Complex networks, represented as graphs, serve as powerful models for understanding real-world systems composed of interacting entities. These networks offer valuable insights into both their structural and dynamic properties. This study concentrates on three fundamental aspects of complex network analysis: centrality, link prediction, and community detection. Centrality focuses on identifying influential nodes within the network, link prediction aims to forecast potential future connections, and community detection uncovers cohesive substructures. Through a thorough review of relevant literature, an exploration of practical applications, and an evaluation of benchmark datasets, this work presents a comprehensive analysis of these critical challenges and assesses the performance of widely utilized algorithms.

Counterfeit products continue to pose a significant challenge to consumer safety and brand integrity worldwide. Traditional counterfeit detection techniques frequently lack the openness and effectiveness needed to properly address this widespread problem. In order to improve the identification of counterfeit goods, this work presents a novel technique that combines blockchain technology with barcode systems. Every product is given a unique blockchain barcode that contains vital information including its origin, manufacturing specifications, and supply chain history, thanks to the utilization of blockchain’s immutable ledger. Verification procedures are automated by smart contracts, guaranteeing the accuracy of product data and enabling real-time tracking of goods movements. By establishing a decentralized network, stakeholders across the supply chain, including manufacturers, distributors, retailers, and consumers, can securely access and authenticate product information. Customers are better equipped to make educated purchases because to this transparent and traceable system, which also helps to build customer confidence in the legitimacy of the goods. This work presents a thorough implementation technique for blockchain barcode technology, demonstrating how it might transform activities related to detecting counterfeit goods. Through empirical studies and case analysis, the effectiveness and practicality of the proposed solution are demonstrated, offering a promising avenue for bolstering consumer confidence and safeguarding against the proliferation of fake products in the global marketplace.

Forecasting used car prices is an important area of research. The demand for second-hand cars is increasing. This study offers a comparative analysis of different supervised Machine Learning (ML) algorithms for predicting costs. We evaluate Linear, Lasso, Ridge, XGBoost and Random Forest Regression models. Our findings show that Random Forest Regression performs well for individual car brands. It also significantly outperforms traditional regression models overall. This demonstrates the effectiveness of ensemble methods in handling complex data. We assessed each regression model’s performance using the R-Squared (R2) metric. Among all the models studied, Random Forest regression achieved the highest R² value of 0.90. Compared to earlier studies, our model considers more factors related to used cars and shows greater predictive accuracy.

Among the most rapidly developing areas in computer vision is object detection. Mask detection is the main objective of the effort. With the use of deep learning and computer vision techniques, this project offers a reliable method for mask identification that is implemented using RESNET architecture. Identifying faces and differentiating between people wearing masks and those without is the main goal. The model is refined via transfer learning on a customized dataset that includes annotated photos of faces that have been masked, masked incorrectly and unmasked faces. © 2024 Taylor & Francis Group, London.

Topic analysis is also known as topic detection or topic extraction, refers to ML method that categorizes larger text datasets into categories based on the individual text. It employs natural language processing to analyze human communication by breaking it down into components such as speech, words, sentences, and context, aiming to identify patterns and unveil underlying meanings within texts. This process aids in deriving insights and facilitating data-driven decisions. Within topic analysis, the primary machine learning techniques employed areas of focus include topic modeling and topic classification within this field. However, topic modeling encounters various challenges, specific to document properties. NLP is an integrative subject that merges CS, AI, and linguistics to construct systems capable of comprehending and processing human language. The prevalent machine using labeled data to categorize unlabeled data. This process relies on the knowledge gained during training to classify new data. In general, text classification methods handle predefined and finite categories such as predicting labels like credible or not credible for credibility assessment, or determining movie ratings (bad, okay, good) based on reviews. The difficulty in text classification arises from the predetermined set of topics or labels. When the topics are not known in advance, the concept of topic modeling becomes crucial. This statistical modeling approach is designed to identify abstract topics within a set of documents that lack predefined labels. By analyzing labelled data, this method extracts underlying topics.

Heart disease represents the leading cause of death globally. Timely diagnosis and treatment can prevent cardiovascular issues. An Electrocardiograms (ECG) serves as a diagnostic tool for identifying heart difficulties. Cardiovascular Disease (CVD) often gets identified through ECGs. Deep learning (DL) garners attention in healthcare due to its potential in swiftly diagnosing ECG anomalies, crucial for patient monitoring. Conversely, automatic CVD detection from ECGs poses a challenging task, wherein rule-based diagnostic models usually achieve top-notch performance. These models encounter complications in supervision vast volumes of diverse data, demanding widespread analysis and medical capability to ensure precise CVD diagnosis. OBJECTIVE: This study aims to enhance cardiovascular disease diagnosis by combining symptom-based detection and ECG analysis. METHODS: To enhance these experiments, we built a novel automated prediction method based on a Feed Forward Neural Network (FFNN) model. The fundamental objective of our method is to develop the accuracy of ECG diagnosis. Our strategy employs chaos theory and destruction analysis to combine optimum deep learning features with a well-organized set of ECG properties. In addition, we use the constant-Q non-stationary Gabor transform (CQNGT) to convert one-dimensional ECG data into a two-dimensional picture. A pre-trained FFNN processes this image. To identify significant features from the FFNN output that correspond with the ECG data, we employ pairwise feature proximity. RESULTS: According to experimental findings, the suggested system, FFNN-CQNGT, surpasses other state-of-the-art systems in terms of precision of 94.89%, computational efficiency of 2.114 ms, accuracy of 95.55%, specificity of 93.77%, and sensitivity of 93.99% and MSE 40.32%. CONCLUSION: Contributing an automated ECG-based DL system based on FFNN-CQNGT for early-stage cardiovascular disease identification and classification holds great potential for both patient care and public health.

In this era, technology has significantly simplified people's lives, and one of the recent advancements in artificial intelligence is deep learning. Deep learning has emerged as a field that enables the creation of intelligent software and machines capable of assisting individuals in their daily tasks. One notable application of deep learning is Human Activity Recognition (HAR). Deep learning, a subset of machine learning, is used effectively to identify human activities. In this project, we used a model based on Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN) to detect human activities. This model is trained on the UCF50 dataset, which allows rigorous testing and validation. A dataset is created from the main dataset (UCF 50) with 10 action categories, and further, the dataset is split into two parts: testing and validation. Using the subsequent dataset, the ConvLSTM model accuracy is 81.4%, and the LRCN model accuracy is 85.3%.

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We investigate how AI and ML might revolutionize the agricultural industry, particularly with regard to increasing crop output while decreasing input costs. Applying AI and ML technology has promise in a society struggling with population increase, climate change, and resource constraints. This study highlights the practical advantages of AI and ML in agriculture via a well-crafted research process, including data gathering, model creation, and assessment. The results show that AI and ML models are useful for forecasting agricultural yields, identifying illnesses, allocating resources efficiently, and assisting farmers with decision-making based on empirical evidence. Results like this highlight the importance of these technologies in advancing goals of efficiency, sustainability, and food safety. Additionally, the study acknowledges the significance of addressing ethical problems in AI deployment, guaranteeing equal access to these advancements. We should expect to see more research into cutting-edge methods, Internet of Things (IoT) integration, and accessible tools for subsistence farmers as we go further in the use of AI and ML in the agricultural sector. The full promise of AI and ML in designing a resilient, productive, and sustainable agricultural future requires collaborative efforts across stakeholders. In the struggle to feed the globe while protecting its resources, this study shines a bright light of optimism.

Remote underwater picture and video capture is used by marine biologists to monitor different fish species in their natural environments. This aids in their comprehension and forecasting of the responses of fish to fishing pressure, habitat degradation, and climate change. Having this knowledge is crucial for creating environmentally friendly, sustainable fisheries for human use. Humans, on the other hand, find it difficult and time-consuming to extract useful information from massive amounts of collected videos. Deep learning (DL) appears to have an issue with this. With the help of DL, marine biologists can rapidly and effectively parse massive amounts of film, uncovering specialized information that is not accessible via manual monitoring techniques. We present a two-step deep learning technique in this study that can recognize and classify temperate fishes without the use of pre-filtering. Every fish in a picture must first be identified, regardless of species or gender. For this, we employ the You Only Look Once (YOLO) object detection technique. The classification of each fish in the image is done in the second stage using a squeeze-and-excitation (SE)-designed convolutional neural network (CNN). Despite the short training sample size of temperate fishes, we use transfer learning to improve classification accuracy. For this, the fish classifier was trained using a public dataset, and the object detection model was trained using ImageNet. Both models were then updated with pertinent temperate fishes. Weights are always added both before and after a workout. The CNN-SE model performed admirably, with a 96.22% accuracy. Extensive comparative research revealed that the CNN-SE model outperformed more recent approaches.