Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning‐based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real‐time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password‐sized text and paragraph‐sized text—that achieves target fair results compared to the existing algorithms in real‐time deep learning networks for IoT applications.

Liver cirrhosis is a terminal pathological result of chronic liver damage, illicit drugs, hepatotoxicity, and non-alcoholic steatohepatitis. Assessment of liver cirrhosis via non-invasive methods in order to circumvent the limitations of liver biopsy. This research builds upon the growing body of knowledge in liver cirrhosis assessment by examining a dataset comprising cases of primary biliary cirrhosis. Prior research has primarily concentrated on comparative analyses and development of machine learning models integrating imaging modalities such as ultrasound, magnetic resonance imaging (MRI), and elastography, with limited focus on serum biomarkers. This research endeavors to address the neglected aspects of liver cirrhosis assessment by leveraging Explainable AI algorithm to bridge the gap between AI models and human comprehension via providing insights into the intricate decision-making process of the proposed machine learning model, thus enhancing transparency and trustworthiness. This novel approach aims to overcome the limitations of previous works and contribute to improved liver cirrhosis diagnosis.

In 2020, there were more than 1.2 million new skin cancer diagnoses, and melanoma was the most recurrent type of cancer. On the other hand, melanoma is the least common but most serious form of skin cancer affecting both men and women. This work aims to assemble classification models to detect a case of melanoma with high accuracy based on a Convolutional Neural Networks system. The methodology considers training 21 models for image classification, with the best assembly performance of EfficientNet and VGG-19 architectures, the data augmentation technique was used to the images to improve its performance. The results show 92.85% of accuracy, 71.50% of sensitivity, and 94.89% of specificity, with an improvement of 0.06% in accuracy and specificity. The assembly of the classification models achieved higher accuracy in melanoma skin cancer image classification.

Objectives: This review aims to evaluate several convolutional neural network (CNN) models applied to breast cancer detection, to identify and categorize CNN variants in recent studies, and to analyze their specific strengths, limitations, and challenges. Methods: Using PRISMA methodology, this review examines studies that focus on deep learning techniques, specifically CNN, for breast cancer detection. Inclusion criteria encompassed studies from the past five years, with duplicates and those unrelated to breast cancer excluded. A total of 62 articles from the IEEE, SCOPUS, and PubMed databases were analyzed, exploring CNN architectures and their applicability in detecting this pathology. Results: The review found that CNN models with advanced architecture and greater depth exhibit high accuracy and sensitivity in image processing and feature extraction for breast cancer detection. CNN variants that integrate transfer learning proved particularly effective, allowing the use of pre-trained models with less training data required. However, challenges include the need for large, labeled datasets and significant computational resources. Conclusions: CNNs represent a promising tool in breast cancer detection, although future research should aim to create models that are more resource-efficient and maintain accuracy while reducing data requirements, thus improving clinical applicability.

The objective of the paper is to provide a model capable of serving as a basis for retraining a convolutional neural network that can be used to detect COVID-19 cases through spectrograms of coughing, sneezing and other respiratory sounds from infected people. To address this challenge, the methodology was focused on Deep Learning technics worked with a dataset of sounds of sick and non-sick people, and using ImageNet's Xception architecture to train the model to be presented through Fine-Tuning. The results obtained were a precision of 0.75 to 0.80, this being drastically affected by the quality of the dataset at our availability, however, when getting relatively high results for the conditions of the data used, we can conclude that the model can present much better results if it is working with a dataset specifically of respiratory sounds of COVID-19 cases with high quality.