Decentralizing video copyright protection: a novel blockchain-enabled framework with performance evaluation.

Introduction: Digital content, including images and videos, is increasingly ruling the online world, and so multimedia services form a part of this modern life. However, the digital resources face significant problems, especially regarding copyright infringement. In such an instance, any modification without authority infringes intellectual property rights.

Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.

Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on performance metrics such as classification accuracy, precision and recall, they fail to assess whether the models are considering relevant features for decision-making. The main objective of this work is to develop and validate a comprehensive three-stage methodology that combines conventional performance evaluation with qualitative and quantitative evaluation of explainable artificial intelligence (XAI) visualizations to assess both the accuracy and reliability of deep learning models.

Collaborative filtering models an experimental and detailed comparative study.

Recommender systems have become indispensable tools in various domains, such as e-commerce, entertainment, and social media, for delivering personalized user experiences. Collaborative Filtering (CF) is an essential technique in RS that leverages user similarity patterns to suggest items which align with individual preferences. This study presents an experimental comparative analysis of collaborative filtering-based recommender system methods including memory-based methods (KNN variants), model-based approaches (SVD, SVD++, co-clustering), and techniques based on neural networks (NCF, DeepFM, LightGCN).

Integrating Google Maps and Smooth Street View Videos for Route Planning.

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations.

0.91 V reference, 3.3 ppm/°C Sub-BGR with second-order compensation and improved PSRR.

This work introduces the design and analysis of a Bandgap Reference (BGR) circuit with better temperature stability and reduced process variation. The second-order compensation method is implemented for design through an optimized error amplifier and a resistor network with a significantly better temperature coefficient performance. The startup mechanism is carefully designed for ensured strong and stable circuit performance under every variation of process-voltage-temperature (PVT).

A novel and efficient personalized stress detection technique using a deep learning model.

Stress is a type of mental tension or escalated psycho-physiological state of the human body caused by a problematic situation. It majorly affects adults and elders, which further leads to chronic health problems and heart-related diseases. Various techniques were developed to detect stress levels.

Multiscale feature tuned trans-DeepLabV3+ based semantic segmentation of aerial images using improved red piranha optimization algorithm.

The use of higher-resolution spatial aerial images for semantic segmentation in everyday tasks has increased due to recent advancements in remote sensing and several other applications. Nonetheless, supervised learning necessitates a substantial quantity of images with pixel-level labeling. Currently, available techniques, which are mostly Deep Semantic Segmentation Networks (DSSN), might not be appropriate for application domains with a dearth of labels containing targeted masks of outputs.

Lanthanide(III) Thiosemicarbazone Complexes: Synthesis and Emission Properties with Preliminary Polymer Film Investigations.

Ligand L, 2-((2-hydroxynaphthalen-1-yl)methylene)-N'-(-(2-hydroxynaphthalen-1-yl)methylene)hydrazine-1-carbothiohydrazide, synthesized through the condensation reaction of thiocarbohydrazide and o-hydroxy naphthaldehyde, characterized. Lanthanide complexes of the ligand L such as C (L-Nd), C (L-Gd) and C (L-Yb) were synthesised with co-ligand 1,10-phenanthroline. The photoluminescence properties of the synthesized complexes, including ligand-centred emission and metal-centred NIR emission, HR-ESI mass spectra, EPR spectra were also recorded.

Leaf disease detection and classification in food crops with efficient feature dimensionality reduction.

Computer vision heavily relies on features, especially in image classification tasks using feature-based architectures. Dimensionality reduction techniques are employed to enhance computational performance by reducing the dimensionality of inner layers. Convolutional Neural Networks (CNNs), originally designed to recognize critical image components, now learn features across multiple layers.

Hyperparameter tuned deep learning-driven medical image analysis for intracranial hemorrhage detection.

Intracranial haemorrhage (ICH) is a crucial medical emergency that entails prompt assessment and management. Compared to conventional clinical tests, the need for computerized medical assistance for properly recognizing brain haemorrhage from computer tomography (CT) scans is more mandatory. Various deep learning (DL) and artificial intelligence (AI) technologies have been successfully implemented for the analysis of medical images, namely grading of diabetic retinopathy (DR), breast cancer detection, skin cancer detection, and so on.

Mechanical properties and residual stresses in multi pass AISI 304 to AISI 1018 steel welds by SMAW and GTAW.

Welding is a highly reliable, efficient, and widely used technique for joining metallic structures. In this study, AISI 1018 Mild Steel (MS) and Stainless Steel (SS) AISI 304 were joined using Shielded Metal Arc Welding (SMAW) and Gas Tungsten Arc Welding (GTAW) multipass welding methods. Post-Weld Heat Treatment (PWHT) was performed on the weldments using an induction furnace to analyse the effects of PWHT on microstructural variations, mechanical characteristics and residual stresses of dissimilar weld joints.

Optimized predictive maintenance for streaming data in industrial IoT networks using deep reinforcement learning and ensemble techniques.

Predictive maintenance in Industrial IoT (IIoT) networks faces challenges due to dynamic conditions, device heterogeneity, and evolving data patterns. This paper introduces an ensemble-based framework combining Deep Reinforcement Learning (DRL), Random Forest (RF), and Gradient Boosting Machines (GBM) to improve fault prediction and maintenance efficiency. Key contributions include: (i) adaptive fault prediction using DRL, which dynamically learns from real-time sensor data to optimize maintenance decisions; (ii) robust fault classification via RF, addressing class imbalance in IIoT environments; and (iii) enhanced predictive accuracy through GBM, leveraging feature dependencies for better generalization.

Multi-camera spatiotemporal deep learning framework for real-time abnormal behavior detection in dense urban environments.

The emerging density in today's urban environments requires a strong multi-camera architecture for real-time abnormality detection and behavior analysis. Most of the existing methods tend to fail in detecting unusual behaviors due to occlusion, dynamic scene changes and high computational inefficiency. These failures often result in high rates of false positives and poor generalization for unseen anomalies.

Modified energy-based GAN for intensity in homogeneity correction in brain MR images.

Brain Magnetic Resonance image diagnostics employs image processing, but aberrations such as Intensity Inhomogeneity (IIH) distort the image, making diagnosis difficult. Clinical diagnostic methods must address IIH discrepancies in brain MR scans, which occur often. Accurate brain MR image processing is difficult but required for clinical diagnosis.

Extraction of carbon powder from pyrolysis of low-density polyethylene plastics and its application in composite laminates.

The escalating environmental concerns associated with plastic waste, particularly Low-Density Polyethylene (LDPE), have spurred research into sustainable recycling strategies. Pyrolysis has been developed as a viable technique for transforming LDPE into appreciated by-products, including carbon powder, which holds potential for advanced material applications. This study investigates the extraction of carbon powder from LDPE via pyrolysis and its subsequent utilization in composite laminates.

Sequence-similarity-based approach to SARS-CoV-2 genome sequence and lung cancer-related genes via multivariate feature extraction method.

The COVID-19 pandemic has prompted genomic studies linking SARS-CoV-2 and lung cancer-related genes. This study explores sequence similarity and motif patterns to assess disease susceptibility. We applied a data mining approach to compare human and SARS-CoV-2 genomes, revealing high sequence identity (0.

Polar code construction by estimating noise using bald hawk optimized recurrent neural network model.

Polar codes are making significant progress in error-correcting coding due to their ability to reach the limit of the Shannon capacity of communication channels, indicating great advancements in the field. Decoding errors are common in real communication channels with noise. The main objective of this study is to develop a recurrent neural network decoder for robust polar code construction with the Bald Hawk Optimization (RNN-based Decoder with BHO) model that can estimate the error in information bits.

Enhancing sarcasm detection in sentiment analysis for cyberspace safety using advanced deep learning techniques.

Social media has become an integral part of daily life, with platforms like Twitter serving as popular outlets for users to share information and express grievances. While social media offers numerous benefits, it can also be misused for cyberbullying, such as insults and harassment. This highlights the importance of detecting and mitigating cyberbullying to ensure a safe online environment.

Sentiment classification for telugu using transformed based approaches on a multi-domain dataset.

Sentiment analysis is an essential component of Natural Language Processing (NLP) in resource-abundant languages such as English. Nevertheless, poor-resource languages such as Telugu have experienced limited efforts owing to multiple considerations, such as a scarcity of corpora for training machine learning models and an absence of gold standard datasets for evaluation. The current surge of transformed based models in NLP enables the attainment of exceptional performance in many different tasks.

An efficient signature aggregation scheme with antenna hardware implementation for VANETs.

Vehicular ad-hoc networks (VANETs) plays a major role in enhancing road safety and traffic management. However, they suffer from security and scalability issues due to dynamic network conditions. Conventional signature aggregation schemes have high computational and communication cost limiting real-time efficiency.

Microfluidic self-quadruplexing SIW antenna with independent tuning and simultaneous sensing of liquid materials.

This paper presents a novel microfluidic substrate integrated waveguide (SIW) based self-quadruplexing antenna that integrates independent frequency tunability with liquid dielectric sensing capabilities. The proposed antenna utilized a modified Swastik-shaped slot and embedded microfluidic pockets to dynamically control resonance frequencies. By adjusting the arm lengths of the Swastik-shaped slot, the antenna achieved self-quadruplexing functionality with high port isolation better than 30 dB, and compact footprint of 0.

Electron-phonon coupling in two-dimensional Ruddlesden-Popper hybrid perovskites.

Two-dimensional lead halide perovskites are considered low-cost and relatively stable materials for high-efficiency photovoltaic applications. Photoexcitation of lead halide perovskite materials leads to the generation of charge carriers. Understanding the charge carrier dynamics, especially at the early stage of their generation, is crucial for improving photovoltaic efficiency.

An efficient attention Densenet with LSTM for lung disease detection and classification using X-ray images supported by adaptive R2-Unet-based image segmentation.

Lung diseases represent one of the most prevalent health challenges globally, necessitating accurate diagnosis to improve patient outcomes. This work presents a novel deep learning-aided lung disease classification framework comprising three key phases: image acquisition, segmentation, and classification. Initially, chest X-ray images are taken from standard datasets.

Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data.

Background: Depression, including Bipolar and Unipolar types, is a widespread mental health issue. Conventional diagnostic methods rely on subjective assessments, leading to possible underreporting and bias. Machine learning (ML) and deep learning (DL) offer automated approaches to detect depression using behavioral and demographic data.

Iterative segmentation and classification for enhanced crop disease diagnosis using optimized hybrid U-Nets model.

The major challenges that the agricultural sector faces are that with the kind of methodologies that exist, gross limitations may occur to the exact diagnosis of crop diseases. They are unable to achieve correct precision in disease classification, relatively lower accuracy, and delayed response time-all these obstacles result in a deficiency in effectual disease management and control. Our research proposes a new framework instigated and developed to improve crop disease detection and classification by multifaceted analysis.