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Facebook as a Socialising Agent and its Impact on Academic Achievements on an Individual
Golden Research Thoughts Vol.2,Issue 9,pp.1-3 ISSN No. 2231-5063 -
Facebook post analysis for a celebrity page
Social Media is a subject that is large and extremely talked about in the present day. The expanding accessibility of the web and the development of social media brought in a necessity for the analysis of data based on a Facebook page. The number of information generated by social media platforms are expanding exponentially. The best approach to understand data of this magnitude lies in mining and analysis of such big data. The present focus is on the analysis of a Facebook Fan Page. This research work concentrates on the examination of the number of likes, post shares and correlation of posts and comments on various topics, fields or areas of a Facebook page. 'Like' is not the only way of rating a post nowadays. There are also recently added features like emoticons which display emotions such as 'love', 'wow', 'angry', 'sad', etc., to rate a post. The objective of this paper is to extract the data available and to perform analysis on a cricket fan page. Python is used to extract and analyse the information. IAEME Publication. -
Facial Emotion Detection Using Deep Learning: A Survey
The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE. -
Facial Emotion Recognition Augmented with CNNs and Face Detection: Toward Emotive Emoji Synthesis
Emotion recognition is a crucial component with broad applications in technology and healthcare industries specifically in humancomputer interaction. To improve emotion recognition accuracy, this research introduces an innovative technique that integrates face detection with Convolutional Neural Networks (CNNs). Using the Fer2013 dataset, the approach consists of carefully identifying faces in images as a preprocessing step, followed by training a CNN network to identify emotions and create corresponding emojis. After conducting extensive testing and assessment, it is determined that after employing a face detection algorithm the suggested framework is effective in both correctly identifying emotions and producing visually appealing emojis. This helps to create an interface for emotional communication that is more user-friendly and captivating. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Facial emotion recognition using convolutional neural networks
Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FERC). The FERC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FERC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FERC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). 2021 -
Facial Expression Recognition Using Pre-trained Architectures
In the area of computer vision, one of the most difficult and challenging tasks is facial emotion recognition. Facial expression recognition (FER) stands out as a pivotal focus within computer vision research, with applications in various domains such as emotion analysis, mental health assessment, and humancomputer interaction. In this study, we explore the effectiveness of ensemble methods that combine pre-trained deep learning architectures, specifically AlexNet, ResNet50, and Inception V3, to enhance FER performance on the FER2013 dataset. The results from this study offer insights into the potential advantages of ensemble-based approaches for FER, demonstrating that combining pre-trained architectures can yield superior recognition outcomes. 2024 by the authors. -
Facial Expression Recognition with Transfer Learning: A Deep Dive
In the realm of affective computing, where the nuanced interpretation of facial expressions plays a pivotal role, this research presents a comprehensive methodology aimed at refining the precision of facial expression recognition on the CK+ (Cohn-Kanade Extended) dataset. Our method uses the robust DenseNet121 architecture that has been pretrained on the 'imagenet' dataset, and it leverages transfer learning on the foundational CK+ dataset. The model deftly handles issues with overfitting, normalization, and feature extraction that are present in facial expression detection on CK+. Our approach not only achieves an overall accuracy of 98%, with a 5.86% accuracy enhancement over the base paper on the CK+ dataset, but also shows remarkable precision, recall, and F1-score values for individual emotion classes. It is noteworthy that emotions such as anger, contempt, and disgust have precision rates that reach 100%. The study highlights the model's noteworthy input to affective computing and presents its possible real-world uses in emotion analysis on CK+ and human-computer interaction. 2024 IEEE. -
Facial pain expression recognition in real-time videos
Recognition of pain in patients who are incapable of expressing themselves allows for several possibilities of improved diagnosis and treatment. Despite the advancements that have already been made in this field, research is still lacking with respect to the detection of pain in live videos, especially under unfavourable conditions. To address this gap in existing research, the current study proposed a hybrid model that allowed for efficient pain recognition. The hybrid, which consisted of a combination of the Constrained Local Model (CLM), Active Appearance Model (AAM), and Patch-Based Model, was applied in conjunction with image algebra. This contributed to a system that enabled the successful detection of pain from a live stream, even with poor lighting and a low-resolution recording device. The final process and output allowed for memory for storage that was reduced up to 40%-55% and an improved processing time of 20%-25%. The experimental system met with success and was able to detect pain for the 22 analysed videos with an accuracy of 55.75%-100.00%. To increase the fidelity of the proposed technique, the hybrid model was tested on UNBC-McMaster Shoulder Pain Database as well. 2018 Pranti Dutta and Nachamai M. -
Facial Recognition Model Using Custom Designed Deep Learning Architecture
Facial Recognition is widely used in some applications such as attendance tracking, phone unlocking, and security systems. An extensive study of methodologies and techniques used in face recognition systems has already been suggested, but it doesn't remain easy in the real-world domain. Preprocessing steps are mentioned in this, including data collection, normalization, and feature extraction. Different classification algorithms such as Support Vector Machines (SVM), Nae Bayes, and Convolutional Neural Networks (CNN) are examined deeply, along with their implementation in different research studies. Moreover, encryption schemes and custom-designed deep learning architecture, particularly designed for face recognition, are also covered. A methodology involving training data preprocessing, dimensionality reduction using Principal Component Analysis, and training multiple classifiers is further proposed in this paper. It has been analyzed that a recognition accuracy of 91% is achieved after thorough experimentation. The performance of the trained models on the test dataset is evaluated using metrics such as accuracy and confusion matrix. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Facile combustion synthesis of highly active Mo doped BiVO4 for photocatalytic dye degradation, photo-oxidation of alcohols, antifungal and antioxidant activities
This work represents the facile and green synthesis of Molybdenum (Mo)-doped bismuth vanadate (BiVO4). Green synthesis of Mo-doped BiVO4 was done using combustion technique using Mangifera indica (Mango) leaf extract as the fuel for combustion. The material synthesised was pure and characterised using X-ray diffraction, scanning electron microscopy, high resolution transmission electron microscopy, ultravioletvisible diffuse reflectance spectroscopy, Fourier transform infrared spectroscopy and photoluminescence (PL). It was found that Mo-doped BiVO4 had monoclinic scheelite phase, with a bandgap of 3.71eV. Various application was possible from the synthesised material like photodegradation of Malachite Green, a typical organic which showed excellent degradation efficiency of 99% under 120minutes. The catalyst also gave up to 95% yields in the light-assisted oxidation of aromatic alcohols to corresponding aldehydes. The material also showed excellent antioxidant properties showing 6.7g of ascorbic acid equivalence (AAE). It gave an excellent minimum lethal dosage (MLD) of 500g against Penicillium and Trichoderma fungal strains and showed maximum of 32 mm zone of inhibition. These applications show the versatility of the material to be used in various fields. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Facile construction of gefitinib-loaded zeolitic imidazolate framework nanocomposites for the treatment of different lung cancer cells
Gefitinib (GET) is a revolutionary targeted treatment inhibiting the epidermal growth factor receptor's tyrosine kinase action by competitively inhibiting the ATP binding site. In preclinical trials, several lung cancer cell lines and xenografts have demonstrated potential activity with GET. Response rates neared 25% in preclinical trials for non-small cell lung cancer. Here, we describe the one-pot synthesis of GET@ZIF-8 nanocomposites (NCs) in pure water, encapsulating zeolitic imidazolate framework 8 (ZIF-8). This method developed NCs with consistent morphology and a loading efficiency of 9%, resulting in a loading capacity of 20wt%. Cell proliferation assay assessed the anticancer effect of GET@ZIF-8 NCs on A549 and H1299 cells. The different biochemical staining (Calcein-AM and PI and 4?,6-Diamidino-2-phenylindole nuclear staining) assays assessed the cell death and morphological examination. Additionally, the mode of apoptosis was evaluated by mitochondrial membrane potential (??m) and reactive oxygen species. Therefore, the study concludes that GET@ZIF-8 NCs are pledged to treat lung cancer cells. 2024 International Union of Biochemistry and Molecular Biology, Inc. -
Facile engineering of aptamer-coupled silk fibroin encapsulated myogenic gold nanocomposites: investigation of antiproliferative activity and apoptosis induction
Nanocomposites selectively induce cancer cell death, holding potential for precise liver cancer treatment breakthroughs. This study assessed the cytotoxicity of gold nanocomposites (Au NCs) enclosed within silk fibroin (SF), aptamer (Ap), and the myogenic Talaromyces purpureogenus (TP) against a human liver cancer cell (HepG2). The ultimate product, Ap-SF-TP@Au NCs, results from a three-step process. This process involves the myogenic synthesis of TP@Au NCs derived from TP mycelial extract, encapsulation of SF on TP@Au NCs (SF-TP@Au NCs), and the conjugation of Ap within SF-TP@Au NCs. The synthesized NCs are analyzed by various characteristic techniques. Ap-SF-TP@Au NCs induced potential cell death in HepG2 cells but exhibited no cytotoxicity in non-cancerous cells (NIH3T3). The morphological changes in cells were examined through various biochemical staining methods. Thus, Ap-SF-TP@Au NCs emerge as a promising nanocomposite for treating diverse cancer cells. The Author(s), under exclusive licence to Springer Nature B.V. 2024. -
Facile fabrication of 3D-?-Fe2O3@2D-g-C3N4 heterojunction composite materials: Effect of iron oxide loading on the electrochemical performance
Designing heterojunction nanocomposites is crucial for optimizing supercapacitor electrodes. This study addresses the challenge by introducing a facile synthesis method for creating 3D-?-Fe2O3@2D-g-C3N4 heterojunctions through a bulk carbon nitride-assisted hydrothermal process. During this process, the growth of ferric oxide particles coincides with the exfoliation and deposition of carbon nitride, leading to simultaneous structural changes in both iron oxide and carbon nitride phases. The resulting composite's properties strongly correlate with the iron oxide loading. Comprehensive characterization using XRD, FTIR, SEM-EDAX, XPS and TEM identified three distinct structures for ?-Fe2O3/g-C3N4 composites based on iron oxide loading: low, medium, and high. The medium-loaded sample demonstrated superior electrochemical performance, attributed to better interfacial contact and the formation of 3D-Fe2O3@2D-g-C3N4 heterojunctions. This composite, with an optimized 22 wt% iron oxide loading, exhibited a maximum specific capacitance of 925.1 Fg?1 at 5 mVs?1 and 498.6 Fg?1 at 6 Ag?1 in charge-discharge analysis, with stable performance over 2000 cycles. Overall, this research presents an enhanced hydrothermal method for facile preparation of effective ?-Fe2O3/g-C3N4 heterojunction materials. 2024 Elsevier B.V. -
Facile fabrication of dasatinib laden multifunctional polymeric micelles: Evaluation of anti-proliferative and apoptotic activities in human cancer cells
Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol-polycaprolactone (PEG-PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS-loaded PEG-PCL nanoparticles (termed as DAS@PEG-PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug-loaded nanoparticles' apoptotic and anti-proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG-PCL NPs resulted in a dose-dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG-PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment. 2024 Wiley Periodicals LLC. -
Facile fabrication of dasatinib laden multifunctional polymeric micelles: Evaluation of anti-proliferative and apoptotic activities in human cancer cells
Dasatinib (DAS) has recently gained significant interest for its anticancer potential. Yet, the lipophilicity inherent in DAS limited its potential use as a chemotherapeutic drug. This study aimed to examine the effectiveness of polyethylene glycol-polycaprolactone (PEG-PCL) as a nanocarrier for DAS to increase its anticancer capabilities. The DAS-loaded PEG-PCL nanoparticles (termed as DAS@PEG-PCL NPs) were characterized using Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Morphological staining and MTT tests were employed to investigate drug-loaded nanoparticles' apoptotic and anti-proliferative effects. The MTT assay demonstrated that incorporating DAS onto PEG-PCL NPs resulted in a dose-dependent increase in cytotoxicity in A549 (lung cancer) and HeLa (cervical cancer) cells. The A549 cancer cells were analyzed for their morphology using the acridine orange/ethidium bromide (AO/EB) and DAPI staining techniques. Overall, these findings demonstrate that the polymeric PEG-PCL nanoparticle systems hold great potential as a novel therapeutic strategy for cancer treatment. 2024 Wiley Periodicals LLC. -
Facile fabrication of mesh-free, GO-reinforced ZrO2-based separators for advanced alkaline water electrolysis
Alkaline Water Electrolysis (AWE) is a promising method for sustainable hydrogen production due to its maturity and use of non-noble metal catalysts. A key challenge lies in developing cost-effective, durable, and scalable separators that ensure ionic conduction and separation between the electrodes. This study presents a mesh-free composite separator composed of zirconia nanoparticles (ZrO2 NPs), polysulfone (PSU), and graphene oxide (GO), eliminating the need for expensive polyphenylene sulphide (PPS) mesh and its hazardous hydrophilic surface treatments. GO was incorporated as a multifunctional additive to enhance mechanical strength, hydrophilicity, and dispersion of ZrO2 NPs. Separators were fabricated with varying compositions of ZrO2 NPs, PSU, and GO, and tested in a zero-gap titanium-based electrolyser using nickel foam electrodes and 30?wt% potassium hydroxide (KOH) electrolyte. Amongst them, the Sep72/25/3 separator (72?wt% ZrO2, 25?wt% PSU, 3?wt% GO) showed a low area-specific resistance (ASR) of 298?m? cm2 at room temperature (RT). It also exhibited excellent wettability with a reduced contact angle of 23 after 24?h conditioning in 30?wt% KOH, along with a notable improvement in tensile strength, from 1.75?MPa (without GO) to 3.26?MPa, validating the reinforcing role of GO. The results demonstrate a simple and scalable route for fabricating mesh-free separators that strike an optimal balance between ionic resistance, mechanical strength, and wettability, thereby offering a cost-effective alternative for next-generation advanced alkaline water electrolysis (AAWE) systems. The Korean Ceramic Society 2025. -
Facile Fabrication of Nano carbon Derivatives for Optical and Electro chemical Applications
From synthesis of novel materials to their end-use applications, the prime objective of the material science community is to address the burgeoning social issues across the world. Noxious emissions from fossil fuel combustion, increased incidence of skin cancer, drug misuse, and ever-increasing demand for energy are some of the global concerns that require urgent consideration. This drives a relentless quest for a multifunctional material with broad applicability that can directly and constructively impact the quality of life, environment, and economic progress. However, materials of this kind should embrace versatile characteristics, improved competency, plausibility, and lower cost. In light of this, the current doctoral research emphasizes the development of trailblazing graphene-based materials with manifold usages derived from a naturally abundant carbonaceous fossil fuel coke to discover scientific solutions to the aforesaid trials and tribulations. Fossil fuel coal, mainly used for energy purposes, is often discouraged from industrial and domestic consumption due to its contribution to global warming. Despite the fact that coal is a non-renewable resource and a source of greenhouse gas emissions, it is one of the world's bountiful carbon resources. Therefore, it can be exploited as a potent substitute for conventional graphite, enabling the extraction of value-added graphene derivatives along with the sustainable utilization of coal. However, the purity of the precursor is a vital criterion to guarantee the quality and supply of graphene materials. In this doctoral work, coal-coke with 99% carbon content was used for the production of high-quality oxidized multilayer graphene derivatives by employing an environmentally-benign synthesis technique. The obtained graphene structure exhibited a multi-emissive fluorescence property having emissions ranging from blue to green-yellow. In addition, it also possessed remarkable electrochemical performance, good rate capability, and durability, signifying its expediency in energy storage devices. In an attempt to further enhance the scope of as-synthesized coke-based graphene derivatives, heteroatoms such as nitrogen and phosphorus were introduced into the graphene lattice via substitutional doping. It was perceived that nitrogen doping impressively amended the photophysical properties, especially in terms of quantum yield and fluorescence lifetime. Therefore, the as-synthesized nitrogen-doped multilayer graphene derivative was used as a fluorescent biomarker for imaging melanoma skin cancer cells with the purpose of early detection. Wherein co-doping of nitrogen and phosphorus endorsed excellent electrochemical characteristics and sensing performance, owing to the synergistic effect from heteroatoms and the imparted structural corrugations. Thus, by utilizing the as-synthesized nitrogen, phosphorus co- doped heteroatom derivative, oxytocin, a high-risk abused drug, was electrochemically detected in an nM range and validated the possibility of real-time surveillance over its mishandling in edibles and biological models. The coke-based graphene derivatives were further refashioned to obtain optimum textural and surface chemistry characteristics beneficial for energy storage characteristics. Accordingly, simultaneous heteroatom-doping and activation of graphene derivative were achieved. The obtained sample had a high surface area, hierarchical porous structure, increased defect densities, and co-active heteroatom enriched graphene network, suggesting its potential as an electrode material for supercapacitor applications. It was observed that the as-synthesized simultaneously heteroatom-doped and activated samples demonstrated high capacitance value, appreciable cyclic stability, and lower charge-transfer resistance. Henceforth, such enhanced supercapacitive performance points toward the cradle-to-gate transformation of fossil fuel, i.e., the conversion of sluggish black coal to green energy. -
Facile fabrication of stable wettability gradients on elastomeric surfaces for applications in water collection and controlled cell adhesion
We have developed a simple and effective method to prepare stable wettability gradients on an elastomeric soft substrate, polydimethylsiloxane (PDMS). In our method, a partially cured PDMS film composed of a definite ratio of elastomer and crosslinking agent was heated over a hot surface with a temperature gradient. This causes differential thermal curing of the PDMS film and the water contact angle (wettability) of the resultant surface showed gradual variation across the length. This method allows us to design and fabricate wettability gradients with rationally controlled directionality and shapes (e.g., linear and radial gradients). The stability of the wettability gradients was studied and a chemical treatment method was developed to enhance the stability at room temperature. Stable wettability gradients prepared through this method can find applications as reliable platforms and scaffolds offering controlled or directional wetting and adhesion. We have demonstrated the practical applications of the wettability gradients in directional water collection, controlled crystallization of materials, and controlled cell adhesion of HeLa cells, osteoblasts and NIH/3T3 cells. The multi-functional characteristics of these wettable gradients are expected to be handy in other domains using soft materials and interfaces also. 2023 The Royal Society of Chemistry. -
Facile green synthesis of MnV2O6 nanoparticles: Photocatalytic studies and selective oxidation of aromatic alcohols
Advances in nanotechnology play a crucial role in developing reliable and environmentally friendly nanoparticles (NPs). The green synthesis method is one among them and aims to eliminate toxic by-products. Developing low-cost and highly efficient photocatalysts is essential to accelerate these reactions. To perform this, it is successfully synthesized manganese vanadate NPs using eco-friendly Butea monosperma leaves by the solution combustion method, and the synthesised NPs were characterized to examine their structural, optical, and morphological properties. The XRD pattern confirms that the synthesised MnV2O6 (MVO) NPs possess a monoclinic structure with an average crystallite size of about 67 nm. UVVis spectroscopy shows a band gap of 1.69 eV indicating the suitability of the materials in the Visible region. The photocatalytic activity of the resulting MVO NPs was evaluated and good photocatalytic activity for the degradation of methylene dye. Further, experiments were conducted at various parameters to optimize the catalyst and show the rate constant of 0.00467 min?1. catalytic activity of MVO NPs was also studied for the selective oxidation of aromatic alcohols. Among the various oxidizing agents and solvents used in optimization studies, tBuOOH (oxidizing agent) and CH3CN (solvent) showed the highest conversion (%) of benzyl alcohol, i.e., 98%. 2025 Elsevier B.V. -
Facile green synthesis of MoO2/BiOCl nanocomposite using Hibiscus rosa-sinensis leaf extract and its application in visible-light-driven oxidative transformations
This article describes a green approach for synthesizing MoO2/BiOCl nanocomposite using a combustion procedure with Hibiscus rosa-sinensis leaf extract as a renewable fuel source, which also acts as a reducing and stabilizing agent. The synthesized material is characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) and Fourier-transform infrared spectroscopy (FTIR), confirming the successful formation of the nanocomposite. The photocatalytic performance of MoO2/BiOCl nanocomposite was evaluated for visible-light-driven oxidative transformations of different aromatic amines to nitroarenes. The unique structure of MoO?/BiOCl provides better accessibility to the reactant molecules, facilitating faster and more efficient oxidation. The advantages of this oxidative process are high catalytic efficiency, mild reaction conditions, recyclability, environmental sustainability, and producing nitroarenes in good to exceptional yields (6795 %). The conversion of the compounds was validated using gas chromatography-mass spectrometry (GCMS), 1H NMR, and 13C NMR. The results demonstrated that the MoO2/BiOCl nanocomposite exhibited enhanced photocatalytic activity compared to its components, attributed to the synergistic effects between MoO2 and BiOCl. The use of Hibiscus rosa-sinensis leaf extract in the synthesis is not only environmentally friendly and cost-effective but also contributes to the stability and efficiency of the nanocomposite. 2025 Elsevier B.V.