A comprehensive framework for multi-modal hate speech detection in social media using deep learning.

As social media platforms evolve, hate speech increasingly manifests across multiple modalities, including text, images, audio, and video, challenging traditional detection systems focused on single modalities. Hence, this research proposes a novel Multi-modal Hate Speech Detection Framework (MHSDF) that combines Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze complex, heterogeneous data streams. This hybrid approach leverages CNNs for spatial feature extraction, such as identifying visual cues in images and local text patterns, and Long Short Term Memory (LSTM) for modeling temporal dependencies and sequential information in text and audio.

Efficient traffic management with adaptive SDN in vehicular networks.

Improving road safety and easing congestion require effective real-time traffic data analysis and management. A crucial part of intelligent transportation systems, vehicular ad hoc networks (VANETs) deal with issues like inconsistent data from erratic vehicle movements and frequent topology changes. In order to develop a responsive and adaptable network management architecture for VANETs, this study makes use of Software-Defined Networking (SDN).

Compact circularly polarized dual band antenna with modified patch for ISM and V2X communication systems.

This paper introduces a novel design and development of a compact circularly polarized antenna operating at two distinct frequency bands, 2.5 GHz and 6 GHz. The innovation lies in using a modified circular patch and a uniquely designed feeding element, which optimize circular polarization performance while maintaining a low-profile form factor.

Analysis of energy, exergy, and emissions in a diesel engine powered by cotton silk seed oil biodiesel with varying injection timings.

To mitigate the exhaustion of hydrocarbon fuels and the rise of pollutants, one can use biofuels in diesel engines for power generation. This study examines the possibility of enhancing the performance and reducing the pollutions of a compressed ignition engine using methyl ester made from cotton silk seed oil. This study aimed to assess the energy, energy efficiency, and emissions (3E) of the Kirloskar engine operating at 1800 rpm.

Palladium(II) complexes containing andrographolide appended N,O heterocyclic chelators: Investigation of anti-oxidant, anti-cancer and apoptotic activities.

A series of new Pd(II) complexes were synthesized from the reaction of andrographolide appended hydrazide derivatives with potassium tetrachloropalladate K[PdCl]. The formation of the complexes was confirmed through structural assessments conducted using various spectroscopic techniques. From the spectral studies we confirmed that the ligands coordinated to Pd(II) ion via amine nitrogen and enone oxygen.

Addressing a decision problem through a bipolar Pythagorean fuzzy approach: A novel methodology applied to digital marketing.

Decision-making in real-world scenarios faces uncertainty. Fuzzy theory has been a means to represent such uncertainty. In this study, we propose an approach that incorporates bipolarity into multi-criteria decision-making processes applied to digital marketing.

Abnormal events detection using spatio-temporal saliency descriptor and fuzzy representation analysis.

In recent years, the research on abnormal events detection is a significant work in surveillance video. Many researchers have been attracted by this work for the past two decades. As a result, several abnormal event detection approaches have been developed.

Heuristic machine learning approaches for identifying phishing threats across web and email platforms.

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques.

Numerical analysis on the fate and transport of TiO nanoparticles in fractured porous media: Addressing intrinsic heterogeneities in sorptive mass transfer.

The aggregation and slow migration of nanoparticles in aqueous media have caused serious concerns about their fate and impacts in the subsurface environment. Anthropogenic release and distribution of TiO nanoparticles (TNP) have immense potential for surface adsorption, occlusion, impregnation, bioaccumulation, and phase partition into various environmental compartments, and the actual risks in their interactions are still unknown. In an attempt to realize the extent of source zone migration of TNP in a fracture-skin-matrix (F-S-M) medium, a numerical model is developed and analyzed for sensitivity of certain features of the flow field.

A novel artificial neural network based selection harmonic reduction technique for single source fed high gain switched capacitor coupled multilevel inverter for renewable energy applications.

Multilevel inverters (MLIs) are commonly used in renewable energy systems for their high-quality output, low total harmonic distortion (THD), and reduced component count. This study presents a high-gain, single-source MLI designed for renewable applications like solar or wind power. It features a novel topology with twice the voltage-boosting factor, utilizing a single DC source.

Pulsed directed energy deposition-arc technology for depositing stainless steel 309L: Microstructural, elements distribution, and mechanical characteristics.

In recent years, additive manufacturing has increased in prominence as a primary method of manufacturing around the globe. Modern metal additive manufacturing presents an innovative approach to manufacturing complex structures for the aerospace, energy, and construction sectors, due to technological advancements. In the present study, the austenitic stainless steel 309L (SS-309L) thick wall component was manufactured employing pulsed current gas tungsten arc welding (PC-GTAW) WAAM technology.

Investigating topic modeling techniques through evaluation of topics discovered in short texts data across diverse domains.

The online channel has affected many facets of an individual's identity, commercial, social policy, and culture, among others. It implies that discovering the topics on which these brief writings are focused, as well as examining the qualities of these short texts is critical. Another key issue that has been identified is the evaluation of newly discovered topics in terms of topic quality, which includes topic separation and coherence.

A fusion of deep neural networks and game theory for retinal disease diagnosis with OCT images.

Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography).

Extraction and characterization of a new natural cellulosic fiber from the Habara Plant Stem (HF) as potential reinforcement for polymer composites.

In this present work, characterize the chemical, physical, thermal and Morphological behaviour of raw and alkali-treated (NaOH 5 %,10 % & 15 %) new natural cellulose Habara plant stem fiber (HF). From the chemical analysis, the 10 % alkali-treated HF obtained cellulose (67.9 %), hemicellulose (12.

Modification of bentonite with black cotton soil and carboxyl methyl cellulose for the enhancement of hydraulic performance of geosynthetic clay liners.

Geosynthetic clay liners (GCLs) are mostly used as flow barriers in landfills and waste containments due to their low hydraulic conductivity to prevent the leachate from reaching the environment. The self-healing and swell-shrink properties of soft clays (expansive soils) such as bentonite enable them as promising materials for the GCL core layers. However, it is important to modify their physico-chemical properties in order to overcome the functional limitations of GCL under different hydraulic conditions.

Levenberg-Marquardt deep neural watermarking for 3D mesh using nearest centroid salient point learning.

Watermarking is one of the crucial techniques in the domain of information security, preventing the exploitation of 3D Mesh models in the era of Internet. In 3D Mesh watermark embedding, moderately perturbing the vertices is commonly required to retain them in certain pre-arranged relationship with their neighboring vertices. This paper proposes a novel watermarking authentication method, called Nearest Centroid Discrete Gaussian and Levenberg-Marquardt (NCDG-LV), for distortion detection and recovery using salient point detection.

Microwave engineered NiZrO@GNP as efficient electrode material for energy storage applications.

Supercapacitors (SCs) have emerged as attractive energy storage devices due to their rapid charge/discharge rates, long cycle life, and high-power density. However, the development of innovative electrode materials to achieve high-performance remains crucial to meet future requirements in supercapacitor technology. In this work, we have explored the potential of a microwave-engineered NiZrO@GNP composite as a promising electrode material for SCs.

Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the Hybrid LXGB (Long short-term memory Extreme Gradient Boosting) Model, a novel approach for multimodal sentiment analysis that merges the strengths of long short-term memory (LSTM) and XGBoost classifiers. The primary objective is to address the intricate task of understanding emotions across diverse data sources, such as textual data, images, and audio cues.

Spectrum Sensing Based on Hybrid Spectrum Handoff in Cognitive Radio Networks.

The rapid advancement of wireless communication combined with insufficient spectrum exploitation opens the door for the expansion of novel wireless services. Cognitive radio network (CRN) technology makes it possible to periodically access the open spectrum bands, which in turn improves the effectiveness of CRNs. Spectrum sensing (SS), which allows unauthorized users to locate open spectrum bands, plays a fundamental part in CRNs.

Breast Cancer Classification Using Synthesized Deep Learning Model with Metaheuristic Optimization Algorithm.

Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment.

3D Road Lane Classification with Improved Texture Patterns and Optimized Deep Classifier.

The understanding of roads and lanes incorporates identifying the level of the road, the position and count of lanes, and ending, splitting, and merging roads and lanes in highway, rural, and urban scenarios. Even though a large amount of progress has been made recently, this kind of understanding is ahead of the accomplishments of the present perceptual methods. Nowadays, 3D lane detection has become the trending research in autonomous vehicles, which shows an exact estimation of the 3D position of the drivable lanes.

Discovery of plant-based phytochemical as effective antivirals that target the non-structural protein C of the Nipah virus through computational methods.

Nipah Virus (NiV) belongs to the Paramyxoviridae family and was first identified during an outbreak in Malaysia. Some initial symptoms include mild fever, headache and sore throat, which could escalate to respiratory illness and brain inflammation. The mortality rate of NiV infection can range from 40% to 75%, which is quite high.

Correction to: Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals.

[This corrects the article DOI: 10.1007/s11571-022-09817-y.].