In this paper, an unmanned remote-controlled ground vehicle-mounted system for IED/mine detection named as IMTA is presented. It consists of a multichannel ground penetrating radar and a metal detector; the two sensors are integrated in a single panel. Sensors of the proposed detection panel have the feature of working in the same integrated search panel without affecting each other. The integrated sensor panel system consists of sensors and embedded processors. It can easily be mounted on any unmanned ground vehicle. It is obtained that the integrated sensor panel of IMTA can easily detect both metallic and non-metallic targets at usual burial depths during operational movement.

A various GPR configurations can be used to detect buried elongated cables but it arises an inconsistent detection capability from ground clutter effects and many different objects in the ground. To address this issue, we propose a dual sensor configuration including EMI and GPR systems which distinguishes kinds of buried objects and reveal only cable detection. The system utilizes a special arrangement combining EMI and GPR signals to detect shallowly thin cables. While the proposed system has been less affected from other objects in the ground, so it has estimated the cables with high accuracy. The system has been produced a low cost and high efficiency design using STM32H743 microcontroller to control sensors and process and visualize the signals. The system performs robust and accurate cable/IED and landmine estimation. The effectiveness of cable/IED detection performance has been demonstrated using experimental data. The system detects cables 0.5 mm2thickness and above.

Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating significant latency and energy consumption on digital electronic hardware such as GPUs. In this study, we demonstrate that the propagation of a light beam through a transparent medium can be programmed to implement a denoising diffusion model on image samples. This framework projects noisy image patterns through passive diffractive optical layers, which collectively only transmit the predicted noise term in the image. The optical transparent layers, which are trained with an online training approach, backpropagating the error to the analytical model of the system, are passive and kept the same across different steps of denoising. Hence this method enables high-speed image generation with minimal power consumption, benefiting from the bandwidth and energy efficiency of optical information processing.

With the evolution of artificial intelligence, Machine Learning (ML) techniques have become more powerful predictors, and accordingly, the use of ML techniques has become a part of our daily life in different application scenarios such as disease diagnosis, movie recommendation, monitoring system, or detection of malicious attacks. Although ML provides high accurate predictions, it suffers from opacity. By behaving like a black box it excluded users about how to reach particular decisions. Interpretable Machine Learning (IML) is a recent technology that offers a promising solution to the opaqueness problem of complex ML techniques. It provides transparency of how the inner workings of ML lead to certain decisions and allows users to be aware of the decision-making process. Especially, in critical scenarios such as healthcare, it may become extremely important to know the reasons that affect the decision as well as the result. In this study, we aim to show the benefits of IML over a healthcare case study. In experiments, we employ SHAP and LIME IML models for the Random Forest (RF) and Gradient Boosting (GB) algorithms for the problem of diagnosing diabetes and its explanations. Overall results exhibit that applying IML models to complex and hard-to-interpret ML techniques ensures detailed interpretability while maintaining accuracy. We also perform experiments for local interpretability by focusing on an instance, which is another advantage of IML.

Recent online popular platforms such as social media, blogs, and newspapers generate a vast amount of unstructured data per second. Sentiment Analysis (SA) is an efficient technique to identify and extract subjective information in unstructured data to enable businesses to understand the emotional tendency of the interactive users towards its products or services. However, analyzing unstructured data can be more difficult than structural data. In particular, the performance of SA techniques decreases due to the structural complexity of the language. SA techniques are widely used in English since it is universal and structurally more suitable for SA. On the other hand, structural difficulties and complexities in Turkish cause performance degradation of SA studies compared to English. This study aims to overcome this difficulty by first translating Turkish texts into English texts by machine translation, and then realizing sentiment analysis on English texts. To demonstrate the success of machine translation, the experiments are conducted on two different data sets and results are given in a comparative manner for both on Turkish as the original language and English as the translated language. Data sets in both languages are classified by six different machine learning methods which are Logistic Regression, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Network. When the success rates of machine learning methods are examined, a significant increase is observed by machine translation for most of the methods.