Volume 13 | Issue 4

 Abstract

The Startup-Investor Connecting Platform is an innovative digital solution designed to bridge the gap between startups and investors, fostering a vibrant ecosystem of entrepreneurial growth and collaboration. Built using modern web technologies like Next.js (Canary version), TypeScript, Tailwind CSS, ShadCN, and powered by Sanity for content management, the platform offers a responsive, secure, and scalable user interface.Startups can create detailed profiles to showcase their ideas, while investors benefit from powerful search and filtering capabilities--enabling them to discover startups based on founders, categories, or specific keywords. A special "Related Startups" section helps users explore similar ventures, promoting wider discovery and increased engagement across the platform.To facilitate professional networking, the platform includes a "Connect Investors" feature, which links directly to investors' LinkedIn profiles, making it easier to initiate meaningful collaborations. With GitHub authentication integrated via NextAuth, the platform ensures strong data protection and smooth user access.By combining advanced technologies with a user-first design, this platform revolutionizes the way startups and investors connect--driving impactful interactions and supporting the growth of next-generation businesses. It stands as a powerful tool at the forefront of the startup funding landscape, accelerating innovation and transforming ideas into successful ventures.

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 Keywords

Startup-Investor,Next.js,Related Startup,Connect,Founders.

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With the explosive growth of Android applications, mobile malware poses an ever increasing threat to user privacy and device security. Traditional signature based detectors struggle against obfuscated or zero day malware, necessitating intelligent, data driven solutions. This paper presents a lightweight, hybrid Android malware detection framework that combines static feature extraction with an Equilibrium Optimizer (EO)based feature selection module to reduce dimensionality and highlight the most informative attributes. A hybrid ensemble of LightGBM, XGBoost, Random Forest, and a Bidirectional LSTM (Bi-LSTM) model is then employed to classify applications as benign or malicious. The entire pipeline is exposed via a Flask-based REST API, supporting real-time APK uploads, JSON outputs, and SQLite-backed logging. Experimental evaluation on a dataset of 12,000+ APKs achieves an overall accuracy of 95.2%, precision of 94.7%, recall of 95.8%, and F1 score of 95.2%, significantly outperforming baseline methods. The proposed system demonstrates robust detection capabilities, low computational overhead, and easy deploy ability for proactive Android security.

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 Keywords

Android malware detection; machine learning; deep learning; Equilibrium Optimizer; hybrid ensemble; real-time scanning.

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 Abstract

The Smart Voting System through Facial Recognition using OpenCV and Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) is an advanced, secure, and efficient voting mechanism designed to enhance the accuracy and reliability of electoral systems. Traditional voting systems are prone to issues such as voter impersonation, voter fraud, and inefficiencies in the verification process. This system utilizes facial recognition technology to address these challenges by ensuring that only eligible voters can cast their votes. The proposed system leverages OpenCV for real-time image processing and feature extraction, with HOG being used for identifying facial features. The facial features are then classified using a Support Vector Machine (SVM) model, trained to differentiate between authorized voters and unauthorized individuals. The SVM classifier is trained on facial data, enabling it to achieve high accuracy and robustness in diverse conditions, such as different lighting or angles of faces. The process begins by capturing the voter's face through a webcam or camera at the voting booth. The facial image is then pre-processed, and HOG descriptors are extracted to capture the shape and structure of the face. The descriptors are subsequently input to the SVM classifier, which compares the facial features with a pre-registered database of authorized voters. If the system matches the captured face with the database, the voter is granted permission to vote. This innovative approach improves the efficiency of the voting process, reduces human error, and significantly increases security by preventing fraud or impersonation. Additionally, the system is cost-effective and scalable, making it a viable solution for both small-scale and large-scale elections.

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 Keywords

Support Vector Machine (SVM), Histogram of Oriented Gradients (HOG), OpenCV, Voter Authentication

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 Abstract

Countries that are constantly fighting like India need a well-developed and efficient transport system. Street accidents continue to be one of the leading causes of deaths and injuries around the world. Rapid detection and timely alarm generation are important to reduce death and allow for faster emergency responses. This article presents a real-time accident detection and alarm system that uses image processing and machine learning techniques to automatically identify road accidents from live video feeds. This system is implemented with the Yolov8 algorithm for object recognition (once, version 8). It is trained on two custom datasets. One is for general accident detection (7,512 images), and the other is for fire detection (10,446 images). The proposed model classifies accidents into three categories: car-to-car collisions, single car accidents, and auto brandy. Once recognized, the system immediately belongs to the type of accident to police, hospital, or fire brigade via SMTP or email. Experimental results show that the system is run with high accuracy in real-world scenarios and provides reliable solutions for intelligent monitoring and intelligent transport systems.

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 Keywords

Accident detection, YOLOv8, SMTP, alert, Email.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Image caption generation, a primary application domain in computer vision and natural language processing, produces text captions of images from deep learning models. The current paper suggests a CNN-LSTM-based system for automatic captioning, where pre-trained convolutional neural networks (CNNs) are employed for image feature extraction and long short-term memory (LSTM) networks for sequential text generation. Inspired by the Flickr8k dataset, the paper emphasizes primary challenges such as vocabulary sparsity, overfitting, and computational complexity. Experimental results achieve BLEU scores of 0.66 or more, exhibiting coherent caption generation and qualitative analysis discloses captioning inefficiencies for complex scenes. The paper also discusses future enhancements such as transformer-based architectures and attention mechanisms to improve caption accuracy and accessibility. The work contributes to improving large-scale human-computer interaction through multimodal AI systems. Caption generation is an important area at the intersection of computer vision and natural language processing, including the generation of descriptive text captions describing images using advanced deep-learning methodologies. Current paper suggests a new approach through a hybrid CNN-LSTM-based system for automatic captioning. This state-of-the-art model employs pre-trained convolutional neural networks (CNNs) for robust image feature extraction to identify and interpret relevant features in an image. These identified features are then fed to long short-term memory (LSTM) networks adept at generating coherent and relevant sequential text based on the visual input.The experimental results revealed excellent BLEU scores of 0.66 or higher, which reflects the model's capacity to generate captions not only accurate but also linguistically sound. Qualitative analysis of the generated captions does call out inefficiencies in handling complicated scenes with more than one element or activity, and it suggests where there is potential for improvement in the future.In the future, the paper foresees potential enhancements, such as the application of transformer-based models and attention, which would significantly improve caption accuracy and user experience for accessibility. Overall, this work contributes to advancing the state of large-scale human-computer interaction by developing sophisticated multimodal AI systems for interpreting and generating human-like text from visual inputs.

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 Keywords

Image captioning, deep learning, CNN, LSTM, attention mechanisms, natural language generation.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In life, quitting is often misunderstood as an act of weakness or failure. However, when seen through a philosophical lens, quitting is sometimes not just necessary but essential for growth,liberation, and authentic living. Ancient Indian wisdom, especially the Upanishads,Bhagwat Gita, Yoga Sutra and Writings of various Scholars, teaches that renunciation, detachment, and purposeful quitting are critical to realizing the self and attaining higher states of conscious-ness. Likewise in a world obsessed with noise and motion, choosing silence is an act of courage, a quiet revolution. Within it, the mind breathes, the heart listens, and the soul speaks.This article explores the philosophical as well as Psychological necessity of Quit and Quiet , drawing insights from both existential thought and Philosophical teachings.

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Quit, Distress Management, Quite, Philosophy, Bhagwat Gita, Buddhism, Mindfulness,Jainism, Freedom, Mental Health

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Clinical trials are defined as a methodical investigation of a novel medication (therapy regimens, gadgets) in human subjects to provide data for identifying or validating clinical claims or pharmacological and side effects to ascertain the safety and effectiveness of the pharmaceuticals in question.[1]

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ADR MONITORING AND SAFETY REPORT: AMOXICILLIN

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Mobile phone overuse has emerged as a growing concern, particularly among young individuals, with potential negative effects on brain health and cognitive performance. Brain-Derived Neurotrophic Factor (BDNF), a key neurotrophin involved in synaptic plasticity, learning, and memory, may be affected by excessive mobile usage. This observational study aims to investigate the relationship between BDNF levels and cognitive performance in individuals identified as mobile addicts. The study will include participants aged 18-40 years who meet established criteria for mobile addiction, assessed using Short version of Smartphone Addiction scale. Baseline blood samples will be collected to measure serum BDNF levels using ELISA, and cognitive functions will be evaluated using CogniFit app. The findings may offer valuable insights into the neurobiological consequences of mobile addiction and underscore the need for early interventions.

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Mobile addiction, brain derived neurotrophic factor, cognition

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the effects of globally social media trends on regional environmental graphic design. The study highlights important worldwide trends such as minimalism, bold font, and brilliant colors through the analysis of designer interviews, and quantitative information, and investigates how these styles are integrated into regional design practices. The findings show that, while these global tendencies improve visual appeal and enable recognition globally, they also pose issues such as homogeneity and loss of regional uniqueness. Designers have to tackle these problems by combining global aesthetics with regional cultural settings, assuring both originality and relevance. The study emphasizes the need for additional study into the long-term implications of trend adoption, as well as the significance of new technology in defining future design practices.

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Graphic design, Social media trends, Globalization, Regional adaptation, Cultural identity

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It is often that a crisis situation exposes the hidden psyche of individuals. The paper is an attempt to uncover the complexities that surround the apparent liberal educated middle class in the backdrop of a communal riot situation. Mahesh Dattani weaves the narrative in such a way that the legacy of hatred on communal lines comes to the forefront in the socio-economic political context where the past entwines with the present to shapes an individual's formation of prejudices and false beliefs about a particular community. This is how the process of 'othering' begins at the rudimentary space of the family.

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 Keywords

communal, other, gender, dramaturgy, guilt

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As we say climate resilience within the purview of gender perspective, the two issues are to be linked with each other in context. The paper views through the United Nations Framework Convention on Climate Change (UNFCCC), Conference of Parties (COP), Kyoto Protocol, Green Climate Fund (GCF), Paris agreement, International Forum for Environment, Sustainability & Technology (iFOREST) & finally the efforts of Ministry of Environment, Forests & Climate Change (MOEFCC) of Government of India. On the other hand, regarding gender perspective, the paper focuses on the issue of preservation of indigenous seeds variety in the context of Genetically Modified Foods (GMF) that cover the preservation of indigenous variety of seeds as a challenge to the domain of GMF. Climate resilience and water stressed areas are interlinked. Here both the issues of GMF & water stress are interwoven with the lives of women. Climate resilience efforts and water stress has put pressure on agricultural lands of joint families & leading to land distribution in the families. As a sequel to the familial land distribution and high cost of agricultural inputs, the income stresses in the families have escalated. As a result of the escalation, women's participation in the labor force has increased. Earlier, it was a thought process that working women will eventually lead to women empowerment. Currently, empowered and working women are also under threat. Due to patriarchal mindset, insecurities among spouses have escalated if the women happen to earn more in cash or kind. This precarious situation has led to high prevalence of Intimate Partner Violence (IPV). The issue of IPV violates basic human rights, undermines economic potential, reduces productivity, negatively impacts future generations when children witness violence at home. Besides the concept of interlinking issues, the resurge of geriatric population in the near future will also impact health care at domiciliary levels. Resurge of climatic temperature will lead to extra effort in reducing internal temperature at home. With a burden on the household economy to keep the home cool coupled with rising food prices, the working dividend male population will be under tremendous pressure to feed both the ends of demography. The dependant population at the initial end of life through the U5 population & through the geriatrics at the distal end of life will become a burden at the national level that is culminated by burden at the family level. The geriatric population will need an extensive domiciliary care that is again going to put further burden on the women at the household level. As way outs, the paper sees the role of the male gender to diversify skill set, develop risk taking capacity so that they sustain the income of the households. For the females, multi tasking is the way out in nuclear families. While being tech centric, both the genders need to be socio centric as well. Social cohesiveness coupled with spousal mutual respect will help us to improve the performances in various gender related indexes. Healthy behavior regarding household waste management & adoption of renewable sources of energy, rain water harvesting will not only add to climate resilience efforts for the current generation but also the future generations. Similarly, efforts at mass level like focus on alternative energy sources, graduating from coal in a phased manner, reducing fossil fuels will bring the nation to better position in the progress of the Sustainable Development Goal number 13.12

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 Keywords

UNFCCC, SDG, COP, GCF, IPV, iFOREST

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Cyberspace has emerged as a transformative domain that transcends geographical boundaries and reshapes the global order by integrating communication, commerce, governance, and national security into a single digital ecosystem. It offers unprecedented opportunities for innovation, efficiency, and connectivity, enabling countries to leapfrog developmental challenges and modernize public services. However, these advantages are matched by equally significant threats, including cybercrime, cyberterrorism, data breaches, and the growing misuse of emerging technologies such as Artificial Intelligence (AI). As cyberspace becomes increasingly integrated with critical infrastructure, state operations, and private enterprises, its vulnerabilities pose severe risks to national security, economic stability, and societal trust. This paper examines the multifaceted nature of cyberspace by analyzing its structure, vulnerabilities, and the legal frameworks developed to address its risks. Particular emphasis is placed on the geopolitical implications of cyberspace in Asia, specifically comparing the strategies of India and China in managing cyber threats, shaping digital policy, and responding to the actions of non-state actors. Through case studies and policy analysis, this research evaluates the challenges these nations face in balancing digital growth with security imperatives. The study concludes by emphasizing the need for stronger international cooperation, robust cybersecurity infrastructure, and ethical frameworks to navigate the increasingly complex cyber landscape and ensure a safe, secure, and equitable digital future.

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 Keywords

Cyberspace, Legality, Framework, Cyberattack, Non-state actors, Domain

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid evolution of digital agriculture necessitates innovative solutions to enhance market accessibility and efficiency for farmers. This project presents "A Smart Agriculture Marketplace for Enhanced Farming and E-Commerce Connectivity," a web-based platform designed to streamline the buying and selling of agricultural products. The system incorporates a dual-login mechanism for administrators and farmers, ensuring secure transactions and role-specific functionalities. By leveraging modern web technologies, the platform enables farmers to list their products, access a broader customer base, and engage in seamless transactions. Additionally, the integration of smart agricultural insights facilitates data-driven decision-making, enhancing productivity and market competitiveness. This research highlights the system's architecture, development process, and potential impact on the agricultural sector, providing a scalable solution to modernize traditional farming commerce.

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 Keywords

Smart Agriculturalists in Farming, AI-driven Analytics, Blockchain in Agriculture-commerce Integration, Digital Farming, Precision Agriculture, Predictive Analytics in Agriculture, Smart Contracts, Real-time Monitoring

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: As the global population ages, there is an increasing demand for innovative healthcare solutions to support elderly individuals. Traditional healthcare systems often fail to provide continuous and personalized care. This research presents Senova, a Virtual Health Assistant that integrate Internet of Things (IoT), and mobile applications to assist elderly users in managing their health. The system includes real-time health monitoring, predictive analytics, and seamless doctor-patient communication. By offering personalized reminders and alerts, Senova enhances the independence and well-being of senior citizens while reducing caregiver burdens

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Virtual Health Assistant, Elderly Care, Fall Detection, Internet of Things (IoT), ESP32, MPU6050, Flutter, Firebase, Real-Time Monitoring, Remote Healthcare, Health Alerts, Mobile Health App, Wearable Devices, User-Centered Design, Emergency Response System

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 Abstract

A circularly polarized antenna intended for wireless communication has been analyzed using a machine learning model optimized through gradient descent to predict its S11 characteristics. The design achieves circular polarization between 4.0 GHz and 4.06 GHz, with an impedance bandwidth extending from 3.9 GHz to 4.1 GHz.

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Machine Learning, Axial Ratio , circularly polarized

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This comprehensive report presents an in-depth analysis of our innovative smart food distribution system designed to reduce food waste and address food insecurity through the application of modern web technologies and artificial intelligence. We meticulously developed and rigorously evaluated a MERN stack-based platform that efficiently connects food donors with non-governmental organizations (NGOs) and volunteers. Our extensive implementation demonstrates that this technology-driven approach substantially outperforms traditional food redistribution methods across all metrics, with particularly dramatic improvements observed for time-sensitive food items and large-scale donation events. The system successfully integrates AI for food expiry prediction, location-based NGO notifications within a 10km radius, volunteer management, and comprehensive administrative tracking tools. This report provides exhaustive performance metrics, multilayered comparative analyses, and detailed implementation considerations that thoroughly document how this sophisticated platform can be efficiently deployed even using standard hosting infrastructure, democratizing access to this powerful technology for addressing critical societal challenges. Keywords: Smart Food Distribution, Food Waste Reduction, MERN Stack, MongoDB, Express.js, React.js, Node.js, Artificial Intelligence, Food Expiry Prediction, NGO Collaboration, Volunteer Management, Location-based Services, Administrative Dashboard, Real-time Notifications, Food Security, Sustainable Development, Community Engagement, Social Impact Technology, Web Application Development, Geospatial Analysis, User Authentication, Mobile Responsiveness, Database Management, API Integration, Food Donation Platform.

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 Keywords

Smart Food Distribution, Food Waste Reduction, Food Security, Sustainable Development, Social Impact Technology, Food Expiry Prediction, Real-time Notifications, Location-based Services, Volunteer Management, NGO Collaboration, MERN Stack, MongoDB, Express.js, React.js, Node.js, Web Application Development, AI, API Integration

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Molecular docking is a key computational technique in drug discovery, used to predict the interaction between small molecules and target proteins. Over the past decade, significant progress has been made in improving docking algorithms, scoring functions, and integrating artificial intelligence (AI) to enhance predictive accuracy.

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molecular docking; numerical analysis; optimization; data mining

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Creative Commons Attribution 4.0 and The Open Definition

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In this digital world, there are numerous credit card fraud detection systems. This research aims to explore the various approaches applied in Credit Card Fraud Detection, along with the selection or pre-processing of datasets for the purpose of constructing Machine Learning, Deep Learning, and Neural Network models. Many models, including decision trees, logistic regression, neural networks, Gaussian kernels, neural networks based on mining systems, self-organizing maps, generative adversarial networks, ensemble learning, AdaBoost, majority voting, deep convolution neural network model, adversarial learning, fuzzy clustering, optimized light gradient boosting, anti-k nearest neighbor, calibrated probabilities, bidirectional Long short-term memory (BiLSTM), and bidirectional Gated recurrent, will be covered.

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Credit Card Fraud Detection, Credit Card, Frauds, Machine Learning, Deep Learning, Detection, Methods, Classifiers.

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Creative Commons Attribution 4.0 and The Open Definition

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In medical emergencies, receiving quick and accurate medicine recommendations is crucial, especially when healthcare professionals are unavailable. This paper presents a Drug Recommendation System that functions as a web application, offering users two treatment options: Allopathy and Ayurvedic remedies. Based on the user's selection and symptoms, the system suggests suitable medicines or herbal treatments. The recommendation process is guided by predefined symptom-drug mappings and expert medical insights to ensure accuracy and reliability. This study highlights how such a system can assist individuals when immediate medical attention is unavailable, when reaching a doctor is difficult, or when dealing with initial-level symptoms. Additionally, it compares our system with existing drug recommendation platforms, emphasizing its ease of use and practical implementation

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Recommend, Allopathy, Ayurvedic, Emergency conditions, TF-IDF, Cosine similarity, Symptoms-based, Drug, Remedies

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Abstract: Food safety is a critical public health concern in India, The need for achieving food security is felt significantly in the recent years.Standards Authority of India (FSSAI), challenges such as lack of enforcement, inadequate consumer awareness, and unethical practices persist. This paper explores the existing food safety issues various challenges to food security in india, identifies key challenges in their implementation, and highlights measures to enhance food quality. Additionally, strengthening regulatory mechanisms and promoting awareness among consumers are crucial for ensuring safe and healthy food consumption in India.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Keywords: Food safety, public health, food quality, India, FSSAI, Food Security, Challenges

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Creative Commons Attribution 4.0 and The Open Definition