Ethereum has emerged as a major platform for decentralized apps and smart contracts with the heightened interest in cryptocurrencies in recent years. Investors and market participants in the cryptocurrency space will find it increasingly important to use reliable price prediction models as Ethereum's popularity grows. To better estimate Ethereum prices around the world, we propose "EtherVoyant," a novel hybrid forecasting model that combines the advantages of ARIMA and SARIMA methods. To improve its forecasting abilities, EtherVoyant uses Ethereum price history to train ARIMA and SARIMA components independently before fusing their predictions. With the help of feature engineering and data preparation, we further improve the model so that it can deal with real-world difficulties like missing values and seasonality in the data. We also investigate hyperparameter optimization for the model's best possible performance. We compare EtherVoyant's forecasts against those of the more conventional ARIMA and SARIMA models to determine its efficacy. By providing more precise and trustworthy price forecasts, our trial results suggest that EtherVoyant is superior to the individual models. The importance of this study resides in the fact that it will lead to the creation of a sophisticated time series forecasting model that will be useful to cryptocurrency investors, traders, and decision-makers. We hope that by making EtherVoyant available on a worldwide scale, we will help advance the field of cryptocurrency analytics and encourage wider adoption of blockchain-based assets.

Left Ventricular Hypertrophy (LVH) is a critical predictor of cardiovascular disease, making it essential to incorporate it as a fundamental parameter in both diagnostic screening and clinical management. Addressing the need for efficient, accurate, and scalable medical image analysis, we introduce a state-of-the-art preprocessing pipeline coupled with a novel Deep Convolutional Neural Network (DCNN) architecture. This paper details our choice of the HMC-QU dataset, selected for its robustness and its proven efficacy in enhancing model generalization. We also describe innovative preprocessing techniques aimed at improving the quality of input data, thereby boosting the model's feature extraction capabilities. Our multi-disciplinary approach includes deploying a DCNN for automated LVH diagnosis using echocardiography A4C and A2C images. We evaluated the model using architectures based on VGG16, ResNet50, and InceptionV3, where our proposed DCNN exhibited enhanced performance. In our study, 93 out of 162 A4C recordings and 68 out of 130 A2C recordings confirmed the presence of LVH. The novel DCNN model achieved an impressive 99.8% accuracy on the training set and 98.0% on the test set. Comparatively, ResNet50 and InceptionV3 models showed lower accuracy and higher loss values both in training and testing phases. Our results underscore the potential of our DCNN architecture in enhancing the precision of MRI echocardiograms in diagnosing LVH, thereby providing critical support in the screening and treatment of cardiovascular conditions. The high accuracy and minimal losses observed with the novel DCNN model indicate its utility in clinical settings, making it a valuable tool for improving patient outcomes in cardiovascular care.

In the rapidly evolving landscape of modern technology, the convergence of blockchain innovation and machine learning advancements presents unparalleled opportunities to enhance computer forensics. This study introduces SentinelFusion, an ensemble-based machine learning framework designed to bolster secrecy, privacy, and data integrity within blockchain systems. By integrating cutting-edge blockchain security properties with the predictive capabilities of machine learning, SentinelFusion aims to improve the detection and prevention of security breaches and data tampering. Utilizing a comprehensive blockchain-based dataset of various criminal activities, the framework leverages multiple machine learning models, including support vector machines, K-nearest neighbors, naive Bayes, logistic regression, and decision trees, alongside the novel SentinelFusion ensemble model. Extensive evaluation metrics such as accuracy, precision, recall, and F1 score are used to assess model performance. The results demonstrate that SentinelFusion outperforms individual models, achieving an accuracy, precision, recall, and F1 score of 0.99. This study’s findings underscore the potential of combining blockchain technology and machine learning to advance computer forensics, providing valuable insights for practitioners and researchers in the field.

Over the past few years, there has been a notable surge in the integration of Blockchain technology into supply chain management systems. This integration holds the promise of enhanced transparency, security, and efficiency in monitoring the movement of goods and services. This study presents a novel approach aimed at fortifying privacy and accuracy within blockchain-based supply chain management systems. The methodology integrates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) units with secure multi-party computation (MPC) and differential privacy techniques as a hybrid model. The objective is to safeguard the confidentiality of transaction data while enabling precise detection of media tampering. Performance evaluation revolves around three key aspects: accuracy, privacy preservation, and computational efficiency. In terms of accuracy assessment, the proposed hybrid approach is benchmarked against traditional machine learning algorithms including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Random Forest. Results indicate superior performance, with the proposed hybrid method achieving an accuracy of 0.95, outperforming conventional algorithms. Precision, recall, and F1-score metrics further confirm the effectiveness of the approach in accurately identifying media tampering instances. Privacy preservation capabilities are evaluated through differential privacy techniques, revealing the method’s ability to inject controlled noise into the data to protect individual privacy. Results demonstrate varying levels of privacy preservation across different settings, highlighting the trade-off between privacy and data utility. Computational efficiency is also scrutinized, considering the additional overhead introduced by privacy preservation mechanisms and secure MPC protocols. While there is a slight increase in computational time, the proposed approach maintains reasonable training and inference times, ensuring practical applicability in real-world scenarios.

In the realm of medical imaging, the early detection of kidney issues, particularly renal cell hydronephrosis, holds immense importance. Traditionally, the identification of such conditions within ultrasound images has relied on manual analysis, a labor-intensive and error-prone process. However, in recent years, the emergence of deep learning-based algorithms has paved the way for automation in this domain. This study aims to harness the power of deep learning models to autonomously detect renal cell hydronephrosis in ultrasound images taken in close proximity to the kidneys. State-of-the-art architectures, including VGG16, ResNet50, InceptionV3, and the innovative Novel DCNN, were put to the test and subjected to rigorous comparisons. The performance of each model was meticulously evaluated, employing metrics such as F1 score, accuracy, precision, and recall. The results paint a compelling picture. The Novel DCNN model outshines its peers, boasting an impressive accuracy rate of 99.8%. In the same arena, InceptionV3 achieved a notable 90% accuracy, ResNet50 secured 89%, and VGG16 reached 85%. These outcomes underscore the Novel DCNN’s prowess in the realm of renal cell hydronephrosis detection within ultrasound images. Moreover, this study offers a detailed view of each model’s performance through confusion matrices, shedding light on their abilities to categorize true positives, true negatives, false positives, and false negatives. In this regard, the Novel DCNN model exhibits remarkable proficiency, minimizing both false positives and false negatives. In conclusion, this research underscores the Novel DCNN model’s supremacy in automating the detection of renal cell hydronephrosis in ultrasound images. With its exceptional accuracy and minimal error rates, this model stands as a promising tool for healthcare professionals, facilitating early-stage diagnosis and treatment. Furthermore, the model’s convergence rate and accuracy hold potential for enhancement through further exploration, including testing on larger and more diverse datasets and investigating diverse optimization strategies.

In today’s globalized economic landscape, the assurance of economic stability is of paramount importance, necessitating precise financial decision-making and policy formulation. This assurance is significantly augmented by innovative approaches to predicting crude oil prices, particularly in the context of energy stock markets denominated in USD. This paper delves into the transformative effect of accurate crude oil price prediction on economic policy stability. It underscores the challenges and limitations posed by policy uncertainties and emphasizes the pivotal role of innovative solutions in mitigating these challenges. Moreover, it recognizes the imperative need for secure data storage to facilitate the application of machine learning in this domain. Furthermore, effective management and regulation of power grid systems are explored as indispensable strategies for tempering the volatility introduced by fluctuations in energy stock markets. As we work to address these gaps in knowledge, the potential for sustainable power systems to supersede fossil fuels emerges as a driving force behind the maintenance of stable economic policies.

Stroke is a severe illness, that requires early stroke detection and intervention, as this would help prevent the worsening of the condition. The research is done to solve stroke prediction problem, which may be divided into a number of sub-problems such as an individual's predisposition to develop stroke. To attain this objective, a multiturn dataset consisting of various health features, such as age, gender, hypertension, and glucose levels, takes a central role. A multiple approach was put forward concentrating on integrating the machine learning techniques, such as Logistic Regression, Naive Bayes, K-Nearest Neighbors, and Support Vector Machine (SV), together to develop an ensemble machine called Neuro-Health Guardian. The hypothesis "Neuro-Health Guardian Model" integrates these algorithms into one, purported to make stroke prediction more accurate. The topic dives into each instance of preparation of data for analysis, data visualization techniques, selection of the right model, training, testing, ensembling, evaluation, and prediction. The models are validated with error rate accounted from their accuracy, precision, recall, F1 score, and finally confusion matrices for a look. The study's result is showing that the ensemble model that combines the multiple algorithms has the edge over them and this is evidently by the fact that it can predict stroke rises. Additionally, accuracy, precision, recall, and F1 scores are measured in all models and the comparison is done to provide a clear comparison of the models' performance. In short, the article presented the formation of the ongoing stroke prediction that revealed the ensemble model as a good anticipation. Precise stroke predisposition forecasting can assist in early intervention thereby preventing stroke-related deaths, and limiting disability burden by stroke. The conclusions that have come out of this study offer a great action item for the development of predictive models related to stroke prevention and treatment.

As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.

Memory Denial of Service (M-DoS) attacks refer to a class of cyber-attacks that aim to exhaust the memory resources of a system, rendering it unavailable to legitimate users. This type of attack is particularly dangerous in cloud computing environments, where multiple users share the same resources. Detection and mitigation of M-DoS attacks in real-time is a challenging task, as they often involve a large number of low-rate requests, making it difficult to distinguish them from legitimate traffic. Several real-time detection schemes have been proposed to identify and mitigate M-DoS attacks in cloud computing environments. These schemes can be broadly classified into two categories: signature-based and anomaly-based detection. Signature-based detection methods rely on the identification of specific patterns or characteristics of known M-DoS attack techniques, while anomaly-based detection methods identify abnormal behaviour that deviates from the normal pattern of usage. This study presents a hybrid model for real-time detection of cloud and MDOS attacks using SVM-KNN-LR. The dataset used in this study was collected from various sources and pre-processed to extract relevant features for attack detection. A feature selection process was also applied to identify the most important features for attack detection. The hybrid model achieved an accuracy of 96%, outperforming other individual models such as SVM, KNN, LR, Naive Bayes, Decision Trees, Extra Trees, Bagging Trees, and Random Forests. Confusion matrices were also used to evaluate the performance of each model. In the discussion section, we examined the performance of the hybrid model in detecting MDOS attacks and found that it had a high precision score of 0.97. However, the recall score was lower at 0.87, indicating that the model was not able to detect all instances of MDOS attacks.

Development and use of IoT devices have grown significantly in recent years. Many departments such as smart homes, smart healthcare, smart sports analysis, and different smart industries use IoT-based devices. In IoT devices, traffic is a very important part. IoT device traffic is distinct from traditional device traffic in various respects. In this study, 41 Internet-of-Things (IoT) devices were used. IoT devices provided 13 network traffic attributes to construct a multiclass classification model. Pre-processing techniques such as Normalization and Scaling of Dataset were used to pre-process the raw data acquired. Features can be extracted from text data using feature engineering algorithms. After stratification, the dataset contains 117,423 feature vectors utilized to develop the classification model further. Multiple performance metrics were used to demonstrate how well LogitBoost algorithms perform in this research. Using ensemble-based hybrid machine learning models to detect network anomalies in this research is an early step in developing an intrusion detection system (IDS). The main objective of this study is to detect attacks and anomalies in an IoT environment in a smart home. We have proposed a novel approach to developing LogitBoost algorithms, i.e., Logi-XGB, Logi-GBC, Logi-ABC, Logi-CBC, Logi-LGBM, and Logi-HGBC. After applying LogitBoost algorithms to the dataset for the classification, Logi-XGB scored 80.20% accuracy, and Logi-GBC scored 77.80% accuracy. Logi-ABC scored 80.33% accuracy. Logi-CBC scored the highest accuracy of 85.66%. Logi-LGBM and Logi-HGBC scored the same accuracy of 81.37%. Compared with previous LogitBoost algorithms implemented in previous studies, our proposed Logi-CBC has scored the highest accuracy on the given dataset.

Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

The Internet of Railways (IoR) network is made up of a variety of sensors, actuators, network layers, and communication systems that work together to build a railway system. The IoR’s success depends on effective communication. A network of railways uses a variety of protocols to share and transmit information amongst each other. Because of the widespread usage of wireless technology on trains, the entire system is susceptible to hacks. These hacks could lead to harmful behavior on the Internet of Railways if they spread sensitive data to an infected network or a fake user. For the previous few years, spotting IoR attacks has been incredibly challenging. To detect malicious intrusions, models based on machine learning and deep learning must still contend with the problem of selecting features. k-means clustering has been used for feature scoring and ranking because of this. To categorize attacks in two datasets, the Internet of Railways and the University of New South Wales, we employed a new neural network model, the extended neural network (ENN). Accuracy and precision were among the model’s strengths. According to our proposed ENN model, the feature-scoring technique performed well. The most accurate models in dataset 1 (UNSW-NB15) were based on deep neural networks (DNNs) (92.2%), long short-term memory LSTM (90.9%), and ENN (99.7%). To categorize attacks, the second dataset (IOR dataset) yielded the highest accuracy (99.3%) for ENN, followed by CNN (87%), LSTM (89%), and DNN (82.3%).

In the Internet of Things (IoT) era, the mobile crowd sensing system (MCS) has become increasingly important. The Internet of Things Auto (IOTA) has evolved rapidly in practically every technology field over the last decade. IOTA-based mobile crowd sensing technology is being developed in this study using machine learning to detect and prevent mobile users from engaging in fake sensing activities. It has been determined through testing and evaluation that our method is effective for both quality estimation and incentive allocation. Using the IOTA Bottleneck dataset, multiple performance metrics were used to demonstrate how well logit-boosted algorithms perform. After applying logit-boosted algorithms on the dataset for the classification, Logi-XGB scored 95.7 percent accuracy, while Logi-GBC scored 90.8 percent accuracy. As a result of this, Logi-ABC had an accuracy rate of 89%. Logi-CBC, on the other hand, got the highest accuracy of 99.8%. Logi-LGBM and Logi-HGBC both scored 91.37 percent accuracy, which is identical. On the given dataset, our Logi-CBC algorithm outperforms earlier Logit-boosted algorithms in terms of accuracy. Using the new IoTA-Botnet 2020 dataset, a new proposed methodology is tested. In comparison to prior Logit-boosted algorithms, the new model Logi-CBC has a highest detection accuracy of 99.8%.

Daily millions of images are uploaded and download to the web, as a result the data is available in the paperless form in the computer system for organization. Nowadays, with the help of powerful computer software such as Photoshop and Corel Draw, it is very easy to alter the contents of the authenticated image without leaving any clues. This led to a big problem due to the negative impact of image splicing. It is highly recommended to develop image tampering detection technique to recognize the authentic and temper images. In this paper, we propose an enhanced technique for blind images splicing by combing Discrete Cosine Transform Domain (DTC) and Markov feature in the spatial domain. Moreover, Principal Component Analysis (PCA) is used to select the most significant features. Finally, Support Vector Machine (SVM) is applied to classify the image as being tempered or genuine on the publicly available dataset using ten-fold cross-validation. By applying different statistical techniques, the results showed that the proposed technique performs better than other available detection techniques in the literature.