This paper presents a novel cryptanalysis method of DES (2-DES and 3-DES) using nature-inspired algorithms; namely Cuckoo Search Algorithm and Grey Wolf Optimization Algorithm. We have shown the loophole of 2-DES and 3-DES encryption systems and discovered the vulnerabilities by some simple mathematical calculations. The Meet-In-The-Middle approach can be executed on 2-DES along with Known Plaintext Attack, Chosen Plaintext Attack, and Chosen Ciphertext Attack. The valid key pairs along with the original key pairs can successfully be recovered by this attack algorithm. But in the Ciphertext Only Attack, the Meet-In-The-Middle approach fails to recover the plaintext as well as the valid key pairs both for 2-DES and 3-DES. To overcome this problem, we have proposed a novel cryptanalysis method of 3-DES with Ciphertext Only Attack using Cuckoo Search Algorithm and Grey Wolf Optimization Algorithm (GWO). We have developed a suitable fitness function, accelerating the algorithm toward the optimal solution. This paper shows how CSA and GWO can break a 3-DES cryptosystem using a Ciphertext Only Attack. This proposed cryptanalysis method can also be applied to any round of DES.

Chronic Kidney Disease (CKD) is considered a leading cause of high morbidity and mortality. Therefore, it needs early detection to allow timely intervention aimed at the enhancement of the patient outcome. The current study presents a Transparent CKD ML which combines the predictive power of efficient ML methods with the eXplainable AI techniques for transparent interpretibility of the prediction. This study has conducted an in-depth performance evaluation of the predictive power of the following eight machine learning algorithms: Logistic Regression, K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, CatBoost, XGBoost, and AdaBoost on the 'Chronic Kidney Disease' dataset provided by UCI Machine Learning Repository. As a further study on algorithm performance, performance measures of accuracy, precision, recall, and F1 score were calculated; it was determined that Logistic Regression, Random Forest, and AdaBoost were performing very well and achieved 100% score in all metrics. This study further combined the ML models with eXplainable AI ( XAI) techniques to increase the transparency of the models.  SHapley Additive exPlanations (SHAP) an XAI technique was used to provide critical insights into the causality that dictates the predictions of CKD. Thus, this combination ensures the best performance of the model, increasing the trust in AI within clinical practice. The present study, therefore, unleashes the transformational potential of AI technologies in radically renovating the management of CKD and improving patient outcomes across the world.

Virtual reality plays a major role in medicine in the aspect of diagnostics and treatment planning. From the diagnostics perspective, automated methods yields the segmented results into virtual environment which will helps the physician to take accurate decisions on time. Virtual reality of 3D brain tissue segmentation helps to diagnostic the brain related diseases like alzheimer's disease, brain malformations, brain tumors, cerebellar disorders and etc. The work proposed a fully automatic histogram-based self-initializing K-Means (HBSKM) algorithm is performed on compute unified device architecture (CUDA) enabled GPU (QudroK5000) machine to segmenting the human brain tissue. Number of clusters (K) and initial centroids (C) automatically calculated from the mid image from the volume through Gaussian smoothening technique. The experimental dataset was collected from internet brain segmentation repository (IBSR) in segmenting the three major tissues such as grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) to experiment the efficiency of the present parallel K-Means algorithm. Computation time is calculated between the homogenous and heterogeneous environment of CPU and GPU for HBSKM algorithm. This proposed work achieved 6× speedup folds while heterogeneous CPU and GPU implementation and 3.5× speedup folds achieved with homogenous GPU implementation. Finally, volume of segmented brain tissue results was presented in virtual 3D and also compared with ground truth results.

Stock price prediction anticipates future stock prices using historical data and computational models to assist and guide investing decisions. In financial forecasting, accuracy and efficacy in stock price prediction are essential for making better choices. This research describes a hybrid deep learning strategy for improving the extraction and interpretation of the crucial details from stock price time series data. Traditional approaches confront challenges such as computational complexity and nonlinear stock prices. The suggested method pre-processes stock data with Moving Average Z-Transformation, which emphasises long-term trends and reduces fluctuations in the short term. It combines a Transformed Moving Average Fast-RNN Hybrid with Advanced CNNs to create an efficient computational framework. The Enhanced Deep-CNN layer comprises convolutional layers, batch normalisation, leaky ReLU activations, dropout, max pooling and a dense layer. The performance of the model is quantified using metrics including Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R-squared (R2). It shows superior prediction accuracy with MAEs of 0.28, 0.15, 0.34, 0.17, and 0.13 for Kotak, ICICI, Axis, and SBI, respectively, outperforming previous models. These measurements provide detailed information about the model's predictive skills, proving its ability to improve stock price forecast accuracy significantly.

This study introduces an improved BERT-based model for sentiment analysis in several languages, specifically focusing on analyzing e-commerce evaluations written in English and Bengali. Conventional sentiment analysis techniques frequently face difficulties in dealing with the subtle linguistic differences and cultural diversities present in datasets containing multiple languages. The model we propose integrates sophisticated methodologies and utilizes Local Interpretable Model-agnostic Explanations (LIME) to enhance the accuracy, interpretability, and dependability of sentiment assessments in various language situations. To tackle the challenges of sentiment categorization in a multilingual setting, we enhance the pre-trained BERT architecture by incorporating extra neural network layers. Compared to traditional machine learning and current deep learning methods, the model underwent a thorough evaluation, showcasing its superior capabilities with accuracy, precision, recall, and F1-score of 0.92. Including LIME improves the model’s transparency, allowing for a better understanding of the decision-making process and increasing user confidence. This research highlights the potential of utilizing advanced deep learning models to address the difficulties of sentiment analysis in global e-commerce environments, providing major implications for both academic research and practical applications in industry.

This study thoroughly looks at how the prices and activity of players of popular indie games on Steam changed after COVID-19. It uses data from SteamDB, which has lots of info about game availability, sales, prices, activity of players, followers, positive and negative reviews on Steam and Twitch viewers. The goal is to deeply analyze how indie game makers and publishers set their prices and reactions of players to games which release date start from period before, during and after COVID-19. The focus is on how they changed their pricing models due to big shifts in market demand and consumer behavior because of the pandemic and how players reacted to these price changes in the context of wage cuts and layoffs. Reactions of players can be tracked not only by the statistics of the maximum or average online in the game, but also by the number of positive and negative reviews, because in difficult times it was important for players to correctly distribute their available funds and not to become disappointed in game and not to let other players become disappointed
By studying these changes, the aim is to find out how the indie game industry responded to tough times and new chances in the digital entertainment world. Since the study is being conducted in the post-COVID-19 period, it is also aimed at helping developers choose the right strategy when pricing their new Indie games or changing the prices of their existing Steam Indie games.
The main objects of research are indie games because this genre is one of the most popular in SteamDB, and to create such games requires less costs, therefore their price is acceptable for the average player.

Accurate electricity forecasting is vital for grid stability and effective energy management, particularly in regions like Benghazi, Libya, which face frequent load shedding, generation deficits, and aging infrastructure. This study introduces a data-driven framework to forecast electricity load, generation, and deficits for 2025 using historical data from two distinct years: 2019 (an instability year) and 2023 (a stability year). Various time series models were employed, including Autoregressive Integrated Moving Average (ARIMA), seasonal ARIMA, dynamic regression ARIMA, extreme gradient boosting, simple exponential smoothing, and Long Short-Term Memory (LSTM) neural networks. Data preprocessing steps—such as missing value imputation, outlier smoothing, and logarithmic transformation—are applied to enhance data quality. Model performance was evaluated using metrics such as mean squared error, root mean squared error, mean absolute error, and mean absolute percentage error. LSTM outperformed other models, achieving the lowest mentioned metric values for forecasting load, generation, and deficits, demonstrating its ability to handle non-stationarity, seasonality, and extreme events. The study’s key contribution is the development of an optimized LSTM framework tailored to North Benghazi’s electricity patterns, incorporating a rich dataset and exogenous factors like temperature and humidity. These findings offer actionable insights for energy policymakers and grid operators, enabling proactive resource allocation, demand-side management, and enhanced grid resilience. The research highlights the potential of advanced machine learning techniques to address energy-forecasting challenges in resource-constrained regions, paving the way for a more reliable and sustainable electricity system.

This research focuses on developing an automated framework for evaluating distress on flexible and rigid pavement surfaces through deep learning and algorithms, enhancing infrastructure monitoring by efficiently identifying, assessing, and measuring road distresses. The methodology begins with identifying road stretches from ground-level images, followed by capturing photos of distresses and applying algorithms to measure their dimensions accurately. A YOLOv5 model is developed to evaluate the length and width of identified distresses, with an exploration of the relationship between camera position and measurement accuracy. Physical measurements using tape are employed for validation, ensuring that the automated results align with real-world dimensions. Results indicate that the average errors of 26.1% for length and 26.9% for width for flexible pavement and the average percentage error in length is about 29% and average percentage error in width is about 1% for rigid pavement. This highlights the importance of precise measurements for effective road rehabilitation. The integration of computer vision in road maintenance, validated through physical measurements, promises significant improvements in the accuracy, efficiency, and resilience of road networks.

The Balanced Academic Curriculum Problem (BACP) is a complex optimization problem in educational institutions, involving the allocation of courses across academic terms while satisfying various constraints. This study aims to optimize BACP using the Teaching-Learning Based Optimization (TLBO) algorithm, addressing the limitations of existing approaches and providing an efficient framework for curriculum balancing. The novelty lies in applying TLBO to BACP, offering a parameter-free, nature-inspired metaheuristic that balances exploration and exploitation effectively. The proposed method models BACP as a mathematical optimization problem and implements TLBO to minimize total load balance delay across academic terms. Computational experiments were conducted on 12 benchmark BACP instances, comparing TLBO against eight other metaheuristic algorithms. Results demonstrate TLBO's superior performance, achieving the best solutions in 75-83% of test problems across various indicators. Statistical analysis using the Wilcoxon rank-sum test confirms the significance of TLBO's improvements. The study concludes that TLBO is a robust and efficient tool for optimizing BACP, outperforming existing methods in solution quality and convergence speed. Future research could focus on enhancing TLBO through hybridization with other algorithms and applying it to real-world BACP scenarios in educational institutions.

The use of millimeter-wave (mmWave) and full-dimensional multiple-input multiple-output (FD-MIMO) antenna systems for 3D wireless communication is being exploited for enhanced network capacity improvement in the ongoing fifth-generation (5G) deployment. For adequate assessment of competing air interface, random access channelization, and beam alignment procedure in mmWave systems, adequate channel estimation and channel models for different use scenarios are necessary. Conventional pilot-based channel estimation methods are remarkably time-consuming as the number of users or antennas tends toward large numbers. Channel reconstruction has been identified as one of the solutions to the above problem. In this work, a ray-tracing study was conducted using a Wireless Insite ray tracing engine to predict measured statistics for large-scale channel parameters (LSPs). Other LSP such as the shadow fading (SF) were generated using algorithm 1. Algorithm 2 was used to generate the small-scale channel parameters (SSP). The LSPs and SSPs were used as input in algorithm 3 to generate the channel coefficients used for the channel reconstruction in the MATLAB LTE toolbox. The results provided an accurate reconstructed downlink channel state information (CSI) for FDD-based mmWave massive-MIMO system in both the line-of sight (LOS) and non-line of sight scenarios. The results provide an opportunity to adapt the transmitted signal to the CSI and thereby optimize the received signal for spatial multiplexing or to achieve low bit error rates in wireless communication.