Volume 13 | Issue 7 |

 Abstract

Fingerprints are essential for blood group identification. to diagnose a patient without waiting for traditional medical formalities and to anticipate the patient's blood type in emergency situations. concentrating on obtaining accurate and good results for everyday medical use in hospitals. It will be more beneficial to avoid time-consuming techniques throughout the critical time of medication. In this study, the only correlation between gender and finger print patterns was that females were more likely than males to have loops and arches, and males were more likely than females to have whorls. For a long time, fingerprint identification has been considered one of the most reliable ways to identify someone, especially in court. Fingerprints are believable because, with the exception of severe skin injuries, the patterns we create while still in the womb don't change throughout our lives.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Blood Group, Machine Learning, Finger print, Medical Field.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Due in large part to the difficulties of learning programming, engagement and retention are ongoing issues in computer science education. Adopting cutting-edge teaching techniques that improve learning results and maintain student interest is essential as the need for coding abilities expands across all industries. Gamification is one such strategy that introduces game-like components into educational environments, including badges, leaderboards, points, and accomplishment milestones. Programming-related gamification turns routine coding tasks into engaging and participatory experiences. Through increasingly difficult assignments, this approach fosters critical thinking, increases student engagement, and promotes problem-solving. Learning and skill improvement are reinforced by immediate rewards and real-time feedback. Additionally, gamification fosters a growth mentality by assisting kids in accepting difficulties, growing from mistakes, and persevering through hardship. Gamified learning environments provide a potent tool to boost motivation and academic achievement in computer science education by making coding more accessible and pleasurable.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Game-based learning, motivation, engagement, gamification, educational technology

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Bioactivity forecast may be a basic errand in sedate revelation and advancement, empowering the recognizable proof of potential medicate candidates with tall viability and negligible poisonous quality. By leveraging endless chemical datasets, ML models can learn complex structure-activity connections (SARs) and make exact expectations almost compound intelligent with natural targets Different ML strategies, counting profound learning, irregular woodlands, bolster vector machines, and gathering models, are utilized to improve prescient exactness. Also, progressions in logical AI (XAI) contribute to way better show interpretability, helping chemists in levelheaded medicate plan. This paper investigates later improvements in ML-based bioactivity forecast, challenges such as information quality and show generalizability, and future headings, counting the integration of generative AI and multi-omics information. In this field, machine learning (ML) has grown as an effective tool for promoting data-driven methods that predict the unplanned behaviour of chemical molecule.

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 Keywords

BIO-ACTIVITY PREDICTION USING MACHINE LEARNING

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Multilingual Translator has the ability to regulate a range of input formats, consisting of speech, images, documents and text. By offering precise and effective translations between several Indian languages, the objective is to remove linguistic obstacles and promote international contact. To accomplish its goals, the project makes use of already existing machine translation technology and APIs. With text translation, users can enter text in a specific language and get an output in the language of their choice. When translating images, text is first extracted from the images using optical character recognition (OCR), then the translated text is displayed below the original image. Users can upload documents in supported formats (such as txt) in .txt form for translation using document translation. The system processes the document, extracts text, translates it, and presents the translated text. Audio translation allows users to speak in one language, and the system converts the speech to text, translates it, and outputs both the translated text and synthesized speech.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Text, Image, Audio, Document, Streamlit, Google Translator APIs, Optical Character Recognition (OCR).

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Leaf diseases in rice and wheat pose a significant threat to global food security by reducing crop yields and quality. Diseases such as leaf rust, bacterial blight, blast, and many more--caused by fungi, bacteria, and viruses--spread rapidly under favourable environmental conditions, leading to severe economic losses. Traditional detection methods, which rely on visual inspection, are often labour-intensive and prone to errors. However, advancements in machine learning, molecular biology, and remote sensing have revolutionized disease detection and management. This paper focuses on the implementation of a technology-driven approach for identifying and classifying leaf diseases in rice and wheat. It examines the causes, symptoms, detection methods, and control strategies while highlighting the role of artificial intelligence and image processing in promoting sustainable agriculture.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Deep Learning, Leaf Disease, Convolutional Neural Network (CNN), Precision Agriculture, Image Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the advancement of artificial intelligence (AI) and natural language processing (NLP), chatbots have evolved into essential tools for multilingual and multi-modal communication. This paper presents an animated multilingual voice and text bot that integrates real-time language translation and speech synthesis for seamless human-computer interaction. The proposed system leverages neural machine translation (NMT) and deep learning-based text-to-speech (TTS) synthesis, ensuring accurate, real-time conversational experiences. The inclusion of animated facial expressions enhances user engagement, particularly for diverse linguistic users. This research explores the architecture, methodology, and implementation of the bot and discusses experimental results demonstrating its effectiveness in bridging language barriers.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Multilingual Chatbot, Neural Machine Translation, Text-to-Speech, Animated Conversational Agents, Natural Language Processing.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid shift to online education has underscored the need for accessible and secure examination systems, particularly for Individuals with disabilities who face barriers in traditional, visually oriented platforms. This paper presents an innovative audio-based online examination and proctoring system leveraging artificial intelligence (AI) to ensure inclusivity and integrity. By integrating speech recognition, text-to-speech synthesis, and real-time video monitoring, the proposed system enables visually impaired and disabled students to participate in assessments seamlessly. The AI-driven proctoring mechanism detects irregularities through audio and visual analysis, ensuring a fair evaluation process. Testing results indicate high accuracy in speech recognition (>90%) and robust quiz management, demonstrating the system's potential to enhance accessibility in digital education environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Audio-based examination, artificial intelligence, speech recognition, text-to-speech, proctoring, accessibility, inclusivity.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The superior breadth of data transmission through the internet is rapidly increasing in the current scenario. The images are really critical in Banking, Military, Medicine, etc, especially, in the medical field as people are unable to travel to different locations, they rely on telemedicine facilities available. All these areas hold equal importance vulnerable to intruders. So, to prevent such an act, encryption of these data can be accomplished through images. using chaos encryption. Chaos Encryption has made significant strides in the realm of Secure Communication. Its distinctive features provide a level of security that surpasses traditional algorithms. Numerous straightforward chaotic maps can be utilized for encryption purposes. In this study, we initially employ the Henon chaotic map for encryption. A comparison of this algorithm with standard algorithms is also presented. Additionally, a security assessment is conducted to demonstrate the algorithm's strength. Various existing versions, along with some novel combinations, are compared to determine if a new configuration could yield improved results. The simulation findings indicate that the proposed algorithm is both robust and user-friendly for this application. Moreover, a new combination of the map has been identified for use in this application.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Data Transmission, Chaotic Encryption, Scan Pattern, Security Analysis

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study explores the integration of artificial intelligence (AI) in the early detection and diagnosis of skin cancer, with a focus on convolutional neural networks (CNNs), transfer learning, and hybrid learning methods. The use of pre-trained models such as VGG16, coupled with advanced data augmentation and optimization techniques, demonstrates significant improvement in classifying skin lesions with high accuracy. The proposed system incorporates an enhanced CNN model, a validation module to eliminate irrelevant inputs, and an appointment scheduling feature, making it a practical and scalable tool for clinical use. Mobile AI deployment and lightweight model architectures are highlighted as effective strategies for expanding access in resource-constrained environments. Furthermore, the research addresses critical challenges in clinical adoption, including algorithmic bias, data diversity, and ethical concerns such as patient privacy. This work underscores the transformative potential of AI in dermatology by enabling early diagnosis, personalized care, and expanded access to diagnostic support in underserved regions.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Skin Cancer Detection, Convolutional Neural Networks (CNN), Transfer Learning, VGG16, Deep Learning, Dermoscopic Images, Artificial Intelligence in Healthcare, Medical Image Classification, Clinical Integration, Ethical AI, Mobile AI, Data Augmentation, Patient Privacy, Real-time Diagnostics, Hybrid Models

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

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Creative Commons Attribution 4.0 and The Open Definition