aberrant tissues from healthy tissue in brain imaging. The brain tumor is identified from MRI images by using some classification techniques, where the area of the tumor as well as the tumor size is detected. Automatic tumor detection using brain MRI is efficient and time- saving, assisting the neurologists in diagnosis.  Tumors can increase the risk of  cancer,  which  is  the  most  common  cause  of  death  or major cause of mortality worldwide. To detect brain tumors at the moment, effective automation of tumor detection is essential. Marker based Watershed algorithm is a typical segmentation technique which is used for identifying brain tumors. For brain tumor detection, we performed marker based watershed classification on MRI images with the use of gray scale images, then   by noise removal and morphological operations. The steps in the methodology are as follows:  Gray-level and sharpening was used in the pre-processing, and the image was segmented using thresholding as well as the marker based watershed algorithm, and the CNN was used for classifying the images. Finally, the tumor's location and size were determined

A human activity recognition using CNN and long term short term memory

Human activity recognition aims to work out the activities performed by someone in a picture or video. Examples of actions are running, sitting, sleeping, and standing. Complex movement patterns and harmful occurrences like falling may be a part of these activities. The suggested ConvLSTM network can be created by successively combining fully connected layers, long immediate memory (LSTM) networks, and convolutional neural networks (CNN). The acquisition system will pre calculate skeleton coordinates using human detection and pose estimation from the image/video sequence. The ConvLSTM model builds new controlled features from the raw skeleton coordinates and their distinctive geometric and kinematic properties. Raw skeleton coordinates are utilized to generate geometric and kinematic properties supported by relative joint position values, joint differences, and their angular velocities. By utilizing a multi-player trained CNN-LSTM combination, novel spatiotemporal directed features can be obtained. The classification head with completely connected layers is then utilized. The suggested model was tested using the KinectHAR dataset, which consists of 130,000 samples with 81 attribute variables and was compiled using the Kinect (v2) sensor Experimental data is used to compare the performance of independent CNN and LSTM networks

Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network

chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge of each and every entity instead of majority voting of decision. The proposed method has been verified over a large set of data experimentally and observed that there is significant improvement with proposed ensemble approach in compare to individual recognizer

Business analysis and modelling of flight delays using artificial intelligence

In the current day, with the growth of technology the number of people travelling by flights has increased. Consistently a significant number of flights are delayed or crossed out because of numerous of reasons. These delays bother travelers. These delays also cost a lot to the aircraft organization. Flight delays negatively affect carriers, air terminals and travelers. There are different methodologies used to manufacture flight delays expectation models from the Data Science point of view. The key resource of a flight includes aircraft, cockpit crew and cabin crew. For purposes of dispatching resources effectively, the three resources may be distributed independently. If the initial flight of a flight plan is delayed due to bad weather or other factors, it may result in the delays of the directly downstream flights that need to await its resources. If the delays continue to spread to the lower downstream flights, it may result in large area delay propagation. The method proposed here introduces and summarizes the initiatives used to address the flight delay prediction problem, according to scope, data and computational methods, giving special attention to an increasing usage of machine learning methods