Smart grids represent a modern energy distribution system that integrates traditional electricity grids with advanced digital communication and control technologies. Characterized by two-way data flow and sophisticated automation capabilities, this system facilitates seamless interaction among all components in the energy supply chain— from generation to consumption. The smart grid aims to enhance energy management by improving efficiency, reliability, and sustainability. It dynamically monitors and manages energy demand, optimizes supply-demand balance, detects and addresses faults promptly, and supports the integration of renewable energy sources. Artificial intelligence (AI) plays a transformative role in advancing the performance and efficiency of smart grids. By processing, predicting, and analyzing vast and complex datasets, AI enables precise energy demand forecasting, early fault detection and mitigation, optimized energy distribution, and robust protection against cybersecurity threats. The integration of AI in smart grids ensures greater flexibility, efficiency, and reliability in energy distribution, fostering the sustainable use of resources, reducing costs, and improving user satisfaction.

In recent decades, obtaining energy from wind, solar, and hydro energy has brought hope for a sustainable future. Machine learning (ML) significantly impacts the field of energy systems. ML techniques have emerged as a powerful tool for analyzing and optimizing energy systems. This approach can potentially reduce the cost of analyzing a device by focusing solely on the data generated during its operation. This paper investigates the application of regression models to predict renewable energy production based on weather conditions, aiming to enhance the reliability and efficiency of renewable energy systems. Experiments are performed using Decision Tree (DT), k-nearest Neighbors (KNN), Multiple Linear (MLR), Polynomial (PR), Random Forest (RF), and Support Vector (SVR) Regression algorithms. The performances of ML models are compared based on the evaluations obtained through the Coefficient of Determination (R2), and error measures namely Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and Root Mean Square Error (RMSE). RF is the most successful algorithm in predicting renewable energy production depending on weather conditions as it has the highest R2 value and the lowest error measures.

Robotic systems, which were initially intended only for industrial work, have become a part of our daily lives in recent years. ROS is a popular middleware for developing applications in robotic systems. The security of ROS-based systems has gained importance with the expansion of robotic system usage. To enhance security, machine learning algorithms play an important role in robotic systems, especially in attack detection. This article provides an overview of the security of robotics by examining the machine learning based detection methods of cyber-attacks against ROS. We start by presenting security issues with a brief review of attack vectors in ROS. Next, we discuss the machine learning based attack detection approaches. Finally, we conclude by reviewing studies in the literature that use machine learning algorithms on attack detection in ROS. This survey aims to investigate the most popular and effective techniques applied to detect cyber-attacks, focusing on highlighting the latest advances in the field of machine learning in ROS attack detection. Our study will raise awareness among researchers in robotic system security and encourage researchers to upgrade attack detection in ROS-based systems.

The First International Workshop on "Intelligence Cross-Data Analytics and Retrieval" (ICDAR'20) welcomes any theoretical and practical works on intelligence cross-data analytics and retrieval to bring the smart-sustainable society to human beings. We have witnessed the era of big data where almost any event that happens is recorded and stored either distributedly or centrally. The utmost requirement here is that data came from different sources, and various domains must be harmonically analyzed to get their insights immediately towards giving the ability to be retrieved thoroughly. These emerging requirements lead to the need for interdisciplinary and multidisciplinary contributions that address different aspects of the problem, such as data collection, storage, protection, processing, and transmission, as well as knowledge discovery, retrieval, and security and privacy. Hence, the goal of the workshop is to attract researchers and experts in the areas of multimedia information retrieval, machine learning, AI, data science, event-based processing and analysis, multimodal multimedia content analysis, lifelog data analysis, urban computing, environmental science, atmospheric science, and security and privacy to tackle the issues as mentioned earlier.

Emergence of Internet of Things (IoT) concept is expected to deeply change everyday life through a vast number of services and applications. Heterogeneity of IoT networks and unique characteristics of individual services, causes requirement of different quality factors for services and applications. Research on Quality-of-Service (QoS) aims to satisfy quality requirements of different services on heterogeneous IoT networks. To ensure a certain level of QoS, QoS prediction methodology is used to enhance service selection for IoT users. In this survey, QoS prediction approaches are briefly explained. The advantages and disadvantages of the existing approaches are presented and response times of QoS prediction methods are compared using two statistical metrics, namely Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

In this study, a holistic method which uses statistical, linguistic and rule-based approaches for Turkish aspect-based sentiment analysis is proposed. The proposed method has been tested on the Turkish restaurant dataset created within the scope of SemEval Aspect Based Sentiment Analysis (ABSA) 2016. Firstly, candidate aspect terms were acquired employing LDA, C-value and WSBFE. Afterwards, aspect terms were found by rule-based approach and aspect-sentiment pairs were determined. In aspect term extraction 56,28% f-score was obtained while in aspect-sentiment matching phase 52,05% accuracy was achieved.

Internet of Things (IoT) applications are generating tremendous amount of data which is not only extremely big, but also missing, noisy, and uncertain due to intrinsic characteristics of IoT. These phenomenons pose a number of challenges in managing the IoT network and trustworthiness of the data analytics. Specifically, transferring all IoT data to the cloud for data analytics may be costly, inefficient and infeasible in some cases. Therefore, migrating sensor data processing and analysis closer to the edge devices plays a vital role in terms of reducing the amount of data sent to the cloud, IoT service delay and network latency. In this paper, we first aim to enable deep learning models in resource constrained IoT devices. Then, we design and implement a real IoT testbed consisting of resource constrained devices. We also provide a solution to the missing sensor data problem in IoT from the perspectives of edge, fog and cloud computing. Finally, we compare all computing approaches in terms of network load, latency and delay. Experimental results show that deep learning based edge and fog computing approaches can improve network delay and bandwidth requirements greatly and efficiently.

That is a fact that the increasing volume of data brings security problems. Encryption measures taken against these emerging and increasing security problems have been examined, and it has been seen that encryption approaches are performed mainly in the areas of neural and quantum cryptography. According to these two of the advanced cryptography techniques are discussed and analyzed with their advantages and disadvantages. This article is committed to paper to be able to provide solutions for big data privacy and big data security problems with Neural and Quantum Cryptography techniques when questioning their feasibility.