Radio-Controlled Intelligent UGV as a Spy Robot with Laser Targeting for Military Purposes

The main objective of this unmanned ground vehicle is to deal with the security issues like terrorist activities across the border and in various remote combat missions by reducing the involvement of soldiers. This unmanned ground robot comprises a wireless high-definition camera that can transfer live streams from the robot to headquarters using Wi-Fi. The robot’s movement can be controlled with two modes; one of them is a radio controller working on 2.4 GHz frequency with seven independent channels. Secondly, its movement can also be controlled using a Python-based GUI application. Nowadays, different techniques have been used for face recognition; in our remotely piloted robot, we have used Haar-cascade classifiers in combination with the LBPH algorithm to implement real-time facial recognition. The robot uses a rack and pinion driving mechanism and an ATMEL Cortex-M3 CPU as a controller with 32-bit/s processing speed. In addition, a laser is installed on the turret to shoot the targets down, which can be used in an autonomous mode based on facial recognition results, or it can be used manually either through an RF controller or Python-based GUI. The turret moves in 2-DOF with the help of metallic geared servo motors. Both servo motors can rotate up to 180°. The driving mechanism of the robotic tank is just like DDR, with one difference, the two DC gear motors of the robot are connected diagonally

Optimization of DevOps Transformation for Cloud-Based Applications

Rapid software development is critical for meeting company objectives and competing more effectively in the competitive IoT infrastructure. DevOps is a growing technique that enables enterprises to provide high-quality software capabilities through automation, to improve team communication, and to increase efficiency across the software product lifecycle. Research problem: Due to the increased demand for new products and technologies, a huge overwork shifted on the organizations for introducing software with pace and to become stable to compete with others. Due to this, the majority of organizations prefer an automated system for product development and require cloud-based applications. The git version control system is used for version management and Docker is used to package code and provide libraries. AWS services are leveraged to deploy an application as a cloud. Jenkins is used as a CI/CD pipeline to manage various phases of development and to make the development process continuous. The ELK stack is used to monitor and visualize the execution of code. In light of the findings, DevOps is an efficient method for cloud application deployment and resource selection based on the relative importance of each optimized objective in terms of value parameters such as cost, memory, and CPU capacity, and that the method can be tailored to specific application requirements. The findings of this analysis indicate that an application can be deployed to the cloud using DevOps techniques. The proposed approach cost 60% less at full weight 1.0 and 11.3% less with no weight compared to the benchmark solution’s 15.078

An IoT-Based Framework for Personalized Health Assessment and Recommendations Using Machine Learning

To promote a healthy lifestyle, it is essential for individuals to maintain a well-balanced diet and engage in customized workouts tailored to their specific body conditions and health concerns. In this study, we present a framework that assesses an individual’s existing health conditions, enabling people to evaluate their well-being conveniently without the need for a doctor’s consultation. The framework includes a kit that measures various health indicators, such as body temperature, pulse rate, blood oxygen level, and body mass index (BMI), requiring minimal effort from nurses. To analyze the health parameters, we collected data from a diverse group of individuals aged 17–24, including both men and women. The dataset consists of pulse rate (BPM), blood oxygen level (SpO2), BMI, and body temperature, obtained through an integrated Internet of Things (IoT) unit. Prior to analysis, the data was augmented and balanced using machine learning algorithms. Our framework employs a two-stage classifier system to recommend a balanced diet and exercise based on the analyzed data. In this work, machine learning models are utilized to analyze specifically designed datasets for adult healthcare frameworks. Various techniques, including Random Forest, CatBoost classifier, Logistic Regression, and MLP classifier, are employed for this analysis. The algorithm demonstrates its highest accuracy when the training and testing datasets are divided in a 70:30 ratio, resulting in an average accuracy rate of approximately 99% for the mentioned algorithms. Through experimental analysis, we discovered that the CatBoost algorithm outperforms other approaches in terms of achieving maximum prediction accuracy. Additionally, we have developed an interactive web platform that facilitates easy interaction with the implemented framework, enhancing the user experience and accessibility

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images

Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network

Brain tumor (BTs) is considered one of the deadly, destructive, and belligerent disease, that shortens the average life span of patients. Patients with misdiagnosed and insufficient medical treatment of BTs have less chance of survival. For tumor analysis, magnetic resonance imaging (MRI) is often utilized. However, due to the vast data produced by MRI, manual segmentation in a reasonable period of time is difficult, which limits the application of standard criteria in clinical practice. So, efficient and automated segmentation techniques are required. The accurate early detection and segmentation of BTs is a difficult and challenging task in biomedical imaging. Automated segmentation is an issue because of the considerable temporal and anatomical variability of brain tumors. Early detection and treatment are therefore essential. To detect brain cancers or tumors, different classical machine learning (ML) algorithms have been utilized. However, the main difficulty with these models is the manually extracted features. This research provides a deep hybrid learning (DeepTumorNetwork) model of binary BTs classification and overcomes the above-mentioned problems. The proposed method hybrid GoogLeNet architecture with a CNN model by eliminating the 5 layers of GoogLeNet and adding 14 layers of the CNN model that extracts features automatically. On the same Kaggle (Br35H) dataset, the proposed model key performance indicator was compared to transfer learning (TL) model (ResNet, VGG-16, SqeezNet, AlexNet, MobileNet V2) and different ML/DL. Furthermore, the proposed approach outperformed based on a key performance indicator (Acc, Recall, Precision, and F1-Score) of BTs classification. Additionally, the proposed methods exhibited high classification performance measures, Accuracy (99.51%), Precision (99%), Recall (98.90%), and F1-Score (98.50%). The proposed approaches show its superiority on recent sibling methods for BTs classification. The proposed method outperformed current methods for BTs classification using MRI images