Volume 13 | Issue 4 |

 Abstract

Skin cancer is one of the most prevalent and life-threatening diseases worldwide, emphasizing the need for accurate and early diagnosis to improve patient outcomes. This research presents an advanced skin cancer detection frame- work that integrates the Swin Transformer (Shifted Window Transformer) with semantic segmentation techniques for the classification of dermatoscopic images. The Swin Transformer effectively captures hierarchical global features, enabling precise differentiation between melanoma and benign lesions. To enhance the model's generalizability, preprocessing tech- niques such as image resizing, normalization, and augmentation are applied to datasets including HAM10000, ISIC-2008, ISIC- 2019, and PH-2. Furthermore, Explainable AI (XAI) methodolo- gies, including Grad-CAM and Grad-CAM++, are incorporated to provide visual explanations, highlighting crucial regions influ- encing classification decisions. The model's performance is evalu- ated using accuracy, sensitivity, specificity, and the Dice similarity coefficient, ensuring reliable and interpretable diagnostics. Additionally, Bayesian Optimization is employed for hyperpa- rameter tuning, optimizing model performance while maintaining computational efficiency. This research contributes to the field of AI-driven dermatology by offering a transparent and robust diagnostic tool that assists dermatologists in early melanoma detection. Future enhancements include optimizing the model for real-time edge deployment, expanding datasets to improve generalizability across diverse skin tones, and integrating multi- modal data for enhanced diagnostic accuracy.

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 Keywords

Interpretable Machine Learning, Swin Trans- former, Explainable AI, Skin Lesion Classification, Attention Mechanism, Model Transparency, Performance Metrics.

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Effective post-operative pain management is a critical component of modern surgical care, directly impacting recovery time, patient satisfaction, and overall outcomes. The goal of postoperative pain control is to reduce the negative consequences of postsurgical pain and help the patient transition smoothly back to normal function. Traditionally opioid analgesic therapy has served the mainstay of treatment for postoperative pain. This review explores traditional and emerging strategies in post-operative pain control including pharmacological, regional anaesthesia, and multimodal approaches. It emphasizes patient-centred care and highlights recent innovations and challenges.

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Post-Operative Pain, Analgesia, Multimodal Pain Management, Opioids, Regional Anaesthesia, Enhanced Recovery

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Transoral Robotic Surgery (TORS) has revolutionized the management of head and neck cancers, offering a minimally invasive approach with enhanced visualization and precision. This study compares the oncologic, functional, and quality-of-life outcomes of TORS with traditional open and endoscopic techniques in patients with oropharyngeal and laryngeal malignancies. Data synthesis from multicenter studies and institutional cohorts reveals favorable surgical margins, reduced morbidity, shorter hospital stays, and improved swallowing function in patients undergoing TORS. However, certain limitations regarding tumor accessibility and cost remain significant. This manuscript discusses the clinical benefits, challenges, and future scope of TORS in the treatment of head and neck cancers.

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Head and neck cancers, particularly oropharyngeal squamous cell carcinomas (OPSCC), present unique anatomical and functional challenges in surgical oncology. Traditional surgical approaches often compromise speech and swallowing, leading to considerable morbidity. The advent of Transoral Robotic Surgery (TORS) has enabled surgeons to perform complex resections via the oral cavity, reducing the need for external incisions and improving post-operative outcomes.

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DeepFake technology, driven by artificial intelligence, has transformed digital media manipulation, making it possible to create highly realistic synthetic images and videos. While this advancement offers benefits in entertainment and content production, it also poses significant ethical and security challenges, such as misinformation, identity fraud, and unauthorized impersonation. This research focuses on developing an advanced DeepFake detection system that leverages machine learning techniques to identify and counteract these risks. The proposed system comprises two key components: the User Module, which allows user registration, media uploads for detection, and result tracking, and the Admin Module, responsible for content moderation and system oversight. Utilizing sophisticated deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), the system is designed to deliver high detection accuracy, real-time processing, and adaptability to evolving DeepFake techniques. This study emphasizes the importance of automated DeepFake detection in maintaining digital authenticity and trust. It addresses the increasing threats posed by manipulated media across various sectors, including journalism, cybersecurity, and social media governance.

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 Keywords

DeepFake Detection, Machine Learning, Convolutional Neural Networks, Recurrent Neural Networks, AI in Cybersecurity, Digital Forensics, Face Forgery, Synthetic Media, GANs, Feature Extraction, Real-Time Detection, Image and Video Forensics, Adversarial Learning, Identity Theft Prevention, Misinformation Control, Ethical AI, Explainable AI, Content Moderation, Neural Networks, Data Augmentation, Pattern Recognition, Computational Photography, Flask Application, PyTorch, Deep Learning, Web-Based D

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The Project, FORMULATION AND EVALUATION OF HERBAL CHURNA AND JUICE AS A LIVER TONIC FOR THE TREATMENT OF JAUNDICE, Jaundice is a clinical condition characterized by yellowing of the skin, eye, and mucous membranes due to elevated bilirubin levels, remains a significant health concern world-wide. Conventional medicine treatment causes liver dysfunction and infection. However herbal medicine gained attention for their potential efficacy, affordability and minimal side effect. This review explores the role of the herbal plants in managing jaundice, emphasizing their hepatoprotective and anti-oxidant properties. It is characterized by yellow skin, mucosa, and sclera resulting from the increased production of bilirubin, usually over 2.5 and 3.0 mg/dL. Jaundice may occur either from increased production or reduced excretion of bilirubin due to several diseases and conditions that affect the liver, such as different types of hepatitis, liver cirrhosis, Gilbert syndrome, cholestasis, alcohol or drugs abuse, autoimmune disorders, hemolytic anemia, liver cancer, etc. For treatment of jaundice various herbs are used such as bhumi Amla (Phyllanthus niruri), tulsi (Ocimum tenuiflorum) and Kutki (Picrorhiza kurroa). The plants were arranged with correct nomenclature along with their common name, family, botanical name, the part of plant with their mechanism of action which have been reported in different kinds of literature. The use of decoction is the most preferred method of herbal preparation. The study indicates that the local inhabitants rely on medicinal plants for treatment. This paper proposed further clinical experimentation in order to scientifically evaluate these widely used herbal remedies for possible bioactive effects.

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we are making syrup and churna for treatment of jaundice

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This study explores the influence of China-made mobile phones on consumer buying behavior in Coimbatore City. With the rapid penetration of China-made mobile phone brands like Xiaomi, Realme, Vivo, and Oppo in the Indian market, consumer preferences and purchasing patterns have shifted significantly. The study analyzes factors such as price sensitivity, features, brand perception, after-sales service, and peer influence, which affect consumer choices. The findings aim to help marketers and businesses understand consumer expectations and develop strategies to compete or collaborate with Chinese brands.

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IMPACT OF CHINA-MADE MOBILE PHONES ON CONSUMER BUYING BEHAVIOUR WITH SPECIAL REFERANCE TO COIMBATORE CITY

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Calcaneal spur is a bony overgrowth from calcaneum bone which occur when foot exposed to constant strain leads to calcium deposition in heel bone, it is painful condition. It can be correlated with vatakantaka according to Ayurveda and can be treated with agnikarma. Agnikarma is an important parasurgical procedure of Ayurvedic surgeons, which has been used widely in the clinical practice. Agnikarma has emerged as an integral part of the Ayurvedic therapeutics. In Ayurveda, agnikarma is a treatment in which heat is applied to a particular part of body to reduce inflammation there and hence the underlying pain.Due to Ushna Teekshna and Sukshma Guna of Agni it reduces the Localised Vata.It stimulates musculoskeletal tissue to remove spasm pain.Agnikarma therapy is mostly used in treatment of calcaneal spur by treating vata. Agnikarma treatment is carried out with a special instrument made of 5 metals (Copper, Iron, Zinc, Silver & Lead, also contains gold in some special ones), thus is named as Pancha Dhatu Shalaka. A patient successfully treated with Agnikarma therapy is presented in case study.

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Agnikarma, Calcaneal Spur, Vatakantaka, Heel Pain, Ayurveda, Parasurgical Procedure

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Although agriculture is the foundation of India's economy, farmers encounter difficulties such as limited access to professional advice, lack of individualised guidance, and language barriers. These problems are addressed by Kisan Sathi, a voice-activated, multilingual virtual assistant that offers data-driven, locally relevant agricultural recommendations, natural language processing, and machine learning. In order to ensure inclusivity across literacy levels, the platform supports both voice and text interfaces. By providing advice on crop management, pest control, weather updates, and sustainable practices, it empowers farmers and bridges the digital divide in India.

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Voice assistance, multilingual chatbots, smart farming, virtual assistants, natural language processing (NLP), sustainable farming, kisan support, and crop advisory.

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Creative Commons Attribution 4.0 and The Open Definition

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This project proposes the development of an AI-based journal management website aimed at streamlining the submission, review, and publication processes for academic journals. The website leverages artificial intelligence to automate key aspects of journal management, including manuscript categorization, plagiarism detection, reviewer assignment, and predictive analytics for paper acceptance likelihood. The system integrates natural language processing (NLP) for semantic analysis of articles and offers an intuitive dashboard for editors, authors, and reviewers, enhancing user experiences. With features such as AI-driven content suggestions, automated formatting, and personalized recommendations, the platform reduces manual overhead, increases efficiency, and fosters collaboration in the academic publishing ecosystem. Secure access, role-based authorization, and AI-powered chatbots are also incorporated for real-time support and communication. This approach provides a comprehensive solution for journal management, ensuring higher accuracy, scalability, and streamlined publication workflows.

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Manuscript Processing, Academic Publishing, Peer Review System, Editorial Workflow, Digital Content Management

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With the growth of smart transportation systems, there is a need for reliable ways to detect and read license plates automatically. This research introduces an ALPR (Automatic License Plate Recognition) system that works well even in difficult situations, like poor lighting, messy backgrounds, or unusual license plate designs. The system uses two main technologies. It finds the license plate in an image with a fast and accurate model called YOLO (You Only Look Once). After the License plate is detected then, it uses LPRNet (License Plate Recognition Network) to read the letters and numbers on the plate. These steps make the system quick and accurate. The process involves finding the license plate, breaking the characters into separate parts, and creating a text box with the results. It can handle challenges like low-quality images, dirty plates, and background noise while still working well. Tests on different datasets show that the system works in both indoor and outdoor conditions and can recognize license plates in many languages and styles. This system has many practical applications, including automatic payment collection at toll booths, traffic monitoring, law enforcement, and smart city projects, where fast and accurate vehicle identification is essential. By providing reliable results in real-time, this ALPR system helps make transportation systems more efficient and supports various operations like managing traffic and enforcing laws. By using advanced computer vision techniques, this recognition system provides an efficient solution for vehicle identification tasks.

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 Keywords

License Plate Recognition, YOLO, LPRNet, Smart Transport, Vehicle Identification, Edge detection

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