Harnessing AI and analytics to enhance cybersecurity and privacy for collective intelligence systems.

Collective intelligence systems like Chat Generative Pre-Trained Transformer (ChatGPT) have emerged. They have brought both promise and peril to cybersecurity and privacy protection. This study introduces novel approaches to harness the power of artificial intelligence (AI) and big data analytics to enhance security and privacy in this new era. Contributions could explore topics such as: leveraging natural language processing (NLP) in ChatGPT-like systems to strengthen information security; evaluating privacy-enhancing technologies to maximize data utility while minimizing personal data exposure; modeling human behavior and agency to build secure and ethical human-centric systems; applying machine learning to detect threats and vulnerabilities in a data-driven manner; using analytics to preserve privacy in large datasets while enabling value creation; crafting AI techniques that operate in a trustworthy and explainable manner. This article advances the state-of-the-art at the intersection of cybersecurity, privacy, human factors, ethics, and cutting-edge AI, providing impactful solutions to emerging challenges. Our research presents a revolutionary approach to malware detection that leverages deep learning (DL) based methodologies to automatically learn features from raw data. Our approach involves constructing a grayscale image from a malware file and extracting features to minimize its size. This process affords us the ability to discern patterns that might remain hidden from other techniques, enabling us to utilize convolutional neural networks (CNNs) to learn from these grayscale images and a stacking ensemble to classify malware. The goal is to model a highly complex nonlinear function with parameters that can be optimized to achieve superior performance. To test our approach, we ran it on over 6,414 malware variants and 2,050 benign files from the MalImg collection, resulting in an impressive 99.86 percent validation accuracy for malware detection. Furthermore, we conducted a classification experiment on 15 malware families and 13 tests with varying parameters to compare our model to other comparable research. Our model outperformed most of the similar research with detection accuracy ranging from 47.07% to 99.81% and a significant increase in detection performance. Our results demonstrate the efficacy of our approach, which unlocks the hidden patterns that underlie complex systems, advancing the frontiers of computational security.

Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure.

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.

Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification.

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.

Enhanced Pelican Optimization Algorithm with Deep Learning-Driven Mitotic Nuclei Classification on Breast Histopathology Images.

Breast cancer (BC) is a prevalent disease worldwide, and accurate diagnoses are vital for successful treatment. Histopathological (HI) inspection, particularly the detection of mitotic nuclei, has played a pivotal function in the prognosis and diagnosis of BC. It includes the detection and classification of mitotic nuclei within breast tissue samples. Conventionally, the detection of mitotic nuclei has been a subjective task and is time-consuming for pathologists to perform manually. Automatic classification using computer algorithms, especially deep learning (DL) algorithms, has been developed as a beneficial alternative. DL and CNNs particularly have shown outstanding performance in different image classification tasks, including mitotic nuclei classification. CNNs can learn intricate hierarchical features from HI images, making them suitable for detecting subtle patterns related to the mitotic nuclei. In this article, we present an Enhanced Pelican Optimization Algorithm with a Deep Learning-Driven Mitotic Nuclei Classification (EPOADL-MNC) technique on Breast HI. This developed EPOADL-MNC system examines the histopathology images for the classification of mitotic and non-mitotic cells. In this presented EPOADL-MNC technique, the ShuffleNet model can be employed for the feature extraction method. In the hyperparameter tuning procedure, the EPOADL-MNC algorithm makes use of the EPOA system to alter the hyperparameters of the ShuffleNet model. Finally, we used an adaptive neuro-fuzzy inference system (ANFIS) for the classification and detection of mitotic cell nuclei on histopathology images. A series of simulations took place to validate the improved detection performance of the EPOADL-MNC technique. The comprehensive outcomes highlighted the better outcomes of the EPOADL-MNC algorithm compared to existing DL techniques with a maximum accuracy of 97.83%.

Chaotic Mapping Lion Optimization Algorithm-Based Node Localization Approach for Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion Optimization Algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

Bio-Inspired Artificial Intelligence with Natural Language Processing Based on Deceptive Content Detection in Social Networking.

In recent research, fake news detection in social networking using Machine Learning (ML) and Deep Learning (DL) models has gained immense attention. The current research article presents the Bio-inspired Artificial Intelligence with Natural Language Processing Deceptive Content Detection (BAINLP-DCD) technique for social networking. The goal of the proposed BAINLP-DCD technique is to detect the presence of deceptive or fake content on social media. In order to accomplish this, the BAINLP-DCD algorithm applies data preprocessing to transform the input dataset into a meaningful format. For deceptive content detection, the BAINLP-DCD technique uses a Multi-Head Self-attention Bi-directional Long Short-Term Memory (MHS-BiLSTM) model. Finally, the African Vulture Optimization Algorithm (AVOA) is applied for the selection of optimum hyperparameters of the MHS-BiLSTM model. The proposed BAINLP-DCD algorithm was validated through simulation using two benchmark fake news datasets. The experimental outcomes portrayed the enhanced performance of the BAINLP-DCD technique, with maximum accuracy values of 92.19% and 92.56% on the BuzzFeed and PolitiFact datasets, respectively.

Disadvantaged Economic Conditions and Stricter Border Rules Shape Afghan Refugees' Ethnobotany: Insights from Kohat District, NW Pakistan.

The study of migrants' ethnobotany can help to address the diverse socio-ecological factors affecting temporal and spatial changes in local ecological knowledge (LEK). Through semi-structured and in-depth conversations with ninety interviewees among local Pathans and Afghan refugees in Kohat District, NW Pakistan, one hundred and forty-five wild plant and mushroom folk taxa were recorded. The plants quoted by Afghan refugees living inside and outside the camps tend to converge, while the Afghan data showed significant differences with those collected by local Pakistani Pathans. Interviewees mentioned two main driving factors potentially eroding folk plant knowledge: (a) recent stricter border policies have made it more difficult for refugees to visit their home regions in Afghanistan and therefore to also procure plants in their native country; (b) their disadvantaged economic conditions have forced them to engage more and more in urban activities in the host country, leaving little time for farming and foraging practices. Stakeholders should foster the exposure that refugee communities have to their plant resources, try to increase their socio-economic status, and facilitate both their settling outside the camps and their transnational movement for enhancing their use of wild plants, ultimately leading to improvements in their food security and health status.

Food Security beyond Cereals: A Cross-Geographical Comparative Study on Acorn Bread Heritage in the Mediterranean and the Middle East.

This article aims to contribute to the limited literature on traditional gastronomic knowledge concerning acorn-based bread by ethnographically documenting the ingredients, preparation techniques and consumption practices of baked goods made from acorn seeds and flour that are still used today or at least still present in living memory. A qualitative comparative case method was adopted, and ethnographic data were gathered from 67 people in six selected Mediterranean, Central Asian and Middle Eastern countries. The analysis highlighted distinct trajectories in the development of acorn-based bread, showing some differences in terms of ingredients, preparation techniques and baking methods in the two cultural and geographical macro-regions. By exploring the evolution of the alimentary role of acorn bread in the past century, our findings also support the hypothesis that the product, at least during the last two centuries, has mostly been used as a famine food. By acknowledging the cultural importance of acorn fruits and acorn-based products, this study suggests that the rediscovery of acorn-based products and associated traditional knowledge may foster the sustainable development of rural and marginal regions in the Mediterranean, Middle East and Central Asia. This could help to reinforce the resilience of local communities and thus increase food security. Furthermore, reassessing acorns as a foodstuff may aid in developing innovative products in line with emerging trends in the food sector, which is looking for new non-cereal-based bakery products and other novel culinary applications.

Natural history of rheumatic heart disease, a 12 years observational study "penicillin bites the muscle but heals the heart".

BACKGROUND: Rheumatic Heart Disease (RHD) is amajor cause of cardiovascular morbidity and mortality in young individuals, in developing countries. Long term studies regarding natural history of RHD in Pakistan have not been reported in literature. We present our follow up observations on RHD patients at the end of 12 years since our first survey conducted in rural communities in 1994. METHODS: Our study patients were known cases of RHD, diagnosed in cross sectional survey of rural areas of Rahimyar Khan in 1994. Second survey conducted in 2006, in which these RHD patients were evaluated in detail with history/Physical examination, 12 lead ECG, X-ray chest PA view and Echo/Doppler studies at Sheikh Zayed Medical College Rahimyar Khan. RESULTS: Out of 57 patients enrolled in 1994, 21 patients (37%) were available for further evaluation. Overall mortality was 23%. Male to female ratio was 1:1.62. Age ranges between 20- 80 years with mean of 43 years. Only 6 patients (29%) were taking rheumatic prophylaxis (RP) and six patients had recurrent RF. Five patients (24%) developed new aortic regurgitation (AR) and 38% increased in grade of severity of lesions on Echo (none of them was on RP). Regression of mild lesions noted in six patients (all of them were on RP). Two patients underwent surgery. 10% developed new atrial fibrillation. CONCLUSION: Unabated RF/RHD led to a very high mortality. Favourable out come observed with prophylaxis even for short period on mild or moderate RHD. Patients not on RP had severe diseases. This small study is a big blow to our claims of combating RF/RHD in the 21st century.