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Characterization of bioactive compounds from Saraca asoca and their antibacterial activity against fish pathogens in Oreochromis niloticus
Saraca asoca, known for its therapeutic properties in Ayurveda, is the focus of this study, aiming to identify and quantify the bioactive compounds in its leaf extract using Gas Chromatography-Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FTIR). The study also investigates the antibacterial efficacy of methanol, ethanol and acetone extracts of S. asoca against fish pathogens in Oreochromis niloticus like Vibrio alginolyticus, Streptococcus pyogenes, Pseudomonas fluorescens and Aeromonas hydrophila through the well-diffusion method. GC-MS confirmed the presence of compounds such as 3- hydroxy biphenyl, n-hexa decanoic acid, oleic acid, octadecanoic acid, 4,5-diethyl octane and 9-tetradecen-1-ol. In contrast, FTIR spectra revealed several significant peaks, indicating the presence of specific functional groups in the S. asoca leaf fraction. The results exhibited high absorbance in the wavenumber ranges of 4000-3500 cm-1, 3000-2500 cm-1, 1800-1500 cm-1 and 1100-950 cm-1. The findings of the antibacterial assay suggest that the methanolic extract exhibited a strong inhibitory effect against bacterial pathogens, with zones of inhibition ranging from 6 0.21 to 18 0.57 mm in size. These results indicate that S. asoca leaf extract contains bioactive compounds effective against the pathogenic bacteria in O. niloticus, supporting the growing shift towards reducing antibiotic use in aquaculture. (2025), (Horizon e-Publishing Group). All rights reserved. -
Sustainable mosquito control: A tool in the fight against Aedes aegypti using Flemingia wightiana
Mosquito-borne diseases, particularly those transmitted by Aedes aegypti, have been proven to be a global health challenge. A. aegypti, a major vector of Zika virus, Dengue virus, Chikungunya, is traditionally controlled through synthetic insecticides. However, the factor of environmental issues and rising insecticide resistant breeds have prompted the exploration of eco-friendly and sustainable alternatives. Here, we attempt to use the leaf extract of Flemingia wightiana to produce silver nanoparticles (FWAgNP). The construct of AgNPs was first indicated by UV-Vis spectroscopy, with a peak at 461 nm. NP was then characterized by SEM, EDX and functional groups were analyzed using FTIR spectroscopy. Safety assessments of synthesized NP were carried out on Oreochromis niloticus. Percentage mortality was studied on A. Aegypti with both test samples, FWAgNP and FWME. FWAgNP were found to be effective; the lowest percentage mortality of 70 % was recorded for forth instar larvae and 100 % mortality was observed in the first and second instar larvae. Oxidative stress assays such as AChe, SOD, CAT, GSH and GST were carried out. SOD, CAT and GSH showed significant elevated levels. GST and AChe levels reduced as the concentration increased, indicating the role of test samples in oxidative stress. Antiviral assay was conducted to check the effect of AgNPs in inhibiting the growth and infection of Zika virus (ZIKV) on Vero cells. The percentage inhibition property of AgNP was found to be 25 %. In conclusion, the developed FWAgNPs have significant potential in the control of vectors and a limited inhibitory activity on Zika virus. The Author(s). -
Antibiofilm and anti-quorum properties of ethanolic leaf extracts of Syzygium jambos and Psidium guajava and their gel formulation for wound healing applications
Most bacterial species today have evolved with time and gained resistance to a wide range of antibiotics, primarily due to formation of biofilms and ?-lactamases. Many phytochemicals have been explored for their ability to inhibit bacterial biofilms. The present study sheds light on antibiofilm properties of two such plants viz. Psidium guajava and Syzygium jambos, of the Myrtaceae family. They were found to be effective against four different biofilm forming pathogens - Chromobacterium violaceum, Klebsiella pneumoniae, Pseudomonas aeruginosa and Staphylococcus aureus. Synergistic use of the plant extracts showed slightly better antibacterial activity than a single extract. Quorum sensing being one of the key factors required for biofilm formation, the isolate Chromobacterium violaceum was used as the indicator organism to study the anti-quorum properties of the plant extracts. At 10 mg/mL, ethanolic extract of S. jambos inhibited violacein pigment the most (78.84%) and therefore can be considered as a quorum sensing inhibitor (QSI). Since silver nanoparticles (AgNPs) have become increasingly significant in the field of drug delivery, they may be utilized to coat implants to avoid subsequent infections in patients who have had implant surgery and to reduce biofilm development in pathogens. In the present study, five gels were formulated using plant extracts and AgNPs, of which two showed promising results in wound healing assay. The non-toxic nature of the synthesized gels has been verified by studies on L-929 mouse fibroblast cell lines, which opens the door for their prospective application as topical treatments to accelerate the healing process in both acute and chronic wounds. Given that S. aureus and P. aeruginosa are the most commonly isolated bacteria from diabetic foot ulcers, the resulting gels can considerably curb the spread of infection and gangrene and thus prevent amputation. Copyright: The Author(s) -
Phytochemical profiling and evaluation of antioxidant and anti-inflammatory activities of Ipomoea alba L.
Plant-based medicine has been one of the oldest therapeutic practices in India and continues to offer valuable treatments for various ailments. Ipomoea alba, commonly known as morning glory, belongs to the family Convolvulaceae and is native to the tropical and subtropical regions of North and South America. It is renowned for its large, fragrant, nocturnal blooms, this plant holds significant potential in traditional medicine, particularly for managing gastrointestinal disorders, inflammation, and skin conditions. The nutrient content of Ipomoea alba leaves and seeds has demonstrated promising health benefits. This study investigated the phytochemical profile of Ipomoea alba leaves using three solvents: water, methanol, and chloroform. Phytochemical analysis confirmed the presence of carbohydrates, proteins, alkaloids, flavonoids, saponins, and tannins. HPLC analysis identified the presence of phenols in the aqueous extract, albeit in small quantities. Among the three extracts,the methanolic extract exhibited the highest antioxidant activity, as determined by DPPH, ABTS, and FRAP assays. Anti-inflammatory activity, assessed using a proteinase inhibitory assay, demonstrated that the methanolic extract showed the greatest inhibition at lower concentrations compared to the aqueous and chloroform extracts. The results suggest that the antioxidant and anti-inflammatory properties of Ipomoea alba may hold potential applications in cancer prevention and treatment. Future studies will aim to evaluate its cytotoxic effects, thereby exploring its potential role in cancer therapy. The Author(s). -
Metabolite profiling and bioactivity assessment of diverse endophytic fungi from the endangered plant, Nilgirianthus ciliatus
Endophytic fungi are potential sources of bioactive compounds with therapeutic properties. This study investigated the fungal endophytes associated with Nilgirianthus ciliatus, an endangered medicinal plant, to discover its secondary metabolites and bioactivities. Molecular analysis revealed the prominent species to be Aspergillus niger, Didymella sp., Trichoderma viride, Bipolaris zeicola and Nigrospora sphaerica. Alkaloids, flavonoids, phenolics, terpenes and saponins were detected in ethyl acetate extracts employing phytochemical screening. Didymella sp. has showed the highest level of antioxidant activity, demonstrating strong DPPH radical scavenging and reduction capability. T. viride had strong antibacterial action against Klebsiella pneumoniae and Escherichia coli, meanwhile Didymella sp. and N. sphaerica were most effective against E. coli. GC-MS analysis uncovered many bioactive chemicals, including trans-farnesol and pentadecanoic acid, which are renowned for their antibacterial and antioxidant properties. These findings highlight the presence of the rich variety of diverse endophytic fungi harboring such medicinal plants, which offer promising applications in medicine, biotechnology and agriculture as sources of novel bioactive compounds. Further exploration and characterization of these strains could unlock valuable sustainable resources for various industries. The Author(s). -
Effect of heavy metal elicitation on antioxidants and andrographolide content in cell suspension cultures of Andrographis paniculata
Andrographolide, a bicyclic diterpene from Andrographis paniculata is of immense pharmaceutical importance. A. paniculata, an annual herb from Acanthaceae is widespread in the Indian subcontinent. Heavy metals act as abiotic elicitors. The study deals with the effect of mercury (Hg), cadmium (Cd) and arsenic (As) on andrographolide content, phenols and flavonoids and each of their correlation with the metal chelating and radical scavenging activity, in cell suspension cultures of A. paniculata. Andrographolide was estimated using HPLC, while other estimation methods were used for other metabolites. Four different concentrations of each of the heavy metal salts CdCl2, As2O3and HgCl2, were administered in liquid MS media containing 1 g of cells. Media without any metal served as control. Higher concentrations of Cd and As imparted a positive effect on andrographolide content, Hg imparted a negative effect. The cells were most sensitive to Hg and most tolerant to Cd. Cd could be the best choice as an elicitor for increased production of andrographolide. While phenols show a positive correlation with antioxidants, flavonoids and andrographolides do not show a positive correlation with antioxidants. The Author(s). -
Mosquito larvicidal property of Citrus species
Mosquitoes and their larvae have several detrimental effects on humans, animals and the environment.. Their bites cause itching, allergic reactions and skin irritation. Mosquito larvae thrive in stagnant water, polluting water sources and creating breeding grounds for further infestations. Large mosquito populations negatively impact agriculture and livestock by transmitting diseases to animals. Additionally, their presence reduces outdoor activities, affecting tourism and economic productivity in affected regions. The review focuses on the Culicidae mosquito genera Anopheles, Aedes and Culex, including many species in each. The papers show that Clevenger and Soxhlet apparatus methods maintain high-quality and quantity oils because of their unique properties. These methods are cost-effective and environmentally friendly since chloroform, carbon tetrafluoride and other similar pollutants are not used, which causes severe health issues.Future research will examine how oil release from plant parts varies with age and how this relates to mosquito mortality. Different plant parts may yield varying quantities of oil at different stages, which can be considered as a point of discussion. The present findings supportthe efficiency of certain Citrus species in the Rutaceae family to eradicate mosquitoes and its larvae. The Author(s). -
Effect of biofertilizers on the survival and growth of air-layered saplings of West Indian cherry (Malpighia glabra L.)
This study evaluated the efficacy of air layering in combination with biofertilizer treatments for the successful propagation of West Indian cherry (Malpighia glabra L.) during 20222023 at the North Farm, School of Agricultural Sciences, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India. Air layering was performed on a 9-year-old tree using 1-year-old, pencil-thick shoots of 60 cm length. Once rooted, the air layers were gradually detached from the mother plant and transplanted into polybags. Biofertilizer treatments were applied to the potting medium and as soil drenches, including Trichoderma viride (3 g L-1), vermiwash (1 %), humic acid (20 g L-1), Azospirillum brasilense (2 g kg-1 planting medium), plant growth-promoting rhizobacteria (PGPR; 10 g plant-1) and vesicular-arbuscular mycorrhiza (VAM; 100 g plant-1), along with an untreated control. Data recorded at 60 days after detachment (DAD) showed that vermiwash @ 1 % (T3) significantly enhanced shoot and root growth parameters. It resulted in the highest plant height (45.23 cm), number of leaves (38.22), number of shoots (6.45), survival percentage (81.04 %), root length (13.80 cm), primary roots (26.31), secondary roots (78.23) and root diameter (4.88 mm). The improved performance under vermiwash treatment is attributed to its rich content of plant growth regulators, enzymes, micronutrients and beneficial microbes, which positively influenced both vegetative and root development. The results underscore the potential of integrating air layering with nutrient-enriched organic treatments like vermiwash to enhance the propagation efficiency and field establishment of West Indian cherry. The objective of this study was to identify an effective biofertilizer-based strategy to enhance survival, growth and nursery establishment of air-layered West Indian cherry saplings. Copyright: The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/) -
Attention and Representation Learning in Byte-Level Digital Forensics: A Survey of Methods, Challenges, and Applications
Byte-level analysis has become an essential capability in digital forensics, enabling content-based investigation when file system metadata, headers, or structural information are unavailable or unreliable. Recent advances in deep learning allow forensic systems to learn discriminative features directly from raw byte streams; however, the growing diversity of representation strategies, architectural designs, and attention mechanisms makes it difficult to assess their relative effectiveness and practical suitability. This study presents a structured survey of representation learning and attention-based approaches for byte-level digital forensic analysis. We examine statistical, embedding-based, image-based, sequential, and hybrid representations, and analyze how architectural choices and attention mechanisms influence performance, robustness, and scalability. Across the literature, hybrid representations combined with lightweight convolutional backbones and selective attention mechanisms consistently provide a favorable balance between accuracy and computational efficiency. The survey also reviews key forensic applications, including file fragment classification, malware and binary analysis, network payload forensics, and encrypted or compressed data triage. In addition, we critically discuss challenges related to distribution shift, dataset bias, adversarial vulnerability, interpretability, and reproducibility, along with practical considerations for deployment in large-scale forensic pipelines. By synthesizing architectural trends, operational constraints, and reliability concerns, this work identifies critical research gaps and provides a structured foundation for the development of robust and trustworthy byte-level forensic learning systems. (2026), (Science and Information Organization). All rights reserved. -
Cognitive Load Optimization in Digital (ESL) Learning: A Hybrid BERT and FNN Approach for Adaptive Content Personalization
Traditional English as a Secondary Language (ESL) learning platform rely on static content delivery, often failing to adapt to individual learners cognitive capacities, leading to inefficient comprehension and increased cognitive load. A novel hybrid Feedforward Neural Network and Bidirectional Encoder Representation Transformer (FNN-BERT) framework stands as our solution because it performs dynamic content personalization through predictions of real-time cognitive load. The proposed approach incorporates Feedforward Neural Networks (FNN) alongside Bidirectional Encoder Representations from Transformers (BERT) to process behavioral analytics for optimized content complexity adjustment and adaptive and scalable learning delivery. Real-time adaptability, scalability and high computational needs of current models reduce their effectiveness in personalized learning environments. Through the application of Test of English for International Communication (TOEIC), International English Language Testing System (IELTS) and Test of English as a Foreign Language (TOEFL) datasets, our methodology uses Feedforward Neural Network (FNN) to forecast cognitive load based on student engagement behaviors and application errors then Bidirectional Encoders Representations from Transformer (BERT) processes content difficulty adjustments automatically. The proposed model delivers a 95.3% accuracy rate, 96.22% precision level, 96.1% recall capability and 97.2% F1-score which surpasses conventional Artificial Intelligence-based English as a Secondary Language (ESL) learning systems. The system makes use of Python for its implementation to improve understanding as well as student focus and mental processing speed. Personalized content presentation methods lead to lower cognitive strain which simultaneously advances student achievement numbers. The research adds value to smart educational frameworks through its introduction of a scalable framework that allows adaptable learning systems for English as a second language (ESL). The following research steps include simplifying system complexity while adding multimodal learning signals including eye monitoring and speech recognition and further developing the model across various educational subject areas. The research works as a promising foundation which propels AI real-time adaptive education systems for students from various backgrounds. (2025), (Science and Information Organization). All Rights Reserved. -
Digital Adoption and Price Discovery in Shadows: Evidence from Indian IPO Grey Markets between 2016-2025
This study examines the role of digital adoption and price discovery through informal IPO markets in India. Due to price anomalies, sentiments around the IPO listing day are channelised through Informal markets known as grey markets. This study verifies the determinants of grey market IPO prices and their linkages with the formal market IPO under-pricing. Apart from dominant market and firm-specific factors, it examines how digital adoption variables such as digital payment usage and new demat accounts affect the IPO prices in both channels. In the post-pandemic period, there is a surge in the number of IPOs offered and participation of institutions and individuals in IPOs. Grey market allows traders to bid on IPO applications before they are officially listed, helping to assess under-pricing in issue prices, if any. This study uses data of 1,155 IPOs that went public in India between 2016 and 2025. Using OLS models, the study examines the relationships among variables, and findings indicate that both grey market activity and digital adoption directly influence listing day pri ces, confirming that under-pricing is predictable through informal channels. Investors should consider these factors, in addition to fundamental aspects, when making IPO investment decisions. 2025, ASERS Publishing House. All rights reserved. -
Non-Accounting Drivers of Forensic Accounting Techniques: Insights from PLS-SEM Analysis
Forensic accounting techniques are pivotal in combating financial fraud and enhancing corporate governance. According to Forensic Accounting Theory, both accounting and non-accounting factors influence the intention to adopt these techniques. This study explores the impact of key non-accounting factors i.e. Bonus Contract, Anonymity, and Collapse Avoidance on adoption of forensic accounting techniques by the practitioners, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) and SmartPLS software. Data was collected from professionals across diverse industries utilising forensic accounting services. The results reveal that these non-accounting factors exert varying levels of influence on adoption intentions. This research enriches the existing body of knowledge by offering new perspectives on the role of non-accounting drivers in forensic accounting adoption, providing actionable insights for policy-makers, regulators, and corporate leaders. 2025 The Author(s). -
From Fabric to Function: Decoding Athleisure Wear Adoption through Fit-inspired Lifestyle and Social Influence; [Od tkanine do funkcionalnosti: analiza sprejemanja portno-elegantnih obla?il skozi zdrav ivljenjski slog in drubeni vpliv]
Athleisure wear has emerged as a prominent trend in the fashion industry, particularly among educated youth in India, and is driven by a growing focus on fitness, health and comfort. This study, based on the stimulus-organism-response model, examines the factors influencing athleisure wear purchase intention, with a focus on fit-inspired lifestyle, perceived product quality, social influence and brand reputation. The study examines how these factors influence enclothed cognition, ultimately impacting purchase intention. Partial least squares-structural equation modelling, based on the two-step approach, was employed using SmartPLS 4.0 software for data analysis. Data collected from young consumers in Bangalore indicated that a fit-inspired lifestyle, perceived product attributes and social influence had a positive influence on enclothed cognition, while brand reputation did not show a significant relationship. Additionally, enclothed cognition was found to be a significant predictor of purchase intention. The results suggest that the alignment of athleisure wear with a fit-inspired lifestyle, functional attributes and social validation plays a crucial role in shaping purchase intention. The study offers practical insights for marketers to focus on lifestyle alignment, product functionality and social engagement in their marketing strategies. The findings also highlight a shift in consumer behaviour where experiential and value-driven factors, such as product benefits and lifestyle fit, outweigh traditional brand reputation. Future research should include exploring diverse demographic segments, longterm behavioural patterns and the impact of sustainability and cultural values on purchase behaviour in the area of athleisure wear. 2026, University of Ljubljana Press. All rights reserved. -
Enhanced Pneumonia Detection from Chest X-rays Using Machine Learning and Deep Neural Architectures
Pneumonia is a major worldwide health concern, particularly for vulnerable groups such as babies and the elderly. Despite advances in medical imaging, diagnosing pneumonia using a chest X-ray remains difficult, due to the subtle presentation of symptoms and the variety in picture interpretation. This study utilizes modern machine learning can improve the accuracy and speed of diagnosing pneumonia using chest X-ray images. Utilizing a comprehensive dataset from the Kaggle online repository, consisting of over 5,000 annotated images, we evaluate the efficacy of various machine learning models including deep convolutional neural networks (CNN) and ensemble learning techniques. Our findings indicate that models like the Fuzzy opponent histogram filter combined with Logistic model trees (LMT) achieved the highest accuracy at 96.97%, while the deep learning-based Lenet (CNN) with LMT closely followed at 95.85%. The study aims to improve diagnostic precision, reduce interpretation discrepancies, and facilitate faster clinical decision-making by identifying the most effective machine learning approaches for real-world applications in healthcare settings. 2025 Kamal Upreti, Anju Singh, Divakar Singh, Preety Shoran, Uma Shankar, Meenakshi Yadav and Rituraj Jain. -
Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies
Image classification is a cornerstone of computer vision, with the applications spanning healthcare, autonomous driving and security. The dependence on large labeled datasets for supervised learning poses significant challenges, particularly in specialized fields where the labeled data is scarce and expensive to obtain. Self-supervised learning (SSL) has emerged as a promising paradigm, enabling models to learn useful representations from unlabelled data by designing pretext tasks that generate pseudo-labels. SSL faces limitations in handling complex data distributions and achieving robust generalization. This paper explores hybrid self-supervised learning strategies that combine multiple SSL techniques, such as contrastive learning, masked image modeling, and clustering, to enhance image classification performance and reduce dependence on labeled data. This study proposes a comprehensive framework that integrates data augmentation, feature extraction, and hybrid learning mechanisms, evaluated on the CIFAR-100 dataset. The experimental results demonstrate that hybrid SSL approaches achieve significant improvements in performance. The combination of SimCLR and masked image modeling (MAE) achieves a Top-1 accuracy of 77.8% on the clean test set and 71.4% on the domain-shifted set, and self-distillation with contrastive learning (DINO) achieves the highest Top-1 accuracy of 78.4% on the clean test set and 72.1% on the domain-shifted set. Advanced data augmentation techniques, such as CutMix and RandAugment, additionally enhance model robustness, with SwAV (contrastive clustering) achieving 76.5% Top-1 accuracy on the clean test set and 70.1% on the domain-shifted set. The findings highlight the effectiveness of hybrid SSL methods in addressing the challenges of limited labelled data, offering valuable insights for future research and applications in image classification. 2025 Seventh Sense Research Group. -
Enhancing Music Emotion Recognition with LSTM: Evaluating Various Embedding Techniques
The study investigates the application of Long Short-Term Memory (LSTM) networks for emotion classification in music lyrics. It focuses on the comparative effectiveness of various word embedding techniques. It evaluates the performance of static embeddings (GloVe, Word2Vec, FastText) versus contextual embeddings (BERT, Distil BERT) across three datasets: MER Lyrics, Mood Lyrics, and Combined Lyrics. Additionally, the study examines the role of stylistic and content-based features in enhancing classification accuracy. The results demonstrate that contextual embeddings considerably outperform static embeddings, achieving accuracy rates of up to 98% compared to 60% for static approaches. Moreover, combining multiple lyric datasets leads to improved model generalization. The findings show the potential of transformer-based models for advancing music emotion recognition. Future research will focus on optimizing large embedding models using techniques such as pruning, quantization, and distillation to enhance computational efficiency. 2025 Seventh Sense Research Group. -
UWB Monostatic RADAR-Based Heartbeat Monitoring in an Autonomous Vehicle
Monitoring a driver's physiological state in real time is vital for enhancing road safety by detecting fatigue, medical emergencies, and enabling future health-intervention systems in autonomous vehicles. Ultra-Wideband (UWB) impulse radio monostatic Radar emerges as an attractive alternative due to its ability to perform non-invasive and highly sensitive detection of vital signs, including respiration and heart rate, through obstacles such as clothing or car seats. This paper presents a radar setup located in the seat, which propagates a UWB signal through human tissues from the back side of the driver up to the heart location. The transmitted and reflected UWB signal and antenna reflection coefficient S11 parameter are analysed to detect the heart rate for a heartbeat-induced heart model. Various UWB pulse types and their spectral characteristics are analysed to ensure efficient energy transmission within the FCC mask safety constraints. Time-domain analysis of the transmitted and received pulses reveals clear heartbeat analysis with minimal distortion, achieving accurate heart detection rates. Reflected-pulse analysis shows clear differences in amplitude between systole and diastole for normal and abnormal heart-radius conditions, allowing reliable detection of heart states. Time-of-flight and range estimation help in tracking the heart-wall movement accurately. FFT-based analysis of the time-varying S11 parameter estimates the heart rate, confirming precise non-invasive heartbeat detection through the thorax. . This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) -
Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques
In recent times, the use of Remote Sensing (RS) data obtained from Unmanned Aerial Vehicles (UAVs) has gained significant popularity in crop classification tasks, including crop mapping, yield prediction, and soil classification. The classification of food crops utilizing RS Imageries (RSI) is a major application of RS tools in crop growing. Meeting the conditions for investigating these data requires more difficult approaches, and Artificial Intelligence (AI) technologies offer the mandatory support. Because of the variation and division of crop planting, archetypal classification methods have fewer classification outcomes. This manuscript focuses on the design and execution of a Leveraging Enhanced Dipper Throat Optimization Algorithm with Dipper-Inspired Precision Classification for Remote-sensed Optimized (DIP-CROP) Processing methodology. The drive of the DIP-CROP algorithm is to classify distinct types of crops that exist in remote sensing. At first, the DIP-CROP model applies image processing using the Sobel Filter (SF) to eliminate the noise. Next, the presented DIP-CROP technique takes place SqueezeNet model is employed for the feature extractor. To classify the food crop types, the DIP-CROP approach utilizes a Multi-Head Attention-based Bi-directional Long Short Term Memory (MHA-BiLSTM) algorithm. For hyperparameter tuning of the MHA-BiLSTM classifier, the Enhanced Dipper Throat Optimization Algorithm (EDTOA) will be applied in this work. The optimization process utilizes Levy flight distribution, which is known for its faster convergence due to efficient exploration of the search space. Levy flights can be used to take larger steps in exploration, which prevents getting stuck in local minima and accelerates convergence. The performance of the DIP CROP method is examined experimentally using a benchmark database. Experimental results affirmed the superior solution of the DIP-CROP algorithm over existing methods. 2026 Seventh Sense Research Group. -
Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier For Hate Speech Detection in Online Social Network
Social networking uses internet-based platforms to facilitate users to make connections with others and share various forms of content, including text, images, videos, and links. Social networking services are mainly used for non-social interpersonal communication. Many approaches have been developed for hate speech detection, but they still face significant challenges, particularly in classifying text into multiple labels accurately and in a timely manner. For accurate hate speech detection in social networks, a Censored Regressive Canonical Optimized Convolutional Deep Belief Classifier (CRCOCDBC) model is developed. The objective of the developed CRCOCDBC is to detect multi-class hate speech with minimal time and error rate. Comparative analysis shows improved performance in terms of minimum error and higher authentication accuracy and precision than other well-known methods. 2026 Seventh Sense Research Group. -
A Deep Learning-Based BCI System for Emotion Classification Using EEG Signals
Electroencephalography-based Brain-Computer Interfacing (EEG-BCI) technologies allow for effortless interaction between external hardware and the human brain through monitoring its electric signals. These systems rely on EEG recordings, which provide non-invasive and real-time neural information through electrodes placed on the scalp. To advance emotion-recognizing efficiency and accuracy, this study proposes a deep learning-based method that can extract valuable temporal and spatial information from EEG signals. The proposed model includes the use of a Graph Convolution Network (GCN) for learning spatial relationships between different EEG channels to model the data in graph form and gain features through that modelling. A Convolutional Autoencoder (CAE) is then used to compress data to low dimensions and to reconstruct it so that major features are not ignored. Furthermore, the model uses an Attention-based Bidirectional Gated Recurrent Unit (ABiGRU) for temporal classification, which can emphasize the most important time steps in both backwards and forward directions. Two standard datasets are employed to test the developed approach. The DEAP dataset is used for emotion recognition with a binary response, and SEED is used with multi-class classification. The model attains great results of 98.12% accuracy on DEAP and 97.58% on SEED datasets. The very high performances show the efficacy of the model for decoding emotional states from EEG signals and very strong potential for real-time emotion recognition in affective computing and BCI. 2026 Seventh Sense Research Group.