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Enhancement of tensile strength and elastic modulus using bio-waste based carbon nanospheres doped polymer nanocomposites
The Carbon Nano Spheres (CNS) derived from areca nuts were synthesized from pyrolysis process and were used as fillers for fabrication of polymer nano composite materials. The filler materials are loaded in 0.05%, 0.1% and 0.5% loading percentages. The optimum sample was subjected to heat treatment. The tensile strength, elastic modulus and % of elongation were investigated for all samples. The Scanning Electron Microscope (SEM) images revealed the morphological features of optimum samples and hence the uniform dispersion of CNS in polymer matrix. The 0.1% samples showed 10% improvement in Ultimate Tensile Strength (UTS) and 24% improvement in Elastic modulus compared to bare epoxy material. When 0.1% samplewas subjected to heat treatment under 200C the UTS improved by 23%. Hence, CNS reinforced composite materials exhibited unique properties like high strength, less weight and low cost making them suitable for various structural applications such as aerospace, automotive, construction, and electronics industries. The Polymer Society, Taipei 2024. -
Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Two dimensional fuzzy context-free languages and tiling patterns
Fuzzy context-free languages are powerful compared to fuzzy regular languages as they are generated by fuzzy context-free grammars and fuzzy pushdown automata, which follow an enhanced computational mechanism. A two dimensional language (picture language) is a collection of two dimensional words, which are a rectangular array of symbols made up of finite alphabets. Two dimensional automata can recognize two dimensional languages that could not be recognized by one dimensional automata. In this paper, we introduce two dimensional fuzzy context-free languages generated by the two dimensional fuzzy context-free grammars and accepted by the two dimensional fuzzy pushdown automata in order to deal with the vagueness that arises in two dimensional context-free languages. We can construct a two dimensional fuzzy context free grammar from the given two dimensional fuzzy pushdown automata and vice versa. In addition, we prove that two dimensional fuzzy context-free languages are closed under union, column concatenation, column star, homomorphism, inverse homomorphism, reflection about right-most vertical, reflection about base, conjugation and half-turn and also show that two dimensional fuzzy context-free languages are not closed under matrix homomorphism, quarter-turn and transpose. Further, we have given the applications and the uses of closure properties in the formation of tiling patterns. 2024 Elsevier B.V. -
Secure approach to sharing digitized medical data in a cloud environment
Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records. 2023 Xi'an Jiaotong University -
Corporate Default Prediction Model: Evidence from the Indian Industrial Sector
The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions. 2021 MDI. -
Real-time human action prediction using pose estimation with attention-based LSTM network
Human action prediction in a live-streaming videos is a popular task in computer vision and pattern recognition. This attempts to identify activities in an image or video performed by a human. Artificial intelligence(AI)-based technologies are now required for the security and human behaviour analysis. Intricate motion patterns are involved in these actions. For the visual representation of video frames, conventional action identification approaches mostly rely on pre-trained weights of various AI architectures. This paper proposes a deep neural network called Attention-based long short-term memory (LSTM) network for skeletal based activity prediction from a video. The proposed model has been evaluated on the BerkeleyMHAD dataset having 11 action classes. Our experimental results are compared against the performance of the LSTM and Attention-based LSTM network for 6 action classes such as Jumping, Clapping, Stand-up, Sit-down, Waving one hand (Right) and Waving two hands. Also, the proposed method has been tested in a real-time environment unaffected by the pose, camera facing, and apparel. The proposed system has attained an accuracy of 95.94% on BerkeleyMHAD dataset. Hence, the proposed method is useful in an intelligent vision computing system for automatically identifying human activity in unpremeditated behaviour. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Strengthening of brick masonry using biaxial polypropylene geogrid as confinement reinforcement
Recent and past earthquakes have once again reiterated the requirement of strengthening the masonry structures to withstand both in-plane and out-of-plane loads. In this experimental investigation, biaxial polypropylene geogrid was used as a confinement reinforcement on the surfaces to strengthen masonry specimens. The masonry specimens without and with geogrid have been subjected to a compression test, flexural bond strength test and diagonal tension (shear) test as per IS 1905, ASTM E518 and ASTM E519, respectively. From the results, it has been found that biaxial polypropylene geogrid significantly enhances the strength in masonry specimens with geogrid and also reduces crack propagation in all three tests. The relationship between compressive strength and flexural bond strength, compressive strength and shear strength of masonry specimens with geogrid has been established. Furthermore, based on the cost analysis of various strengthening techniques, it was concluded that the use of biaxial polypropylene geogrid is an economically feasible alternative to other reinforcing materials, such as stainless-steel wire mesh and polyester geogrid. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Biosynthesis of CuFe2O4@Ag hybrid nanocomposite: Ultrasensitive detection and catalytic reduction of 4-nitrophenol
Due to the dearth of extremely capable, sensitive, and stable catalysts, the efficient detection and catalytic removal of 4-nitrophenol (4-NP) in industrial wastewater remains a serious challenge. The detection and determination of 4-nitrophenol (4-NP) presence in the environment is a matter of paramount importance because it is a high-priority hazardous pollutant that can affect people, animals, and plants. Here, we present a promising and economically viable green synthetic route for fabricating CuFe2O4 and CuFe2O4@Ag hybrid nanocomposites from the leaf extract of Senna didymobotrya. The UVVis, FTIR, XRD, FE-SEM, EDXA, BET and VSM analysis were performed to characterize the synthesis of CuFe2O4@Ag nanocomposite. To evaluate the electrocatalytic capacity of CuFe2O4@Ag, electrochemical sensing stratergy was performed with cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The modified CuFe2O4@Ag glassy carbon electrode (GCE) (CuFe2O4@Ag/GCE) demonstrated a linear response in the range of 0.01-15 ?g/ml (71 nm-107 ?M) and the ability to detect 4-NP at low concentration (0.006 ?g/ml (43 nM)). Due to the increased surface area of CuFe2O4@Ag/GCE by ? 1.5-fold, a greater cathodic current response (-16 ?A/cm2) at a low potential of -0.81 V was observed compared to CuFe2O4/GCE alone for the detection of 4-NP. Additonally, CuFe2O4@Ag showed excellent reduction ability towards 4-NP using NaBH4 with an efficiency of 96.4 % which was higher than the CuFe2O4 (only 87.3 %) in 12 min due to the synergistic relationship among Ag NPs and CuFe2O4 nanostructures. The outcomes from this study shows that the bi-functional electrocatalyst holds vast potential for environmental remediation. 2024 The Author(s) -
Understanding the social identity of adolescents in the Indigenous Kodava Community of India
The social identity development of adolescents in marginalized communities across the globe holds paramount significance in determining the overall well-being of its future population. Focusing on one such community, the Kodavas, an Indigenous community in South India, this study aims to understand the shifting configurations of social identity based on the changing sociocultural structure and its implications on identity perception among the adolescents belonging to the Kodava community in Kodagu district in Karnataka, India. This study used a qualitative research design to develop an analytical framework of social identity formation and its transitions in the context of the Kodavas. Data were collected from 188 adolescents (47% boys, 53% girls) between 13 and 17 years (M age = 15 years), in the form of essay writing. The findings based on thematic analysis highlight the core traditional elements of Kodava identity, factors influencing the transition in identity, and its reflection in the contemporary period. 2024 Society for Research on Adolescence. -
A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors -
Structural, optical and electrochromic properties of WAW films for profound electrochromic applications deposited by DC & RF magnetron sputtering
One of the most frequently used transition conducting oxides (TCO) is indium tin oxide. Indium is very expensive because of the lack of availability. So Most of the researchers focused on cost-effective materials and they have developed Dielectric/Metal/Dielectric (DMD) structures for ITO-free applications. Examples of dielectric materials are AZO, MoO3, TiO2, and WO3. The dielectric material is sandwiched between metals such as Au, Ag, Pt, Cu, and Al. The efficacy of these DMD structures is purely based on the thickness of the dielectric and metal layers. Once the metal layer thickness is more than 15 nm, the transmittance is much less due to the thickness of the material and it will work as a reflector. Moreover, as WO3 is the most widely and frequently used material we focus on the fabrication of WO3/Ag/WO3 (WAW) for replacing TCO in the electrochromic device and making it indium-free. WAW structures are widely used in smart windows, gas sensors, solar cells, photodetectors, etc. For electrochromic applications, these WAW structures showed good transmittance, fast switching speed, best coloration efficiency, and best optical modulation in comparison to WO3/ITO structure and are also cost-effective. 2024 The Author(s) -
Ultra-low loss compact active TM mode pass polarizer using phase change material in silicon waveguide
An active low-loss transverse magnetic (TM) pass polarizer, based on the phase change material (Ge2Sb2Te5), is proposed. The proposed polarizer is based on silicon-on-insulator technology that consists of a silicon waveguide that incorporates a thin layer of Si3N4 placed in-between GST. Enhancing the interaction between light and GST is achieved by strategically placing a double-layer GST adjacent to the slot waveguide. The polarizers tunability, on the other hand, depends on the shift in the refractive index (RI) of GST as it transitions between its crystalline and amorphous phases. By optimizing the structure, the polarizer exhibits negligible loss for both modes in the amorphous phase, and with the change of phase to crystalline, the loss of TE mode is more than 8 dB. In contrast, the loss of TM is less than 0.05 dB with a high ER of 21.82 dB, propagation length of 79.89 m and Figure of merit reaches up to 108 at 1550 nm. Due to the combination of these performance parameters, the suggested active TM pass polarizer is an appealing and effective device for various photonic applications. In addition, the fabrication technique of the proposed active TM pass polarizer is explained. 2024 IOP Publishing Ltd. -
Hydrogen Sulfide-Induced Activatable Photodynamic Therapy Adjunct to Disruption of Subcellular Glycolysis in Cancer Cells by a Fluorescence-SERS Bimodal Iridium Metal-Organic Hybrid
The practical application of photodynamic therapy (PDT) demands targeted and activatable photosensitizers to mitigate off-target phototoxicity common in always on photosensitizers during light exposure. Herein, a cyclometalated iridium complex-based activatable photodynamic molecular hybrid, Cy-Ir-7-nitrobenzofurazan (NBD), is demonstrated as a biomedicine for molecular precision. This design integrates a hydrogen sulfide (H2S)-responsive NBD unit with a hydroxy-appended iridium complex, Cy-Ir-OH. In normal physiological conditions, the electron-rich Ir metal center exerts electron transfer to the NBD unit, quenches the excited state dynamics, and establishes a PDT-off state. Upon exposure to H2S, Cy-Ir-NBD activates into the potent photosensitizer Cy-Ir-OH through nucleophilic substitution. This mechanism ensures exceptional specificity, enabling targeted phototherapy in H2S-rich cancer cells. Additionally, we observed that Cy-Ir-NBD-induced H2S depletion disrupts S-sulfhydration of the glyceraldehyde-3-phosphate dehydrogenase enzyme, impairing glycolysis and ATP production in the cellular milieu. This sequential therapeutic process of Cy-Ir-NBD is governed by the positively charged central iridium ion that ensures mitochondria-mediated apoptosis in cancer cells. Dual-modality SERS and fluorescence imaging validate apoptotic events, highlighting Cy-Ir-NBD as an advanced theranostic molecular entity for activatable PDT. Finally, as a proof of concept, clinical assessment is evaluated with the blood samples of breast cancer patients and healthy volunteers, based on their H2S overexpression capability through SERS and fluorescence, revealing Cy-Ir-NBD to be a promising predictor for PDT activation in advanced cancer phototherapy. 2024 American Chemical Society. -
Parental Attachment, Perceived Parental-Partner Similarity, and Relationship Satisfaction among Indian Emerging Adults
Theories of mate selection debate about whether people tend to choose partners based on similarities to their parents. The present study aimed to address whether a similarity in how people perceive their parents and their partners is associated with the relationship between parental attachment and relationship satisfaction by adopting a template-matching framework. Participants were urban, emerging adults in India (n = 263, 137 male and 126 female) who were measured for how they perceive the traits of a parental figure, traits of a partner, attachment to the parent, and relationship satisfaction with the partner. Data analysis was conducted using correlations, linear regressions, and moderation analyses. Findings show that perceived neuroticism of parents was associated with perceived neuroticism of the partner. Additionally, perceptions of neuroticism of parents predicted neuroticism in partners. Perceived agreeableness, neuroticism, and openness to experience moderated the relationship between parental attachment and relationship satisfaction. A gender difference with a small effect size in perceptions of similarity was observed for openness to experience and agreeableness. Finally, perceived agreeableness also moderated the relationship between parental attachment and relationship satisfaction for men and women separately. However, for men, perceived neuroticism also significantly moderated this relationship. The findings imply that, to an extent, the more emerging adults perceive similarities of certain traits in their parents and partners, the higher likelihood that their attachment to their parent predicts relationship satisfaction with their partner. Limitations and future directions have been discussed. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2024. -
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET
A kind of wireless network called a mobile ad hoc network (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFEs settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible. The Author(s), under exclusive licence to Shiraz University 2024. -
Nonlinear stability analysis of Rayleigh-Bard problem for a Navier-Stokes-Voigt fluid
The linear and nonlinear stability analyses of thermosolutal convection in a non-Newtonian Navier-Stokes-Voigt fluid, considering Soret and Ekman damping effects, are conducted analytically. Instability thresholds are determined for thermosolutal convection within a viscoelastic fluid of the Kelvin-Voigt type, wherein a dissolved salt field exists. Two scenarios are examined: one where the fluid layer is heated from the bottom and concurrently salted from the bottom, and the other where the fluid layer is heated from the bottom and concurrently salted from the top. The governing partial differential equations system includes conservation laws of mass, momentum, energy, and salt concentration. Using the energy method, the disturbances to the fluid system are shown to decay exponentially. Analytical expressions are developed for the eigenvalue as a function of Soret, Lewis, Prandtl, Kelvin-Voigt, and Rayleigh friction numbers. The study illustrates the shift from a stationary mode of convection to an oscillatory mode and provides thresholds that indicate these transitions. It is found that the viscoelastic property of the fluid acts as a stabilizing agent for oscillatory mode convection. Rayleigh friction substantially controls the convection threshold. Upon comparing threshold values between linear and nonlinear theories, a subcritical instability region is observed in the heating bottom-salting bottom case (case-1), whereas such a region is absent in the heating bottom-salting top case (case-2). 2024 Elsevier Ltd -
Nondestructive and cost-effective silkworm, Bombyx mori (Lepidoptera: Bombycidae) cocoon sex classification using machine learning
Sericulture is the process of cultivating silkworm cocoons for the production of silks. The quality silk production requires quality seed production which in turn requires accurate classification of male and female pupa in grainage centers. The challenges in the current methods of silkworm cocoon sex classification using manual observation lie in the time-consuming nature of the process, potential human error, and difficulties in accurately discerning subtle morphological differences between male and female cocoons. FC1 and FC2 single hybrid variety breed pupa are commonly used in south India for the production of high yielding double hybrid bivoltine silkworm seeds. In this study, 1579 FC1 and 1669 FC2 variety samples were used for the classification process. To overcome the challenges of present physical observation by expert employees, camera images of FC1 and FC2 cocoons were used in this study for sex classification. The proposed model used Histogram Oriented Gradient (HOG) feature descriptor of cocoon samples. Linear Discriminant Analysis (LDA) was applied on the feature vector to reduce the dimension and this feature matrix was given to the classical machine learning algorithms support vector machine (SVM), k-nearest neighbors (kNN), and gaussian nae bayes for classification with stratified 10-fold cross validation. The results showed that for FC1 data HOG + LDA + Nae Bayes performed better with a mean accuracy of 95.3% and for FC2 data HOG + LDA + KNN attained a mean accuracy of 96.2%. Our results suggest that this camera imaging method can be used efficiently in the classification based on the cocoon size and shape of different breeds. African Association of Insect Scientists 2024. -
The development and validation of digital amnesia scale
The usage of digital devices has increased rapidly in recent times due to the expansion of online learning platforms, leading to greater reliance on them. As a result, people forget simple information, dates, and appointments that might lead to digital amnesia. Hence, we aimed to develop and validate a digital amnesia scale (DAS). The study was carried out in two studies. In the first study, we collected data from 616 college students to examine the factor structure of the model and its underlying dimensions for a large pool of items. These analyses showed that the scale formed a three-dimensional structure: digital distraction, digital dependency, and digital detox. In the second study, we collected data from 383 college students to confirm the three-factor structure of the DAS. A satisfactory level of reliability was demonstrated by McDonalds ? value for the dimensions. The testretest reliability was found to be 0.76. The DAS had satisfactory convergent and discriminant validity. This scale could be useful for both researchers and educators to assess digital amnesia among college students. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Electrochemical performance of ZnxCo3-xO4/N-doped rGO nanocomposites for energy storage application
In this study, nanocomposites consisting of zinc-doped cobalt oxides with a spinel structure and nitrogen-doped reduced graphene oxide (ZnxCo3-xO4 (x = 0 and 1))/N-doped rGO) were synthesized using a solvothermal method. The synthesized materials were investigated using XRD, TEM, EDS, BET, Raman, and XPS for their phase formation, morphology, elemental composition, surface area, and chemical states. XRD analysis revealed that the metal oxides (Co3O4 and ZnCo2O4) present in the composites exhibited a single-phase cubic spinel structure, with a nanocrystalline nature and crystallite size ranging from 8 nm to 20 nm. Raman and TEM analyses revealed the co-existence of metal oxide nanoparticles and N-doped rGO phases in the composites. Electrodes were fabricated using the synthesized nanocomposite materials and subjected to electrochemical testing, including CV, GCD and EIS. The specific capacitiance (Cs) of samples determined to be 181 F/g and 234 F/g for CO/NrGO (Co3O4/N-doped rGO) and ZCO/NrGO (ZnCo2O4/N-doped rGO) nanocomposites, respectively, at lower current density (0.5 A/g). At all current densities, the CS of ZCO/NrGO nanocomposite electrode is observed to be higher than the CO/NrGO nanocomposite, probably due to structural defects and uniform anchoring of ZnCo2O4 particles over the layers of NrGO. The ZCO/NrGO composite electrode exhibits ?86 % capacitance retention after 3000 cycles. 2024 Elsevier B.V. -
Predicting Stock Market Movements Through Multisource Data Fusion Graphs: An Approach Employing Graph Convolutional Neural Network
The stock market plays an important role in the capital market, and investigating price fluctuations in the stock market has consistently been a prominent subject for researchers. The application of soft computing techniques to predict and categorize stock market movements is a significant research challenge that has gathered considerable attention from researchers. Although several studies highlight the significance of incorporating information from two sources in stock movement prediction, the potential of advanced graphical techniques for modeling and analyzing multi-source data remains an unattended research area. This study aims to address this gap by introducing a novel model that utilizes multi-source data fusion graphs to predict future market movements. The primary challenge involves establishing a model that can effectively gather the relationships among various data sources and employ this understanding to improve prediction performance. Compared to several existing methods relying only on historical data or sentiment data, which show limited predictive power and lack generality, the proposed approach seeks to overcome these limitations. The proposed model integrates various information sources, including historical prices, news data, Twitter data, and technical indicators for predicting future stock market trends. This presented method involves constructing a subgraph map for each data type to capture events from both rising and falling markets. Then, a Gated Recurrent Unit (GRU) is employed to aggregate the subgraph nodes. These aggregated nodes are then integrated with a Graph Convolutional Neural Network (GCNN) to classify the multi-source graph, therefore achieving stock market trend prediction effectively. To further validate its effectiveness, the presented model is applied to Indian stock market data, demonstrating its feasibility in fusing multi-source stock data and establishing its suitability for effectively predicting stock market movements. 2024 Seventh Sense Research Group