Authors: Rajani Devi K, Gowri Sankar R, Gayathri Reddy R, Harsha Vardhan V, Srikanth T

Abstract: In the modern software development landscape, countless developers—particularly students, beginners, and hobbyists—build innovative web applications but fail to deploy them to the internet due to the complexity of traditional deployment processes. Deploying an application requires extensive knowledge of cloud platforms such as AWS, GCP, or Cloudflare, involving technical hurdles including renting and configuring cloud instances, purchasing domains, setting up web servers, and managing infrastructure. This steep learning curve creates a significant barrier to entry, causing many developers to abandon their fully-functional applications at the development stage without ever making them publicly accessible, thereby limiting innovation visibility and preventing developers from building their portfolios.

 

 

Published by: vikaspatanker

Authors: Tushar Chaudhari

Abstract: The public release of ChatGPT in late 2022 marked a turning point in the adoption and academic investigation of Generative Artificial Intelligence (GenAI). This systematic literature review synthesises 39 peer-reviewed studies to evaluate the applications, pedagogical benefits, and governance challenges of GenAI across global educational contexts. The findings identify four primary application domains: personalised intelligent tutoring, automated content creation, multimodal learning materials, and academic research assistance. Synthesis of the evidence reveals substantial improvements in student performance and affective-motivational states, particularly through adaptive scaffolding and real-time feedback. However, these benefits are countered by significant risks involving academic integrity, "hallucinations," and the potential for cognitive over-reliance. Parallel to these pedagogical concerns, the evidence base remains heavily concentrated in higher education and high-income regions, leaving critical gaps in K-12 settings and the Global South. This review concludes that while GenAI offers transformative potential for personalised learning, its sustainable integration requires robust institutional policy and longitudinal research into long-term cognitive outcomes.

DOI: https://doi.org/10.5281/zenodo.19331843

Published by: vikaspatanker

Authors: Pushkar Chaudhari, Vaibhav Thakre, Tushar Chaudhari, Tanya Bhaute, Dr. Rais Khan

Abstract: Port scanning is a fundamental technique used in cybersecurity for identifying active services and potential vulnerabilities in networked systems. As modern networks grow in complexity, efficient and scalable scanning tools become increasingly important for administrators and security researchers. This paper presents the design and development of an advanced multithreaded port scanner implemented in Python and executed on the Linux operating system. The proposed system aims to provide efficient port discovery, faster scanning performance, and structured reporting for network security analysis. Unlike traditional sequential scanners, the proposed approach utilizes parallel execution techniques to analyze multiple ports simultaneously. The architecture includes modules for user input handling, scanning engine management, multithreading coordination, result processing, and reporting. Experimental evaluation demonstrates improved scanning speed and reliability compared to conventional scanning approaches.

Published by: vikaspatanker

Authors: Mrs. P.V. Javkar, Mr. Damodhar N Bulbule, Mr. Arya D Tapkir, Mr. Kaivalya R Bhadange, Mr. Devraj A Yadav

Abstract: Social media platforms have become a major part of daily communication, marketing, entertainment, and information sharing. Among them, Instagram is one of the most widely used platforms across the world. However, the rapid growth of Instagram has also led to the creation of a large number of fake accounts. These fake accounts are often used for scams, impersonation, phishing, spam promotion, fake giveaways, misinformation, and fraudulent advertisements. Detecting such accounts has become an important research problem in the field of cybersecurity and social media analysis. Traditional fake account detection systems mainly focus on profile-related information such as follower count, following count, number of posts, account age, and user activity. Although these features are useful, they may fail to detect accounts that hide suspicious content inside images. Many fake Instagram accounts include scam messages, promotional offers, fake links, or misleading text inside profile images, stories, and post images. Such hidden text cannot be effectively analyzed using normal text-based techniques alone. This paper proposes a method for detecting fake Instagram accounts using Optical Character Recognition (OCR). OCR is used to extract text from profile pictures, post images, and other visual content associated with an Instagram account. After text extraction, suspicious keywords, spam patterns, links, and unusual promotional phrases are analyzed. These OCR-based features are combined with profile-level features such as follower-following ratio, posting behavior, account age, username structure, and bio information. Based on these features, the account is classified as genuine or fake. The proposed approach improves the detection of fake accounts by analyzing both textual and visual content. This makes the system more effective in identifying hidden spam techniques used by fake profiles. The paper also discusses methodology, algorithm steps, feature extraction, preprocessing, system architecture, results, limitations, and future scope.

DOI: https://doi.org/10.5281/zenodo.19328726

Published by: vikaspatanker

Authors: Miss Payal D. Bhute, Professor Monika Ingole, Professor Vijayata Dalwankar

Abstract: With the growing prevalence of mental health disorders across the globe, the application of Artificial Intelligence (AI) and Machine Learning (ML) has gained significant attention for early detection, prevention, and intervention. This study explores various AI-based models used for mental health self-assessment, including traditional machine learning techniques such as Support Vector Machines (SVM), Logistic Regression, and Random Forest, as well as advanced deep learning approaches. Furthermore, the paper reviews commonly used datasets and highlights the role of Natural Language Processing (NLP) tools in analyzing user-generated data for identifying mental health patterns. Ethical concerns such as data privacy, bias, and transparency are also discussed, along with the feasibility of deploying these solutions through web-based platforms. The objective of this study is to summarize recent advancements and identify existing research gaps, thereby supporting the development of scalable, accessible, and ethically responsible AI-driven mental health systems.

DOI: https://doi.org/10.5281/zenodo.19328659

Published by: vikaspatanker

Authors: Janaki Ramarao Kasa, Dr. Chitra Gupta

Abstract: Due to their straightforwardness in synthesis, controllable electronic properties, high coordination abilities, and extensive biological importance, Schiff base derivatives concentrate greater importance and expertise in the research of organic and medicinal chemistry. The present study article surveys literature up to January 2016 so as to examine the impact of trends in functionalization on the chemical behaviour and medical uses of Schiff base antecedents. The research design used in the study is structured secondary research with the 60 peer-reviewed references published up to the beginning of the year 2016. The coding of these articles was by year of publication, structural classification, functional group modification, the ability to form metal-complex, biological use, and profile of activity reported. This was to identify the dominant research themes, gauge the major research treatment and interpretive uses, and to determine whether structural functionalization and metal coordination accepted superior performance. It has been disclosed that since 2010 and 2015, there was an increase in the number of studies published on Schiff bases, along with the most widespread types of use in the field were antimicrobial, anticancer, antioxidant, enzyme inhibitory, and chemosensing purposes. Schiff bases based on heteroaryl, quinoline, quinazoline, isatin, triazole and coumarin were particularly noticeable. Metal-complexed Schiff bases were found to be more commonly linked with DNA interaction, cytotoxicity and redox-based activity whereas metal free versions were more common in antimicrobial and enzyme-inhibition studies. Electron-withdrawing groups, incorporation of heterocyclic rings and the use of donor atoms like nitrogen and oxygen were always used to enhance reactivity and biological potential. The paper concludes that functionalized Schiff base derivatives were widely used as molecular platforms with twist before 2016 in selecting synthetic organic chemistry and coordination chemistry and medicinal chemistry, and the development of multifunctional therapeutics, molecular probes and catalytic systems used after 2016 builds off their earlier development.

Published by: vikaspatanker

Authors: Dr. R. Indra, Ms . Akshaya

Abstract: With the rapid advancement of digital technology, mobile banking has become an important tool for financial inclusion, especially in rural areas. This study focuses on analysing the buying behaviour of rural people towards mobile banking services. It examines the level of awareness, usage patterns, factors influencing adoption, and challenges faced by rural consumers. The study highlights that convenience, time-saving, and ease of use are the major factors encouraging adoption, while issues such as poor network connectivity, lack of digital literacy, and security concerns act as barriers. The findings reveal that most rural users have a positive attitude towards mobile banking, but there is still a need for awareness and improvement in infrastructure. The study concludes that mobile banking has strong potential to enhance financial inclusion in rural areas.

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Published by: vikaspatanker

Authors: Bhakti Pokale

Abstract: Phishing attacks have become one of the most serious cybersecurity threats worldwide, causing identity theft, financial loss, and data breaches. Attackers use fake websites, emails, and malicious links to trick users into revealing sensitive information. Traditional security mechanisms such as antivirus software and browser filters are often unable to detect newly generated phishing URLs, making users vulnerable to attacks. To address this issue, this project proposes a Real-Time Automatic Phishing Detection System that identifies and blocks phishing links instantly. The system uses Machine Learning techniques, specifically the Random Forest Classifier, to analyze URL features such as length, domain age, and special characters. It operates silently in the background without requiring user intervention, ensuring continuous and seamless protection. The system is developed using Python, Java, JavaScript, Node.js, MongoDB, HTML, and CSS to support multi-platform functionality. It provides real-time alerts and maintains logs of detected threats for further analysis. The proposed solution aims to enhance cybersecurity by offering proactive protection and ensuring a safer digital environment for individuals and organizations.

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Published by: vikaspatanker

Authors: Jyothsna M, Srinika Kontham, Sharanya Balachandran, Meghana Danta, Sindhu Naine

Abstract: The SignVoice system is based on artificial intel- ligence technology that offers a bidirectional communication system for deaf, mute, and visually impaired persons to com- municate smoothly with normal persons. The SignVoice system is based on machine learning, deep learning, and computer vision technologies to offer different types of communication such as sign language, speech, text, and image-based communication. The hand gestures are recorded through the webcam and processed through MediaPipe to identify the landmark and classify the image through machine learning to produce text output. The input is converted into text through Whisper for speech input, and the text output is generated through an artificial intelligence- based chatbot and then converted into audio through text- to-speech technology. The SignVoice system is based on the hybrid approach to process the gestures through client-side processing and computationally intensive operations such as speech recognition through cloud-based services. In addition to this, the chatbot can perform image input, text output, and speech output that can be helpful for visually impaired persons. The proposed SignVoice system can communicate efficiently and accurately for impaired persons through gesture, speech, and intelligent text-based responses.

DOI: https://doi.org/10.5281/zenodo.19326545

 

Published by: vikaspatanker

Authors: P.Sreesudha, CV.Kiranmaiee, P.Santhoshini, Nadia Shareen, Megha Chandana, Harini Vadla

Abstract: Real-time object detection and environmental awareness are essential components in assistive technologies for visually impaired individuals. Traditional mobility aids provide limited information about surrounding objects and their proximity, making independent navigation difficult in complex environments. In this work, an AI-based assistive vision system is proposed that integrates the YOLOv8 deep learning model for real-time object detection, distance estimation techniques for proximity awareness, and text-to-speech output for auditory feedback. The system captures input from a camera, detects and classifies multiple objects in the environment, estimates their distance from the user, and converts the detected object labels along with distance information into speech output. This enables visually impaired users to understand nearby obstacles and objects more effectively while moving in indoor and outdoor environments. The proposed approach offers a practical, low-cost, and efficient assistive solution by combining computer vision and artificial intelligence to enhance user safety, independence, and confidence. Experimental observations indicate that the system performs effectively for common object categories and provides meaningful audio guidance in real time

DOI: https://doi.org/10.5281/zenodo.19280231

Published by: vikaspatanker