Breast cancer is one of the most common forms of cancer. Its aggressive nature coupled with high mortality rates makes this cancer life-threatening; hence early detection gives the patient a greater chance of survival. Currently, the preferred diagnosis method is mammography. However, mammography is expensive and exposes the patient to radiation. A cost-effective and less invasive method known as thermography is gaining popularity. Bearing this in mind, the work aims to initially create machine learning models based on convolutional neural networks using multiple thermal views of the breast to detect breast cancer using the Visual DMR dataset. The performances of these models are then verified with the clinical data. Findings indicate that the addition of clinical data decisions to the model helped increase its performance. After building and testing two models with different architectures, the model used the same architecture for all three views performed best. It performed with an accuracy of 85.4%, which increased to 93.8% after the clinical data decision was added. After the addition of clinical data decisions, the model was able to classify more patients correctly with a specificity of 96.7% and sensitivity of 88.9% when considering sick patients as the positive class. Currently, thermography is among the lesser-known diagnosis methods with only one public dataset. We hope our work will divert more attention to this area.

Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development.

The low cost and advancement of sensor technology have long proven to be invaluable in their use in all areas of science. Several video-based methods have already been suggested for Intelligent Transportation Systems. However, the information collected through image/video processing alone is not sufficient for an error-proof traffic management system. In this paper, we propose Sensor-based Anomaly Detection (SAD), a system that integrates the capabilities of sensors with powerful image processing techniques to build an efficient, self-adaptive traffic control system. This system has two interlinked modules: Accident Detection and Emergency Stat Invocation. The accident detection module uses piezoelectric sensors to measure any pressure variation that in turn invokes an image processing submodule to detect the accident. Once the accident is confirmed, the emergency stat invocation module contacts the nearest emergency service for help. The proposed model is simple and fast to detect anomalies in real-time heterogeneous traffic conditions. The combined use of sensor technology and image processing to detect anomalies significantly increases the accuracy of the system, i.e., by reducing false alarms. By integrating SAD in real-time, the emergency services are alerted instantly, thereby ensuring faster medical assistance. The proposed system, when deployed and analyzed in real-time, achieves 93-95% accuracy in urban areas and 96-99% accuracy on highways. SAD clearly offers more accuracy when compared with other state-of-the-art methods without compromising performance.

Till date, most of the signature schemes are based on an Elliptic curve. The Elliptic curve based on signature scheme is called as ECDSA. In support of this scheme a study on a number of schemes was done. We also extend this signature schemes to a Variant Scheme level of ECDSA. In this paper, we propose a new variant ECDSA scheme that will produces the high level security with the help of the parameters. We give a brief preamble to the signature scheme in chapter I and the give a comparative result in the chapter XI. Finally, we show that the proposed variant ECDSA satisfy every security levels on the signature schemes.