Computer Based Detection of Normal and Alcoholic Signals Using Discrete Fourier Transform

Alcoholism is a severe, disorder that affects; the functionality of neurons in the central nervous system and leads to the loss of .health and wealth. The suggested technique applies statistical and fractal dimension (FD) features to classify alcoholic and normal subjects using eight channels under an SF-based machine learning architecture. Electroencephalogram (EEG) signals are placed in a framework and separated into different EEG bands using an orthogonal wavelet filter. The following three classification approaches are used to classify the alcoholic and normal patterns of EEG data: least-square support vector machine, vector machine (SVM), and Naïve Bayesian. Results showed that the best classification method was SVM with a sensitivity of 0.9267%, an accuracy of 0.9892%, and a specificity of 0.9916%

Detection of Covid-19 and chest pneumonia based on X-ray images using Deep-Transfer Learning

لقي العديد من الأشخاص حتفهم نتيجة تفشي فيروس كورونا في عام 2019 (كوفيد-19)، والذي أثر أيضًا على ملايين آخرين في جميع أنحاء العالم. تنتشر العدوى بسرعة. ولذلك، فإن التكنولوجيا التي تتيح الكشف السريع عن الفيروسات ستوفر لمتخصصي الرعاية الصحية المساعدة التي هم في أمس الحاجة إليها. تهدف هذه الدراسة إلى التعرف على مرض كوفيد-19 من صور الأشعة السينية للأشخاص الأصحاء والمصابين بالالتهاب الرئوي باستخدام نموذج VGG16 المعدل. حقق النموذج المقترح نتائج أفضل من الدراسات السابقة المقدمة بدقة 99.13% واستدعاء 99% ودقة 98.70%.Numerous people have died as a result of the coronavirus outbreak in 2019 (COVID-19), which also affected millions of others worldwide. The infection spreads quickly. Therefore, technology that enables quick virus detection will offer healthcare professionals much-needed assistance. This study aims to identify COVID-19 disease from X-ray images of healthy and infected people with pneumonia by using a modified VGG16 model. The proposed model achieved better results than previous studies presented with an accuracy of 99.13%, a recall of 99%, and a precision of 98.70%

Artificial Intelligence Systems and Medical Negligence: An Overview and Perspective of a Case Study in Ghana Civil Procedure Rules, 2004 (C.I. 47)

Objective: This article discusses the evidentiary requirements for demonstrating scientific negligence under Ghana’s Civil Procedure Rules 2004 (C.I. 47) in the context of emerging artificial intelligence (AI) diagnostic and treatment structures.Method: Legal analysis examines gaps in satisfying burden of proof and standards of evidence, obstacles that restrict evidence collection on AI device deficiencies, and suggestions for adapting legal responsibility policies to AI’s technical opacity.Findings: The present inability to interrogate algorithms, limited access to proprietary training data and methods, lack of diagnosed standards of care for software-based decision-makers, and shortage of qualified professional witnesses pose massive evidentiary challenges for plaintiffs seeking to confirm AI negligence.Conclusions/Recommendations: Standards strengthening algorithmic transparency, auditability, and explainability could ease evidentiary burdens for affected patients. Strict liability schemes and IP protections balancing public safety and innovation aims need to be considered moving forward.Scientific Contributions: This work adapts traditional medical liability systems to today’s realities of increasing reliance on AI in health care and proposes several improvements

Arabic Crime Tweet Filtering and Prediction Using Machine Learning

Crime is undeniably rising, thus negatively affecting countries’ economies. Despite several efforts to study crime prediction to reduce crime rates, few studies take the timeline factor into account when extracting crime-related tweets to predict crime. Aiming to predict Arabic crime tweets on Twitter/X, this study predicts crimes after analyzing social sentiment—that is, whether a tweet raises positive, negative, or neutral feelings—and filters the tweets based on crime behavior through an intelligent dictionary built through a genetic algorithm. The study uses a variety of machine learning (ML) models—random forest, logistic regression, and decision trees—which are assessed according to their accuracy, precision, recall, and F1 scores to guarantee robustness and dependability in crime prediction. The accuracy after filtering crimes based on an intelligent dictionary is 97% for decision tree, 97% for random forest, and 94.43% for logistic regression. This research will provide insight into potential crime attitudes and public opinion toward safety and law enforcement

Digital Image Forgery Detection And Localization Using The Innovated U-Net

A reliable image copy–move forgery detection approach adaptable to different scenarios of tampering with color images is crucial for many applications. Different methods and solutions have been effectively proposed, but they are still subject to false positive/negative detections and cannot handle the variety of copy–move forgeries. In this paper, a machine learning model that combines ResNet 50 and U-net architectures for automatic image forgery detection in color image(s) is presented. The proposed system is inspired by the ResNet 50 architecture as an encoder and the U-Net architecture as a decoder. The encoder function implies applying convolution and normalizing for feature extraction. Conversely, the decoder functions is locating the spatial features. The decoder in the U-Net network comprises multiple decoder blocks, which are connected to corresponding encoder blocks by employing concatenate layers. A binary mask is then produced to represent the tampered regions in the image. Quantitative experimental results on two standard public datasets and a comparison with state-of-the-art methods demonstrate the effectiveness and robustness of the proposed model

Enhanced Hybrid Algorithm for E-AbdulRazzaq and Fast Online Hybrid Matching Algorithms for Exact String Matching

Algorithms for string matching are considered one of the most extensively researched topics in the field of computer science due to their substantial role in various applications, such as information retrieval, editing, security, firewalls, and biological applications. String matching involves examining the optimal alignment by comparing the characters in the pattern and the text. Over the past two decades, it has gained considerable attention due to technological advancements. The need to address string-matching problems has also emerged because of its wide-ranging applications. This study presents the E-ARFO hybrid string-matching algorithm, which combines the best features of two original algorithms, namely, E-AbdulRazzaq and fast online hybrid matching. Compared with other algorithms, the proposed method demonstrates outstanding performance in terms of the number of attempts and character comparisons conducted across multiple databases, including DNA and protein sequences. Results indicate that irrespective of the number of attempts or character comparisons made, E-ARFO consistently ranks first for short and lengthy patterns in most databases. Results also reveal reduced runtimes and competitive character comparisons. Moreover, results underscore the potential effect of E_ARFO on computational biology, offering a new paradigm for precision and efficiency in string matching

Maximum Error Insertion (MEI): A Novel Benchmarking Method for Data Hiding Algorithms

Data hiding is becoming increasingly important due to the growing threat to the privacy and security of data from intruders and hackers. This situation is accompanied by the advancement in artificial intelligence applications designed to reveal hidden data, making it difficult to choose the most appropriate hiding approach from those presented in literature. The benchmarking method serves as an important roadmap for making decisions. We propose a distinct plain benchmarking method called maximum error insertion (MEI) benchmarking. This approach intends to hide data using maximum error insertion. The MEI refers to the maximum amount of distortion that can be added to host data (such as image or audio) while still ensuring the successful retrieval of hidden data. The maximum error that can be generated by each hiding algorithm is intentionally inserted to the media file, thus giving us maximum error, maximum capacity, and maximum sensitivity to signal processing attacks. Investigation of the two hiding algorithms demonstrates their applicability and precision, and their implementation significantly enhances the reliability of results during the benchmarking stage

Advancing Early Warning Systems for Fire Detection: A Comprehensive Approach in Machine Learning

This research conducts a comprehensive investigation of the efficacy of various machine learning algorithms for fire detection. The algorithms that were examined include logistic regression, decision tree, random forest, support vector classifier, gradient boosting, K-nearest neighbors, Gaussian naive Bayes, multilayer perceptron classifier, and XGBoost classifier. Through in-depth experiments, this study rigorously assesses the performance of these algorithms in identifying and predicting fires based on pertinent input features. Among the algorithms that were investigated, logistic regression is the best performer, with a high accuracy rate of 99%. The findings from this research offer valuable insights for optimizing fire detection systems, providing a nuanced understanding of the practical applicability of machine learning techniques in real-time fire monitoring scenarios. The primary objectives of this study are to elucidate specific challenges in fire detection, evaluate the performance of various machine learning algorithms, and contribute to the foundational knowledge that is essential for enhancing fire management strategies. The research addresses the limited precision of existing fire detection systems and aims to rectify this issue through a systematic exploration of advanced machine learning approaches. The overarching goal is to bolster the foundations of fire management, facilitating the development of proactive measures and prompt responses to mitigate the profound impact of wildfires. By presenting a detailed examination of the strengths and weaknesses of various machine learning algorithms, this research strives to foster a robust and effective approach to fire detection, thereby advancing the field and ensuring the safety of communities at risk.

Security Monitoring in Smart Homes Using IoT Data Analytics

With the rapid proliferation of Internet of Things devices in smart homes, the necessity of robust security measures has become clearer than ever. This study aims to apply data analytics to enhance security monitoring within smart home environments. By leveraging the wealth of data generated by IoT devices, the study also aims to create an intelligent system that is capable of proactively identifying and addressing potential security threats through Android mobile devices.Therefore, privacy concerns were addressed through encryption and anonymization methods to protect sensitive information. The study evaluates the effectiveness of the developed security monitoring system through simulated and realistic scenarios, thereby highlighting its ability to detect and mitigate a wide range of security threats. Thus, the research contributes to the development of smart home security by providing a smart, data-driven approach to monitoring security incidents. In the evolving landscape of smart homes, the proposed framework forms a cornerstone for ensuring the safety and privacy of residents within this interconnected ecosystem

Credit Fraud Recognition Based on Performance Evaluation of Deep Learning Algorithm

Over time, the growth of credit cards and the financial data need credit models to support banks in making financial decisions. So, to avoid fraud in internet transactions which increased with the growth of technology it is crucial to develop an efficient fraud detection system. Deep Learning techniques are superior to other Machine Learning techniques in predicting the customer behavior of credit cards depending on the missed payments probability of customers. The BiLSTM model proposed to train on Taiwanese non-transactional dataset for bank credit cards to decrease the losses of banks. The Bidirectional LSTM reached 98% accuracy in fraud credit detection compared with other Machine Learning techniques