Volume 14 | Issue 5 |

 Abstract

The "Elevate Text into Video" system uses techniques like NLP and diffusion models to automatically convert text or speech into engaging videos with minimal user effort.It supports multiple visual styles and automatic voice narration, making video creation fast, efficient, and user-friendly.

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 Keywords

Text-to-Video, NLP, Diffusion Models, AI Video Generation, Deep Learning

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 Abstract

The primary aim of the topic is to formulate and evaluate a phytosome-based topical gel containing extract of Black Turmeric (Curcuma caesia) and Cassia fistula with objective of enhancing phytoconstituent bioavailability and assessing its anti-inflammatory. The present study focuses on the formulation and evaluation of a phytosome-based topical herbal gel containing extracts of Black Turmeric (Curcuma caesia) and Cassia fistula for enhanced anti-inflammatory activity. Herbal extracts often show limited therapeutic effectiveness due to poor solubility and low bioavailability; therefore, phytosome technology using phospholipids such as soya lecithin was employed to improve their absorption and stability. The extracts were prepared using suitable extraction methods and formulated into phytosomes, which were then incorporated into a topical gel base. The prepared formulation was evaluated for parameters such as particle size, entrapment efficiency, pH, viscosity, spreadability, stability, and in vitro drug release. Black Turmeric possesses anti-inflammatory, antioxidant, and analgesic properties, while Cassia fistula is known for its anti-inflammatory and wound-healing effects. The phytosomal gel formulation is expected to improve bioavailability, enhance skin permeation, and provide better therapeutic efficacy compared to conventional herbal formulations .

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 Keywords

Phytosomal gel, Cassia fistula , Curcuma caesia , Phytosome , Herbal drug delivery, Anti-inflammatory activity.

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 Abstract

Necrotizing fasciitis (NF) is a rare, rapidly progressive, and potentially life-threatening soft tissue infection characterized by extensive necrosis of the fascia and surrounding subcutaneous tissues. It is commonly caused by polymicrobial organisms or Group A Streptococcus and is associated with high morbidity and mortality if not diagnosed and treated promptly. Clinical manifestations typically include severe pain disproportionate to physical findings, erythema, edema, fever, and systemic toxicity. Early diagnosis remains challenging because initial symptoms may resemble less severe skin and soft tissue infections. Diagnostic evaluation is primarily based on clinical assessment and may be supported by laboratory investigations, imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI), and scoring systems including the Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC). Prompt surgical debridement, aggressive supportive care, and broad-spectrum intravenous antibiotic therapy constitute the mainstay of management. Delayed intervention significantly increases the risk of septic shock, multiorgan failure, limb loss, and mortality. This review summarizes the epidemiology, etiology, pathophysiology, clinical presentation, diagnostic approaches, and current management strategies of necrotizing fasciitis, with emphasis on the importance of early recognition and multidisciplinary treatment for improving patient outcomes.

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 Keywords

Necrotizing fasciitis; Soft tissue infection; Group A Streptococcus; Surgical debridement; LRINEC score.

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 Abstract

Abstract: Microbiomes : microbial communities exist consistently in or on living hosts, such as humans, animals, plants, in soil, water and air. The microbiome includes micoorganisms, like bacteria, fungi, viruses, and their genes. Microorganisms in microbiomes interact dynamically with their hosts and among themselves to form synergistic relationships. These interactions determine the composition of the microbiome and influence not only the features of the ecosystems where they reside, but can also have effects affecting the physiology of the hosts. The disease development is often correlated with microbial dysbiosis, or a shift in the microbiota. Gut dysbiosis may lead to Neurological, Gastrointestinal diseases, Celiac disease, Liver diseases, Allergies etc. Other reasons for dysbiosis and gut disorders are medications, unhealthy lifestyles, and genetic conditions. Current review focuses on studying effectiveness of Microbiome techniques in treating gut disorders as Microbiome engineering gaining more attention from few decades. Microbiome engineering probably applied most extensively on the human microbiome due to the potential of manipulating microbiota in humans for treatment of diseases. Allopathy often focuses on symptom suppression and acute intervention, while microbiome therapy aims to restore ecosystem (human) balance to address root causes. Microbiome techniques like Probiotoics, FMT, Phage therapy, omics studies are proving their effectiveness in treating the GI conditions. Engineering methods like FMT, Genetically engineered bacteria, prebiotic, probiotics are gaining success. Products like Rebyota, SER-109 / Vowst are successful for treatment of C. difficile infection. Although certain limitations of microbiome engineering are like poor Engraftment, Colonization Challenges & risks of altering the human microbiome detailed studies and research in combination with smart tools will lead to new trend in disease treatment. Microbiome techniques are thus effective, safe & have clinical applications in treating GI disorders.

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 Keywords

Key words: Microbiome, Microbiome techniques/ engineering, Microbes, Gut disorders.

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 Abstract

The rapid advancement of electric vehicles(EVs) has created a critical demand for intelligent and reliable battery management systems (BMS) to ensure safety, efficiency, and extended battery lifespan. Conventional BMS approaches are predominantly rule based and lack of predictive capabilities, resulting in suboptimal performance under dynamic operating conditions. This paper proposes an IoT driven predictive battery management framework that integrates real time sensing, cloud based communication, and artificial intelligence techniques for enhanced monitoring and control of EV batteries. The system continuously acquires key parameters including voltage (0 to 400 V), current (o to 200 A), temperature (-10 c to 60 C), and charge cycles using IoT enabled sensors and transmits the data to a cloud platform for analysis. Machine learning models, including support vector machine(SVM), Random Forest(RF) and Artificial Neural Network(ANN) are employed for accurate estimation of State of charge(SoC) and State of Health (SoH), as well as early fault detection. Experimental results demonstrate that the ANN model achieves a prediction accuracy of 96.2%. outperforming RF (94.8%) and SVM (91.3%. the proposed system reduces fault detection latency by approximately 28% and improves battery lifespan bt 18 to 22 % compared to conventional BMS techniques. Additionally, real time IoT monitoring enables continuous tracking with a data transmission interval of 1 to 2 seconds. The result indicate that the proposed framework significantly enhances battery reliability, operational efficiency, safety, making it a scalable solution for next generation EV applications.

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 Keywords

Keywords- Electric Vehicles, Battery Management System, Internet of Things, Artificial Intelligence, state of Charge, State of Health, Predictive maintenance, Machine learning

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Creative Commons Attribution 4.0 and The Open Definition

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The increasing number of fake, forged, and tampered academic and professional certificates has become a major concern for universities, employers and verification authorities. Traditional certificate verification methods rely on centralized databases, manual checking, or physical document validation, all of which are slow, prone to manipulation, and lack transparency. This paper proposes a Tamper Proof Certificate Verification System using Blockchain Technology, designed to provide a secure, decentralized, and immutable platform for issuing and verifying certificates. The system architecture incorporates a web-based portal for certificate generation by authorized institutions, a secure blockchain backend for data storage using SHA-256 cryptographic hashing and smart contracts, and a public verification module that allows users or employers to instantly validate certificates by scanning a OR code or entering a unique blockchain transaction ID. The findings indicate that blockchain-based systems can successfully prevent certificate fraud, streamline verification processes, and provide a scalable solution for academic and professional credential validation.

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 Keywords

Blockchain, Certificate Verification, SHA-256, Smart Contracts, OR Code Authentication, Decentralized System, Tamper Detection, Digital Credentials.

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Smart grids are modern electrical networks that integrate digital technologies, smart meters, and communication systems to efficiently monitor and manage electricity generation, transmission, and distribution. However, increasing reliance on digital infrastructure makes smart grids vulnerable to cyber threats such as False Data Injection (FDI) attacks and energy theft, which manipulate meter readings or sensor data leading to incorrect billing, operational instability, and potential power system failures. This paper proposes a Convolutional Long Short-Term Memory (ConvLSTM) based optimized model for detecting anomalies in smart grid systems. The model combines the strengths of Convolutional Neural Networks (CNN) for extracting spatial features and Long Short-Term Memory (LSTM) networks for learning temporal patterns from time-series smart meter data. The dataset employed consists of smart grid energy consumption data containing both normal and anomalous patterns. Data preprocessing techniques including handling missing values, outlier reduction, normalization using standard scaling, and class balancing via SMOTE are applied to improve model performance. The ConvLSTM model is trained and evaluated using standard performance metrics. Experimental results demonstrate that the proposed model achieves an accuracy of 78.92%, demonstrating reliable anomaly detection and improved identification of abnormal smart grid behaviors to help utility providers enhance grid security.

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 Keywords

Anomaly Detection, Smart Grid, ConvLSTM, Deep Learning, False Data Injection, Energy Theft, Time-Series Analysis, Cyber Security.

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 Abstract

Global supply chains are increasingly exposed to disruptions caused by geopolitical tensions, pandemics, cyber threats, climate events, and regulatory uncertainty. While traditional supply chain strategies have emphasized cost efficiency and lean operations, recent crises have highlighted the need for resilience--the capacity to anticipate, absorb, respond to, and recover from disruptions. At the same time, Artificial Intelligence (AI) has enhanced forecasting, visibility, and real-time decision support through machine learning, predictive analytics, and digital twins. However, the impact of AI depends not only on technical capability but also on how supply chain partners respond strategically to AI-generated information. This paper develops a conceptual framework that integrates AI, John Nash's game theory, and supply chain resilience. Suppliers and manufacturers are modeled as rational actors choosing between cooperation (information sharing and joint risk mitigation) and non-cooperation (independent decision-making). The framework proposes that AI reduces information asymmetry, improves transparency, and reshapes payoff structures, making cooperative equilibria more attractive and stable. These equilibria enhance preparedness, response speed, recovery effectiveness, and organizational learning. The study contributes by linking digital intelligence, strategic behavior, and resilience in a unified theoretical model. It also offers practical guidance for managers seeking to use AI as a strategic coordination mechanism rather than merely an operational tool. The framework provides a foundation for future empirical, simulation-based, and analytical research on resilient and intelligent supply networks.

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 Keywords

Artificial Intelligence; Game Theory; Supply Chain Resilience; Disruption Risk; Strategic Decision-Making; Information Asymmetry; Nash Equilibrium; Collaborative Supply Chains

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This research focuses on the development of an advanced system for accident detection and automatic ambulance alert to enhance emergency response efficiency. With the increasing frequency of road accidents, rapid detection and immediate response are critical for minimizing casualties. The proposed system utilizes state-of-the-art sensors and image processing techniques to accurately identify and analyze accident scenarios in real-time. Key components of the system include onboard sensors such as accelerometers and gyroscopes . These sensors continuously monitor vehicle dynamics and surroundings, enabling the system to detect sudden changes indicative of an accident. Once an accident is confirmed, the system automatically triggers an alert to emergency services, providing crucial information about the incident's location and severity. Furthermore, the system incorporates geolocation technology to precisely pinpoint the accident site, facilitating quicker ambulance dispatch. The automated ambulance alert aims to reduce response times, ensuring timely medical assistance for accident victims. This innovative approach not only streamlines emergency services but also contributes to the overall improvement of road safety. The study demonstrates the feasibility and effectiveness of the proposed system through simulations and real-world testing, showcasing its potential to significantly impact emergency response outcomes in accident scenarios.

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Accident Detection and Automatic Ambulance Alert in the project is described in the diagram below. Here is a brief description of each of them and how they all interact with one another

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This work presents a system for grouping and analysing music using machine learning and user-focused design. The system converts audio signals into numerical features such as MFCC and STFT, then groups similar tracks using clustering methods. It combines multiple clustering approaches to improve grouping stability and accuracy. The design also focuses on user needs through iterative development and feedback. Results show better grouping quality and improved usability. The system supports applications like recommendation systems and audio analysis.

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 Keywords

Keywords : Music Clustering, Feature Extraction (MFCC, STFT), Hierarchical Classification, Consensus Clustering, User-Centered Design (UCD), Multi-View Learning, Music Recommendation Systems.

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