Volume 13 | Issue 7 |

 Abstract

Cardiovascular diseases (CVDs), especially myocardial infarctions (heart attacks), represent a leading cause of death globally. Traditional diagnostic approaches such as ECGs, biomarkers, and clinical assessments often fall short due to delayed response and limited sensitivity. The integration of Artificial Intelligence (AI) and Machine Learning (ML) introduces novel possibilities for early prediction, personalized diagnostics, and continuous patient monitoring. This paper presents a comprehensive review of AI's role in cardiovascular risk assessment by highlighting the limitations of conventional techniques, the structure of AI architectures, their clinical advantages, key case studies, and future potential involving hybrid and federated learning systems. Furthermore, it emphasizes data privacy, ethical concerns, and regulatory preparedness to ensure real-world deployment and trust in AI-driven systems

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 Keywords

Artificial Intelligence (AI), Machine Learning (ML), Myocardial Infarction, Cardiovascular Disease (CVD), ECG, Risk Stratification, Predictive Modeling, Explainable AI (XAI)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Non playable character (NPC) is the core of an immersive and realistic game. As gaming turns into a huge industry, traditional script following NPCs need to be replaced with more dynamic characterization. This can be done through integrating an AI to the NPCs to make the game more immersive and enhance the realism of the game. Taking advantage of AI, an NPC can be given a framework which uses reinforcement learning and conversational AI within a simulation environment. This allows the NPC to engage in natural conversations with the player, learn from their past interactions and dynamically adapt their behavior with respect to the player. Using reinforcement learning the NPC are able to enhance their decision making based on their previous interactions with the player. Conversational AI makes the dialogue have more depth and context aware of the in-game environment. Testing the NPC in stimulated environment, the results demonstrate a more realistic and self-aware NPC.

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 Keywords

Reinforcement learning, Conversational AI, Simulation, Dynamic-Decision making, Animation, Open-world exploration, virtualization

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 Abstract

In this Generation of emails, messages spam continues to pose several challenges to email ecosystem. Spam detection in emails have been a concern because the user security depends on the classification of emails as spam or ham. The existing methods for spam detection lack in precision and is a time consuming process. This paper provides a spam detecting model that accounts for the dynamic nature of spam mails and learning based clustering techniques for classifying spam and ham messages. The model contains various Machine Learning (ML) algorithms used for detection and classification of spam emails. The model is integrated with Artificial Intelligence (AI) for automatic detection of spam or ham messages, which is most advanced form of detecting spam compared to other methods. The model present a novel approach to detect spam using Random forest (RM) classifier which is further enhanced by the designed methodology. The model claims the effective methodology with robust and interpretable features for detecting the spam messages.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning . Email Spam Detection . Machine Learning

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The growth of deepfake technology has raised great concerns about privacy, misinformation and cybersecurity. Advanced AI can make it difficult to say real and false media, as deeper and more visual content can change. In this paper we will explore current methods to recognize and prevent deepfakes and check how well they work and have limitations. It also explains how deeplearning to create faces changes, focusing on Stylegan and how it is used to edit, restore and change faces in different styles.We also look into the famous deepfake tool deepfacelab and sketch it to work with high resolution facial films. Apart from Visual Deepakes, we look at FluentLip, the latest audio conditioned LipenSthesis model that improves synthesis language synchronization and smoothness. Finally, let's look at recent advances in speech production. We present an approach to using emotions to create more natural and controllable facial expressions. Regarding existing procedures, limitations, and trends, this review suggests more efficient identification measures, the ethical design of AI, and better public education to combat the growing threat of deepfakes.

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 Keywords

Deep learning, deepfakes, face generation, deepfake detection, face-swapping, StyleGAN, AI ethics, audio-driven synthesis, talking face generation

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 Abstract

The Plastic Waste Management and Reward System is designed to promote responsible plastic disposal by leveraging technology to incentivise users for recycling efforts. The system integrates Flask for backend processing, AngularJS, HTML, CSS, and JavaScript for a dynamic frontend, and ImageDB for cloud-based image storage. MySQL is used for secure transaction and user data management, ensuring reliability and efficiency. The platform employs AI-powered image recognition models to classify plastic waste accu-rately, allowing users to earn rewards based on proper disposal. Rigorous testing methodologies ensure performance, security, and scalability. Future enhancements, including blockchain-based rewards, AI-driven classification improvements, and IoT-enabled smart bins, will further optimize waste tracking and management. This project presents an innovative approach to tackling plastic waste pollution by merging technology with sustainability.

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 Keywords

Plastic Waste Management, Reward System, Recycling Incentives, Flask Backend, AngularJS Frontend, ImageDB Cloud Storage, MySQL Database, AI-powered Image Recognition, Waste Classification, Secure Transactions, Performance Testing, Security Testing, Scalability, Blockchain Rewards, IoT Smart Bins, Sustainability, Waste Tracking, Environmental Technology

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Agriculture forms the core of the Indian economy, but farmers still grapple with inefficient crop planning, unpredictable weather, volatile market prices, and limited access to real-time farm insights. 'Raitha Bandhava - Farmer's Companion' is an end-to-end Farming Management System that leverages the latest technologies like artificial intelligence (AI), machine learning (ML), and application programming interfaces (APIs) to empower farmers with data-driven insights. The platform enhances productivity through AI-based crop guidance, weather forecasting, and smart market trends analysis. With API integration, it connects the farmer with the government and private farm database to give insights regarding policies, subsidies, and trends within the market. Further, the platform offers AI-based disease detection, virtual input/output market for produce, and supply chain optimization features. This article discusses the architecture, implementation, and impacts of the system and how it addresses existing technological gaps in agricultural solutions. Pilot implementations initially have reported 20% increased yield and 15% reduction in input costs and affirmed the efficiency of the system. It also discusses digital literacy and connectivity challenges and proposes remedies such as offline capabilities and language capability.

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 Keywords

Smart Farming, Crop Planning, Market Price Analysis, Weather Forecasting, Supply Chain Management, Digital Agriculture, Farmer Marketplace, AI-Based Disease Detection, Agricultural Technolo

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Lung cancer remains a major global health concern, with early diagnosis playing a crucial role in enhancing patient survival rates. According to the Global Cancer Observatory (GLOBOCAN 2024), lung cancer remains the most common cause of cancer-related deaths worldwide, accounting for over 2.4 million new cases and 1.8 million deaths annually. The application of Artificial Intelligence (AI) in medical imaging has opened new avenues for improving lung cancer detection. This review examines the role of AI, particularly deep learning algorithms, in analysing medical images such as CT scans, X-rays, and MRIs for lung cancer diagnosis and prognosis. Various AI-based techniques, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and meta-heuristic approaches like the Crow Search Algorithm (CSA), have shown substantial progress in identifying and categorizing lung nodules as benign or malignant. Pre-processing steps such as image segmentation, edge enhancement, and resampling contribute to improving image clarity, thereby enhancing the accuracy of AI-driven diagnostic models. Despite these advancements, challenges such as data imbalance, model interpretability, and generalization persist. This paper also explores the potential of Computer-Aided Diagnosis (CAD) systems in complementing AI methodologies for more precise and reliable clinical applications. Additionally, the study reviews the limitations of conventional histopathological diagnostic techniques and the potential of molecular biomarkers in refining lung cancer classification. The growing use of AI in healthcare is paving the way for personalized treatment strategies, yet the necessity for diverse and extensive datasets remains critical for improving model reliability. Through this review, we aim to provide a structured overview of AI-driven medical imaging advancements in lung cancer detection, offering insights to guide future research and development.

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 Keywords

Deep Learning, Convolutional Neural Networks (CNNs), Transfer Learning, Lung Cancer Detection, Lung Nodule Classification, Computer-Aided Diagnosis (CAD), Machine Learning (ML), Artificial Intelligence (AI), Feature Extraction, Deep Neural Networks (DNNs), ResNet, GoogleNet, MobileNetV2, VGG16, InceptionV3, Support Vector Machines (SVM), Random Forest (RF), Optimization Algorithms, Segmentation Techniques, Generative Adversarial Networks (GANs), Conditional Tabular Generative Adversarial Networ

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 Abstract

Artificial intelligence (AI) is playing a critical role in wildlife conservation by enabling species monitoring, poaching prevention, and habitat restoration efforts [1] . Due to habitat loss, poaching, and climate change, wildlife conservation is a major concern. Habitat assessment and resource conservation involve AI-powered image analysis, which aids in assessing forest health, detecting deforestation, and identifying areas in need of restoration [2] . The AI-Powered Wildlife Conservation System improves animal conservation and monitoring by utilizing artificial intelligence. With the help of sophisticated image recognition algorithms, users can submit photos or scan animals in real time. Additionally, it offers vital conservation status data, showing, based on international databases, if an animal is vulnerable or endangered. The technology also uses the Google Maps API to find local physicians and animal rescue facilities, guaranteeing prompt assistance for wildlife that is hurt or in danger. This project intends to assist wildlife researchers, conservationists, and the general public in preserving biodiversity by fusing AI with geolocation services.

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 Keywords

Artificial Intelligence (AI), Wildlife Conservation, Animal Identification, Endangered Species Protection, Rescue and Rehabilitation, Conservation Technology, Smart Conservation System, Google Map API, Image Recognition, Environmental Sustainability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Steganography based on web technologies has improved in recent years from simple HTML and CSS manipulation to advanced techniques using artificial intelligence and advanced web APIs with cross-platform implementations. This review article analyses the progress, trends now, issues, and ways forward in web-based steganography. Classical steganography conceals data in images or sound, while web-based steganography extends this to web technologies. We review recent papers on methods such as HTML, CSS, JavaScript, HTTP headers, and web storage. Our research shows an increasing trend in web-based steganography applications of deep learning, especially those that utilize browser-native functionalities. Important research shortcomings are cross-browser compatibility, the absence of standardized metrics for evaluation, and few studies on steganalysis specific to the web. This review will be an asset to information security, data hiding, and web technology researchers and practitioners.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web-Based Steganography, HTML Data Hiding, Web Page Steganography, Browser-Based Information Hiding, Network Security, Web Technology Encryption, Data Protection Strategies.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Research often lacks an incentive mechanism, and traditional Academic Publishing lacks transparent and equitable mechanism to reward the researchers and creates hindrances instead; this paper introduces the platform "Raktbeej", a blockchain-based platform that is inspired by the retroactive public goods funding which solves this problem by allowing authors to define royalty distribution percentages for cited works. Using smart contracts, Raktbeej automates the distribution of royalties to the cited authors whenever a donation is made to an author. We evaluate the potential of the platform to transform academic publishing for the better.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blockchain, Academic Publishing, Decentralised Science, Smart Contracts, Citations, Ethereum, Retroactive Public Goods Funding.

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Creative Commons Attribution 4.0 and The Open Definition