The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service‐based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

Impaired renal function poses a risk across all age groups. Because of the global shortage of nephrologists, the growing public health concern over renal failure, and technological improvements, there is a demand for an AI-driven system capable of autonomously detecting kidney abnormalities. Chronic kidney disease, commonly known as chronic renal failure, is characterized by a progressive decline in kidney function. Renal failure can be caused by a variety of reasons, including cysts, stones, and tumors. Chronic kidney disease may not have apparent symptoms at first, resulting in instances staying untreated until they reach an advanced state. Tumors are dense clumps of tissue that can cause direct injury to glands, spinal cells, and other organs. The presence of a substantial number of solids in the digestive tract causes kidney stone disease, also known as urolithiasis. This study used a deep learning model to detect kidney illnesses to solve the global scarcity of urologists. The project entailed obtaining and annotating a large dataset of 12,446 CT whole abdomen and urogram images, with an emphasis on kidney stones, cysts, and tumors, which are the most common types of renal illness. The dataset was divided into four categories: cyst, tumor, stone, and normal. Data was collected from several hospitals in the Dhaka area. This work presents an innovative and customizable platform for the clinical diagnosis of kidney diseases such as tumors, stones, and cysts. Our YOLOv8 model’s enhanced accuracy opens up new possibilities for identifying kidney cysts, stones, and tumors. We used criteria like accuracy, precision, recall, F1 score, and specificity to evaluate its performance. The network attained an accuracy rate of 82.52%, 85.76% precision, 75.28% recall, 75.72% F1 score, and 93.12% specificity.

The Internet of Things (IoT) cyberattacks of fully integrated servers, applications, and communications networks are increasing at exponential speed. As problems caused by the Internet of Things network remain undetected for longer periods, the efficiency of sensitive devices harms end users, increases cyber threats and identity misuses, increases costs, and affects revenue. For productive safety and security, Internet of Things interface assaults must be observed nearly in real time. In this paper, a smart intrusion detection system suited to detect Internet of Things‐based attacks is implemented. In particular, to detect malicious Internet of Things network traffic, a deep learning algorithm has been used. The identity solution ensures the security of operation and supports the Internet of Things connectivity protocols to interoperate. An intrusion detection system (IDS) is one of the popular types of network security technology that is used to secure the network. According to our experimental results, the proposed architecture for intrusion detection will easily recognize real global intruders. The use of a neural network to detect attacks works exceptionally well. In addition, there is an increasing focus on providing user‐centric cybersecurity solutions, which necessitate the collection, processing, and analysis of massive amounts of data traffic and network connections in 5G networks. After testing, the autoencoder model, which effectively reduces detection time as well as effectively improves detection precision, has outperformed. Using the proposed technique, 99.76% of accuracy was achieved.

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

This paper introduces YOLO, the best approach to object detection. Real-time detection plays a significant role in various domains like video surveillance, computer vision, autonomous driving and the operation of robots. YOLO algorithm has emerged as a well-liked and structured solution for real-time object detection due to its ability to detect items in one operation through the neural network. This research article seeks to lay out an extensive understanding of the defined Yolo algorithm, its architecture, and its impact on real-time object detection. This detection will be identified as a regression problem by frame object detection to spatially separated bounding boxes. Tasks like recognition, detection, localization, or finding widespread applicability in the best real-world scenarios, make object detection a crucial subdivision of computer vision. This algorithm detects objects in real-time using convolutional neural networks (CNN). Overall this research paper serves as a comprehensive guide to understanding the detection of objects in real-time using the You Only Look Once (YOLO) algorithm. By examining architecture, variations, and implementation details the reader can gain an understanding of YOLO’s capability.