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The detection of chest diseases from X-ray images is a crucial aspect of medical diagnostics, playing a vital role in the early identification and treatment of respiratory conditions. This review paper gives a general overview of how machine learning algorithms are used to identify chest diseases from X-ray pictures. It emphasizes the importance of accurate and timely diagnosis of chest diseases, while acknowledging the challenges faced by conventional diagnostic methods. The review highlights the potential of machine learning as a promising approach to enhance diagnostic accuracy and efficiency in this field. It explores the utilization of both handcrafted features and deep learning-based approaches for extracting informative features from chest X-rays. Furthermore, it discusses the adoption of various machine learning algorithms, including support vector machines, random forests, and convolutional neural networks, for effective detection of chest diseases. The review paper also recognizes the significance of training and validation strategies in ensuring the robust development of models. Additionally, it addresses the potential impact of the proposed methodology on chest disease detection and patient outcomes. The review paper discusses challenges and limitations associated with this approach, such as data availability, interpretability, and ethical considerations. In summary, this extensive analysis strives to make a valuable contribution to the field by offering insights into cutting-edge machine learning techniques, recognizing existing challenges, and proposing potential avenues for future research. [ABSTRACT FROM AUTHOR]

Orthopedic implant replacement surgery is a procedure that involves the removal of a damaged or diseased joint and the placement of a new, artificial joint. The new joint is made of metal, plastic, or ceramic materials that are designed to mimic the function of the natural joint. Orthopaedic implant replacement surgery is a highly successful procedure that can significantly improve the quality of life for patients who suffer from chronic joint pain. However, as with any surgery, there are risks associated with ortho implant replacement surgery, including infection, blood clots, and nerve damage. This may lead to aseptic loosening which will become the main cause for surgeries to happen for the second time. Therefore, it is crucially important to diagnose it clearly during the pre-operative and post-operative phases. Our main objective is to assist surgeons in identifying these illnesses accurately during the process, to avoid more serious stages. This paper reviews the methods and approaches for Orthopaedic implant replacement surgery using Computer-Aided Design (CAD), Convolutional Neural Networks (CNN), Augmedics, SCORE-CAM and deep learning algorithms. During all the three phases, various technologies are utilized to improve patient wellness. [ABSTRACT FROM AUTHOR]

Crop diseases present a substantial threat to worldwide food security, emphasizing the critical importance of timely and precise disease detection to mitigate yield losses and support sustainable agriculture. This study introduces an innovative approach that harnesses the power of Machine Learning (ML) and the Internet of Things (IoT) to revolutionize the detection of crop diseases. Within this framework, IoT sensors strategically deployed across agricultural fields gather real-time environmental data, while high-resolution crop images are captured using drones and on-site cameras. ML models, including Convolutional Neural Networks (CNNs), harness this data to enable early and accurate disease detection. By integrating IoT capabilities, we can correlate disease outbreaks with environmental factors, thereby enhancing the decision-making process. The results are promising, consistently demonstrating high accuracy in disease detection and the potential for reducing the need for chemical treatments. This research paper signifies a significant step towards promoting sustainable farming practices and enhancing global food security. [ABSTRACT FROM AUTHOR]

This paper's objective is to improve accuracy in plant leaf disease detection using Machine learning techniques such as K-Nearest Neighbors classifier (KNN) with a sample size of 10 and Novel Convolution Neural Network (CNN). This article consists of 2 groupsi.e Convolutional Neural Network (CNN) and K-Nearest Neighbors Classifier with a sample size of 10 for each group. G Power software is used to determine sample size with a pretest power value is 0.8 and alpha 0.05. The accuracy evaluated using both algorithms is compared and CNN appears to be the most significant with an accuracy of 92.48% and KNN with an accuracy of 74.14% with p=0.041. An Independent sample t-test has been used and analyzed with a confidence level of 95 percent. Results show that the CNN algorithm is significantly better than KNN in detecting plant leaf disease. [ABSTRACT FROM AUTHOR]

Multi-class image classification is one of the tasks of computer vision in which convolutional neural networks are widely used. In this paper, we create a novel ensemble learner by stacking InceptionV3, Xception, MobileNetV2, and ResNet50 convolutional neural network in an attempt to outperform the best performance of each individual models. In an ensemble learning algorithm, by combining multiple different models with different architectures, an error of prediction from a single model can be corrected by other models, thus reducing the likelihood of selecting a wrong prediction. The dataset that we used to do benchmarking is the Intel Image Classification dataset which consists of 6 classes that displays images of fully colored natural scenes. The training set consists of 14034 images, while the test set consists of 3000 images, all having 150x150 as their default size. The training process on all models involves reducing their learning rate when the performance plateaus, and early stopping if there aren't any significant improvements from an epoch to the next. The result is that each individual models have an average of 83% accuracy, yet our ensemble model managed to reach 88% on the test set. [ABSTRACT FROM AUTHOR]

The reality is that the population and number of automobiles on the street are growing day by day. The efficient monitoring of continuously increasing traffic is a critical challenge that traffic operators are facing. Traffic control is generally achieved using stationary video recorders, observers or manual counters. Hence, Unmanned Aerial Vehicles or drones are the high-quality answers to this trouble as they lessen the price of the set-up of numerous cameras and sensors. This paper's principal ideology is split into phases. The first one is the monitoring of traffic using a drone which helps in analyzing the traffic in a particular area and making interpretations. The second is fire detection and extinguisher. Here the drone is used to detect fire with the aid of a fire sensor as it helps the fire-fighters to analyze the affected area and prioritize their work. This project also up-skills in designing a quad-copter. Our motivation for this project lays both personal interests in a better understanding of object detection and academic research. A drone consists of a camera to cover a wider area which helps in traffic analysis. Here, the number of vehicles are estimated. Drones have recently emerged as an outstanding technology in the business market and are geared up with a camera, geo-positioning sensors, and communications hardware that may relay statistics. This project is implemented by using artificial intelligence and the Internet of Things for Fire Detection. To be more specific traffic detection is achieved using Deep Learning algorithm which is the Convolutional Neural Networks Algorithm using python. Fire sensors are mounted on the drone, and it flies on a flight path within the building and sends fire alert data to the system controller upon sensing a fire condition via fire sensor and also puts off the fire using the water pump. For fire detection the sensor used in the drone basically detects IR (Infra-Red) light wavelength that is emitted from the fire flame which helps fire-fighters to quickly assess and monitor dangerous areas which results in more safety and limits the time it takes to scan the whole area. [ABSTRACT FROM AUTHOR]

With the upcoming large-scale surveys like LSST, we expect to find approximately 105 strong gravitational lenses among data of many orders of magnitude larger. In this scenario, the usage of non-automated techniques is too time-consuming and hence impractical for science. For this reason, machine learning techniques started becoming an alternative to previous methods. In our previous work [1], we have proposed a new machine learning architecture based on the principle of self-attention, trained to find strong gravitational lenses on simulated data from the Bologna Lens Challenge. Self-attention-based models have clear advantages compared to simpler CNNs and highly competing performance in comparison to the current state-of-art CNN models. We apply the proposed model to the Kilo Degree Survey, identifying some new strong lens candidates. However, these have been identified among a plethora of false positives, which made the application of this model not so advantageous. Therefore, throughout this paper, we investigate the pitfalls of this approach, and possible solutions, such as transfer learning, are proposed. [ABSTRACT FROM AUTHOR]

Cervical cancer is one of the most known health problems faced by women around the world. Early detection of cervical cancer may reduce the mortality rate. Pap smear images are new techniques used for screening cervical cancer. This paper excludes the nucleus of pap smear images, and the resultant images are classified into seven classes based on the surrounding region nucleus. Automated features are extracted using three pre-trained convolutional neural networks (CNN). The resultant features are twenty-one. The principal component analysis reduces the dimensionality and selects the most significant features into ten features. These features are fed to two types of machine learning algorithms: support vector machine (SVM) classifier and random forest classifier. The support vector machine classifier achieved the highest accuracy for seven classes, reaching 93.1%. This method will help the physicians in the diagnosis of cervical cancer depending on the tissues, not the nucleus. Furthermore, the result can be enhanced using a huge amount of data. [ABSTRACT FROM AUTHOR]

After a deep questioner among students, faculty members and higher education experts. The demand for qualified engineers in the specific field as understandably gone down. The situation is grimmer for tier-2 and tier-3 colleges in India. The purpose of opting engineering course is not to get expertise in the particular course, instead to get a job in government sector with valid degree. The problem identified in the field of engineering education towards improving the quality in education is addressed with the help of exploratory data analysis. The dataset used in our experiment is collect from UCI machine Learning Repository having 33 variables and 1044 observations. The contribution made in the paper is to identify the vital attributes using single and multi-variant regression techniques. To perform prediction using Decision Tree, Random Forest, Support vector Machine and compare their performance in terms of classification accuracy and F-Score. In addition to that a convolution Neural Network (CNN) model is established in which, the vital attributes identified using regression techniques are provided as inputs and weights at each stage is estimated using Gradient Decent algorithm with step size 0.5. The classification accuracy from 63% is improved to 97% with the help of CNN model in 26664 iterations. The finding of the present research states that student having backlogs are less frequently opting for higher studies. [ABSTRACT FROM AUTHOR]

Detection of diseases in crops is an important aspect to deal with as it affects the productivity of the agricultural yield. The farmer may not realize that the crop is suffering from a disease by looking at the leaves. In this paper, we will be performing Convolutional Neural Networks (CNN) where data needs to be pre-processed and then we need to divide the data into training dataset and test dataset. We will use the tomato leaves dataset to predict whether a tomato plant is healthy or diseased. Finally, we will be giving a user input image and using this model we will be detecting whether the plant is healthy or diseased and which disease it is suffering from which will help in enhancing the life of a farmer by increasing his productivity. Based on this detected information the farmer will be cautious if the plant is diseased and take appropriate actions. [ABSTRACT FROM AUTHOR]

Aim: The main aim of this paper is to recognize mango leaf disease using convolutional neural networks in google co-laboratory. Materials and Methods: A dataset of 120 photographs of diseased and normal mango leaves was used to distinguish mango leaves using the CNN technique which is based on a deep learning algorithm. Results: The proposed 5D CNN model has a 98.67 % accuracy and 2D CNN has a 89.05% accuracy in leaf disease classification, so the novel 5D Convolution Neural Network algorithm appears to be better than 2D CNN standard deviation is 1. Conclusion: In mango leaf disease detection, the proposed novel 5D Convolution Neural Network based leaf disease recognition model shows less training time and high training accuracy. This demonstrates the adaptability of CNN models in real-time implementations. [ABSTRACT FROM AUTHOR]

In this paper, Convolutional Neural Network Segmentation (CNNS) by Dual-Interface Reign Architecture is proposed to figure out the condition of breast tumor. The existing novel approaches from machine learning may not be voluntarily useful as they require a huge training data bit with segmented pixels. This architecture is built upon a CNN and a dual mode network which is used to segment and can non-linearly maps the input data into a deep latent space respectively. the two paths of dual-interface networks are locality preserving learner and a conditional graph learner, both are used for the extraction of images and exploit the input features. The complemented dual-interface architecture is further used to improve the rate of classification. This proposed method can overwhelm the restriction of some benchmarks for the classification in breast imageries. In addition, to perform extensive error analysis, an analytical model is developed for mammograms and tested for varying pixel widths and input probabilities. The analytical model is validated through simulation. The results of our proposed method demonstrate that it can effectively detect the different kinds of mammograms in database by segmenting them more precisely. When comparing with previous works the precision is improved up to 5% and 8% than the GSA-ANN and ANN respectively. [ABSTRACT FROM AUTHOR]