A chronic neurological disorder called epilepsy is characterized by frequent, unplanned seizures. A seizure is an unexpected and uncontrolled electrical disturbance in the brain that can cause a variety of physical and behavioral symptoms. Prognosis of epilepsy can be done based on pre-ictal (prior to seizure) signal variations in Electroencephalogram (EEG) rhythm. EEG rhythm like alpha, beta, theta and delta are substantial for epilepsy analysis. This study aimed to investigate the impact of various features from EEG rhythm and the feature reduction in classification of pre-ictal and inter-ictal (between two seizures) signal. Dataset of CHB-MIT comprises of 23 patients with 23 channels are used to extract Time, Frequency and Time-frequency features from EEG rhythms. Analysis shows that, compared to other bands, beta band features show major variation in pre-ictal and inter-ictal phases, which makes training of a Support Vector Machine (SVM) classifier easy for prediction of seizures. Further reduction in feature size using statistical analysis helped to achieve 75% reduction in computation. Results show average sensitivity of 93% and false positive rate of 0.14 per hour. The proposed method classified pre-ictal signal with maximum accuracy of 95%, sensitivity of 100%, specificity of 93% and false positive rate of 0.07per hour with reduced complexity compare to other state of art methods.

Quantum computing is a rapidly developing field with faster computational capabilities than classical computing. The popularity of quantum computing has reached the field of image processing, particularly with a breakthrough method known as Quantum Hadamard Edge Detection. This approach represents a significant advancement in edge detection techniques using quantum computing. Quantum Hadamard Edge Detection is a method that can detect image edges more quickly than classical methods with exponential acceleration. This paper explains the Quantum Hadamard Edge Detection method in detail, including how it is implemented, a time complexity explanation, some experiments, and future research directions. Our experiments utilize a quantum computer simulator and employ four measurement metrics: Structural Similarity Index, Figure of Merit, Entropy, and a Proposed Metric with radius-based features, to detect simple binary images, MNIST images, and the Berkeley Segmentation datasets. We recognize the potential of quantum computing and believe that image processing with quantum representation will make processing more efficient and significantly valuable in the future.

Soil image classification plays a crucial role in agricultural and environmental practices. Traditional methods of soil classification often involve manual labor, which can be time-consuming and prone to human error. Recent advances in computer vision and machine learning have opened new horizons for automating this classification process. This research paper presents a comprehensive study and evaluates the performance of four convolutional neural network (CNN) architectures a custom CNN, ResNet50, InceptionV3, and MobileNetV2 on a custom soil image dataset comprising 1800 labelled images across four soil classes such as Black, Laterite, Red and White. The dataset created using smartphone camera to captured images under varying natural conditions. The objective of this work is to explore the effectiveness and accuracy of different machine learning algorithms used in categorizing soil types based on visual data. Each model’s performance is evaluated in terms of classification accuracy, precision, recall, and F1-score. Results indicate that ResNet50 achieves the highest accuracy 97.3%, followed closely by MobileNetV2 94.7%. The custom CNN, while computationally efficient, achieved 88.2%. We conclude that transfer learning with deep CNNs is highly effective for soil classification, and MobileNetV2 is a strong recommended for mobile applications. The comparative analysis demonstrates their effectiveness in distinguishing between different soil types, textures, and compositions. It also highlights how important it is to select the appropriate CNN architectures for certain tasks related to soil classification.  This work belongs to the increasing collection of information at the interface between soil science and computer vision. It offers a strategy to apply sophisticated deep learning-based algorithms to assess soil type more reliably and effectively, serving as a springboard for future research in the field of soil image analysis and classification.

Infrastructure as a service is used for resource management. Resources that will be available on demand are effectively managed using the resource management module. Predicting CPU and memory usage assists with resource management when cloud resources are provided. This study uses a hybrid DS-RAE model to forecast CPU and memory utilization in the future. Predictions are made using the range of values found, which is helpful for resource management. The memory and CPU use patterns in the cloud traces are identified by the Double Channel Residual Self-Attention Temporal Convolution Network (DSTNW) model as having linear components. The Recursive Autoencoder (RAE) model for tracing and enlarging nonlinear components and power consumption was developed using the DSTNW model. Gathers the raw data taken from the system's operational state, such as bandwidth, disk I/O time, disk space, CPU, and memory utilization. Discover patterns and oscillations in the workload trace by preprocessing the data to increase the prediction efficacy of this model. During data pre-processing, missing value edge computing and z-score normalization are used to select the important properties from raw data samples, eliminate irrelevant elements, and normalize them. After that, preprocessing utilizes a dynamization of the sliding window to improve the proposed model's accuracy on non-random workloads. Next, utilize a hybrid DS-RAE to attain accurate workload forecasting. Comparing the suggested methodology with existing models, experimental results show that it offers a better trade-off between training time and accuracy. The suggested method provides higher performance, with an execution time of 32 seconds and an accuracy rate of 97%. According to the simulation results, the DS-RAE workload prediction method performs better than other algorithms.

Acne is a persistent skin disorder that typically affects children in the age range of 12 to 25. Both inflammatory and non-inflammatory skin diseases can coexist with various types of acne, such as papules, pustules, nodules, cysts, blackheads, and whiteheads. In recent times, the study of acne has been carried out conventionally, with a manual approach for determining the ROI. As a result, the patient's face will be physically counted and marked with the acne that was found in the ROI. This manual method could result in incorrect identification and diagnosis of acne. Moreover, it is still difficult to determine the type of acne related to another. The necessity for patients to visit a dermatologist is growing despite the difficulties in identifying acne manually. For a patient, waiting for the dermatologist to become available is challenging. Thus, an automated application for recognizing acne types is needed, as it may help these individuals. In order to address these problems, a dataset containing images of skin diseases is created. Lanczos resampling, which is frequently used to shift or enhance a digital signal's sampling rate by a fraction of the sampling interval, is employed in the preprocessing of the skin disease data. Subsequently, the pre-processed images are segmented using the Modified Link Net-B7 in order to eliminate noise and correctly categorize images of acne with the segmented skin images. After the model has been trained and validated, the Acne type prediction is forecast using the HR-Net algorithm. The performance metrics for this developed model are FPR, FOR, NPV, kappa, error, accuracy, precision, sensitivity, specificity, f1-score, kappa, training time, testing time, and execution time. Performance metrics values of 95.17%, 94.10%, 92.33%, 96.34%, 93.15%, 85.74%, 4.83%, 4%, 6%, 95%, 7.7%, 1492, 23 and 1515 have been reached for the proposed approach. Therefore, compared to the existing models, Acne type prediction using the different types of Acne disease images based on modified Link Net-B7 and HR-Net algorithm performs better.

The new form of coronavirus started in Wuhan, China, in 2019 and is known as COVID-19. It created severe health issues and also deaths in most of the countries. The test kits and certain imaging techniques, namely computed tomography and X-ray, are utilized to analyze the severity of diseases. Earlier, researchers introduced several machine-learning techniques for medical diagnosis. However, due to complexity concerns and a high error rate, such strategies cannot produce superior results. Recently, several deep learning mechanisms have been utilized in medical diagnosis. In this work, a new triple-task learning architecture is introduced for the identification and categorization of COVID-19 disease by referring to CT images. First, the input images are pre-processed utilizing Gabor filtering and image resizing. After pre-processing, the images are fed to the triple-task learning network. Here, in the proposed network, three modules are included, namely Residual Swin Transformer based U-Net, Deep convolution and Extended BiLSTM. In this, the Residual Swin Transformer-based U-Net performs the segmentation task. After that, the most significant features are extracted using Deep convolution. The extracted features are then used in the classification step when the various classes of COVID-19 are classified. Finally, the classification parameters are fine-tuned utilizing the Adaptive Fire Hawks algorithm. Then, the proposed technique is experimentally verified utilizing a Python tool, and the performance is analyzed by evaluating the performance metrics. Also, the proposed approach is compared to existing techniques, and the comparison results show that the proposed technique achieves better performance, having an accuracy of 99.46%.

The challenge of graph vertex coloring is a well-established problem in combinatorial optimization, finding practical applications in scheduling, resource allocation, and compiler register allocation. It revolves around assigning colors to graph vertices while ensuring adjacent vertices have distinct colors, to minimize the total number of colors. In our research, we introduce an innovative methodology that leverages machine learning to address this problem. Our approach involves comprehensive preprocessing of a collection of graph instances, enabling our machine learning model to discern complex patterns and relationships within the data. We extract various features from the graph structures, including node degrees, neighboring node colors, and graph density. These features serve as inputs for training our machine learning model, which can encompass neural networks or decision trees. Through this training, our model becomes proficient at predicting optimal vertex colorings for previously unseen graphs. To evaluate our approach, the authors conducted extensive experiments on diverse benchmark graphs commonly used in vertex coloring research. Our results demonstrate that our machine learning-based approach achieves comparable or superior performance to state-of-the-art vertex coloring algorithms, with remarkable scalability for large-scale graphs. Further, in this research, the authors explored the use of Support Vector Machines (SVM) to predict optimal algorithmic parameters, showing potential for advancing the field. Our systematic, logical approach, combined with meticulous preprocessing and careful optimizer selection, strengthens the credibility of our method, paving the way for exciting advancements in graph vertex coloring.

The study is devoted to the analysis of public sentiment towards Ukrainian political figures based on comments on social media, in particular, YouTube and Twitter. The work aims to identify differences in the perception of political leaders and to understand how the platform affects the tone of statements. The main research question is to determine how public opinion about politicians in Ukraine differs between YouTube and Twitter during the full-scale war. To do this, a corpus of comments and tweets from 2022 to 2023 was collected, which went through pre-processing stages (including cleaning up slang and spelling mistakes). The article presents the results of a comprehensive analysis of public opinion on five public figures of Ukraine (S. Prytula, P. Poroshenko, V. Zelensky, S. Sternenko, A. Yermak) based on data from the social networks YouTube and Twitter. For data collection, the YouTube Data API and the Apify platform were used, a corpus of Ukrainian-language comments and tweets was collected and processed, which went through the stages of purification, normalisation and lemmatisation, taking into account slang, surzhyk and spelling mistakes. The sentiment analysis model, built on the basis of multilingual-e5-base embeddings and the XGBClassifier algorithm, showed an accuracy of 89.4%, macro-F1 of 88.7%, and a weighted F1 of 89.1%. Sentiment distribution analysis revealed that, on average, 42% of messages were positive, 36% were negative, and 22% were neutral. Twitter had a higher share of negative statements (up to 40%), while YouTube had a predominance of positive sentiment (up to 47%). The results indicate differences in the perception of public figures on different platforms and confirm the effectiveness of the developed approach for the Ukrainian-speaking segment of social networks. The results indicate significant differences in sentiment distribution: comments on YouTube are more likely to be marked by emotional intensity and harshness. At the same time, Twitter exhibits a more concise but no less polarised discourse. One of the reasons for this difference may be the difference in the format of the platforms, their audience, and the speed of content distribution. Further research should take into account the impact of user demographic biases, as well as the activity of bots or coordinated campaigns that can change the perception of public opinion. The practical significance of the study lies in the fact that its results can be used by politicians, journalists, and public figures to better understand the mood of society, predict reactions to political events, and build more effective communication. At the same time, it is worth noting that there are limitations: automated sentiment analysis has difficulty detecting sarcasm, irony, or context-sensitive meanings, which can affect the Accuracy of the results. In addition, the study takes into account the ethical aspects of data collection and analysis: only publicly available comments were used, without interference in the private sphere of users. There are possible risks of abuse of such technologies, and the need for responsible application of the findings is emphasised.