Commonly used for tasks that may be performed on a network of remote servers or “processing units,” cloud computing is a popular platform for executing operations on virtual computers. In both the consumer and the provider realms, task scheduling is an important tool. Finding the best resource to complete a given work is a major challenge for any task scheduler. Thus, the researchers saw job scheduling as one of cloud computing's most basic challenges. Several methods have been proposed by academics to handle the optimization issue that is the optimum determination of scheduling criteria in cloud computing. This work presents a novel hybrid approach that combines the concepts of the Lion Algorithm with the Particle Swarm Optimization-(PSO) algorithm to address the aforementioned optimization problem. The research used both traditional parallel workloads and artificial workloads to perform its tests using the CloudSim toolset. The findings are compared to those produced by other similarly-structured, metaheuristic-based methods tested under the identical circumstances. The promising findings of our simulations suggest that our suggested method has real promise in this area. To reduce the time it takes to submit a particular process in a virtual environment, this study proposes improved versions of the PSO and LA algorithm, subject to user-required budgetary limitations. The results also indicate that, when applied to workflow issues operating on different virtual machines, improved versions of the PSO and LA algorithm outperform the original technique in terms of shorter schedule duration. 

Scheduling the Task of User in Cloud computing using Hybrid Procedure of PSO and Lion Algorithm

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.

human activity recognition using CNN and long term short term memory

Diabetic retinopathy (DR) diagnostic automation using AI is becoming more common. Diseases affecting the blood vessels in the retina, such as those produced by diabetes, are a primary cause of sightlessness and visual impairment worldwide. As a result, early screening and treatment of DR would benefit substantially from automated DR detection systems, preventing visual loss caused by DR. Over the last several years, many methods for identifying anomalies in retinal pictures have been presented by researchers. Traditional automated approaches for detecting diabetic retinopathy relied on manually extracted features from retinal pictures and a classifier for final classification. Diabetic retinopathy may be detected and classified in fundus pictures with the help of a deep learning approach suggested in this study. In this method, the network makes a prediction depending on the quality of the dataset it was trained on. In the first stage, OD (eye) and BV (blood vessel) segmentation are performed using separate U-Net models. The second stage involves applying transfer learning to the deep learning models in order to fine-tune them for improved performance in both the training and validation phases. We use partial data augmentation methods to evenly expand our training dataset. Compared to the sum of the separate models, the suggested weighted classifier performs the best. Additionally, the model is evaluated using the open-access dataset, which consists of 3662 pictures. The APTOS 2019 dataset served as the basis for the five groups. The suggested technique significantly recovers the presentation of DR detection for fundus pictures as assessed by a number of different metrics and compared to current methods to DR identification. 

U-Net based Segmentation and Transfer Learning Based-Classification for Diabetic-Retinopathy Diagnosis

Commonly used for tasks that may be performed on a network of remote servers or “processing units,” cloud computing is a popular platform for executing operations on virtual computers. In both the consumer and the provider realms, task scheduling is an important tool. Finding the best resource to complete a given work is a major challenge for any task scheduler. Thus, the researchers saw job scheduling as one of cloud computing's most basic challenges. Several methods have been proposed by academics to handle the optimization issue that is the optimum determination of scheduling criteria in cloud computing. This work presents a novel hybrid approach that combines the concepts of the Lion Algorithm with the Particle Swarm Optimization-(PSO) algorithm to address the aforementioned optimization problem. The research used both traditional parallel workloads and artificial workloads to perform its tests using the CloudSim toolset. The findings are compared to those produced by other similarly-structured, metaheuristic-based methods tested under the identical circumstances. The promising findings of our simulations suggest that our suggested method has real promise in this area. To reduce the time it takes to submit a particular process in a virtual environment, this study proposes improved versions of the PSO and LA algorithm, subject to user-required budgetary limitations. The results also indicate that, when applied to workflow issues operating on different virtual machines, improved versions of the PSO and LA algorithm outperform the original technique in terms of shorter schedule duration.

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals' daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.

Deep Learning in the Recognition of Activities of Daily Living Using Smartwatch Data.

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.

A survey on human activity recognition using CNN and LSTM

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A survey on human activity recognition using CNN and LSTM

human activity recognition using CNN and long term short term memory