Smart multimedia-based medical analytics and decision-making systems are of prime importance in the healthcare sector. Liver cancer is commonly stated to be the sixth most widely diagnosed cancer and requires an early diagnosis to help with treatment planning. Liver tumors have similar intensity levels and contrast as compared to neighboring tissues. Similarly, irregular tumor shapes are another major issue that depends on the cancer stage and tumor type. Generally, liver tumor segmentation comprises two steps: the first one involves liver identification, and the second stage involves tumor segmentation. This research work performed tumor segmentation directly from a CT scan, which tends to be more difficult and important. We propose an efficient algorithm that employs multi-scale parallel convolution blocks (MPCs) and Res blocks based on residual learning. The fundamental idea of utilizing multi-scale parallel convolutions of varying filter sizes in MPCs is to extract multi-scale features for different tumor sizes. Moreover, the utilization of residual connections and residual blocks helps to extract rich features with a reduced number of parameters. Moreover, the proposed work requires no post-processing techniques to refine the segmentation. The proposed work was evaluated using the 3DIRCADb dataset and achieved a Dice score of 77.15% and 93% accuracy.

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance.

As electricity supply expands, it is essential for providers to predict and analyze consumer electricity patterns to plan effective electricity supply policies. In general, electricity consumption data take the form of time series data, and to analyze the data, it is first necessary to check if there is no data contamination. For this, the process of verifying that there are no abnormalities in the data is essential. Especially for power data, anomalies are often recorded over multiple time units rather than a single point. In addition, due to various external factors, each set of power consumption data does not have consistent data features, so the importance of pre-clustering is highlighted. In this paper, we propose a method using a CNN model using pre-clustering-based time series images to detect anomalies in time series power usage data. For pre-clustering, the performances were compared using k-means, k-shapes clustering, and SOM algorithms. After pre-clustering, a method using the ARIMA model, a statistical technique for anomaly detection, and a CNN-based model by converting time series data into images compared the methods used. As a result, the pre-clustered data produced higher accuracy anomaly detection results than the non-clustered data, and the CNN-based binary classification model using time series images had higher accuracy than the ARIMA model.

Utilizing pre-trained models involves fully or partially using pre-trained parameters as initialization. In general, configuring a pre-trained model demands practitioners’ knowledge about problems or an exhaustive trial–error experiment according to a given task. In this paper, we propose tuning trainable layers using a genetic algorithm on a pre-trained model that is fine-tuned on single-channel image datasets for a classification task. The single-channel dataset comprises images from grayscale and preprocessed audio signals transformed into a log-Mel spectrogram. Four deep-learning models used in the experimental evaluation employed the pre-trained model with the ImageNet dataset. The proposed genetic algorithm was applied to find the highest fitness for every generation to determine the selective layer tuning of the pre-trained models. Compared to the conventional fine-tuning method and random layer search, our proposed selective layer search with a genetic algorithm achieves higher accuracy, on average, by 9.7% and 1.88% (MNIST-Fashion), 1.31% and 1.14% (UrbanSound8k), and 2.2% and 0.29% (HospitalAlarmSound), respectively. In addition, our searching method can naturally be applied to various datasets of the same task without prior knowledge about the dataset of interest.

This paper presents the vulnerabilities analyses of KL scheme which is an ID-based authentication scheme for AMI network attached SCADA in smart grid and proposes a security-enhanced authentication scheme which satisfies forward secrecy as well as security requirements introduced in KL scheme and also other existing schemes. The proposed scheme uses MDMS which is the supervising system located in an electrical company as a time-synchronizing server in order to synchronize smart devices at home and conducts authentication between smart meter and smart devices using a new secret value generated by an OTP generator every session. The proposed scheme has forward secrecy, so it increases overall security, but its communication and computation overhead reduce its performance slightly, comparing the existing schemes. Nonetheless, hardware specification and communication bandwidth of smart devices will have better conditions continuously, so the proposed scheme would be a good choice for secure AMI environment.

This study presents a comprehensive examination of the risks associated with SQL Injection attacks, with a particular focus on the Union Select technique. This method is frequently exploited by attackers to retrieve unauthorized data by appending malicious queries to legitimate database calls. We analyzed multiple real-world cases where personal information was leaked through such attacks, underscoring the urgent need for robust countermeasures in modern web applications. To address these threats, we developed and implemented a multi-layered defense strategy. This strategy includes using PHP Data Objects (PDO) with Prepared Statements to safely handling user inputs, rigorous input pattern validation to detect and reject suspicious payloads, and a redirection-based filtering mechanism to disrupt abnormal access attempts. Through controlled experiments, we validated the effectiveness of these techniques in mitigating SQL Injection attacks. The results demonstrate that our approach successfully blocked malicious queries and prevented unauthorized data access or manipulation. These findings represent a significant contribution to enhancing the security, stability, and trustworthiness of web-based systems, especially those handling sensitive user information. Finally, this work is presented as an educational comparative study, not as a proposal of new defense mechanisms, aiming to provide a clear and reproducible evaluation of standard SQL injection countermeasures. The contributions of this work are threefold: (i) it provides a unified comparative evaluation of three representative SQL injection defense methods—PDO, pattern validation, and attacker redirection—under identical experimental conditions; (ii) it analyzes their strengths, weaknesses, and practical applicability in PHP–MySQL environments; and (iii) it serves as an educational reference that bridges theoretical understanding and practical implementation. The study also suggests directions for extending this work through machine-learning-based anomaly detection and runtime self-protection (RASP) frameworks.