Adroid Conference Series: Engineering and Technology
type: channel
description: Adroid Conference Series: Engineering and Technology is a conference series focused on sustainable engineering, aiming to share the latest insights and expertise in this diverse field. It is open-access and undergoes peer review, targeting researchers, academic professionals, and postgraduate students. The conference imposes a strict double-blind peer-review process and upholds the ethical standards set by the Committee on Publication Ethics (COPE) to maintain publication quality. For quote submission -> Request For Quotation Year: 2025 Frequency of the publication: Annual Publication format: Online Subject: Engineering Language/s: English Highlights of the Adroid Conference Series: - Faster Publication - ⁠Conference Series Indexed (Indexing) - ⁠DOI Assignment to each paper - ⁠Peer-Reviewed Open Access Publication - ⁠Post Publication, the Proceedings will be Submitted to Scopus, WoS for possible Indexing - ⁠Opportunity to publish TOP Quality papers in Adroid Journals
publisher: Adroid Connectz Private Limited (Adroid Publishing)
language: en
publication name: Adroid Conference Series: Engineering and Technology
items: Preface
items: COMPARATIVE ANALYSIS OF LSTM, ARIMA, AND LINEAR REGRESSION FOR E-COMMERCE SALES PRICE PREDICTION
items: CONDITIONAL FASTSPEECH 2 FOR LOW-RESOURCE INDIAN ACCENT SYNTHESIS: A PHONETIC ADAPTATION APPROACH
items: PERSONALIZED HEALTH RECOMMENDATION SYSTEM USING MACHINE LEARNING
items: HEART DISEASE PREDICTION USING MACHINE LEARNING WITH PYTHON
items: A NOVEL WASTE MANAGEMENT MODEL USING IOT IN SMART CITIES
items: IOT POWERED SMART IRRIGATION SYSTEM: BALANCING PERFORMANCE AND ENERGY EFFICIENCY
items: HOUSE PRICE PREDICTION USING LINEAR REGRESSION
items: AI-DRIVEN DYNAMIC COVER CROP MODEL FOR SUSTAINABLE AGRICULTURE
items: METAHEURISTIC-ASSISTED HYBRID DEEP LEARNING FRAMEWORK FOR SKIN CANCER DETECTION, SEGMENTATION, CLASSIFICATION, AND SEVERITY PREDICTION
items: UNCOVERING HIDDEN BIASES IN MACHINE LEARNING MODELS: A STEP TOWARD ETHICAL AI
items: WOMEN SAFETY ANALYTICS: A FRAMEWORK FOR PROTECTING WOMEN FROM THREATS
items: EARLY DETECTION OF NATURAL DISASTER: FOCUS ON FLOODS IN INDIA
items: AI-DRIVEN DETECTION OF LUNG CARCINOMA: LEVERAGING DEEP LEARNING IN CHEST CT ANALYSIS
items: E-FARMING: A SMART AGRICULTURE SUPPORT AND MARKET INTEGRATION PLATFORM
items: SECURE ACCESS CONTROL VIA BIOMETRIC VERIFICATION, TOTP, AND BLACKLIST ENFORCEMENT
items: A NOVEL APPROACH TO SECURITY OF INTERNET OF THINGS
items: BLOCK-CHAIN INTEGRATION IN REAL ESTATE: A TECHNOLOGICAL SHIFT
items: LEAFDOC: AN END-TO-END SYSTEM FOR REAL-TIME PLANT DISEASE DIAGNOSIS AND ADVISORY USING GENERATIVE AI
items: SKIN DISEASE PREDICTION BASED ON MEDICAL IMAGES USING DEEP LEARNING TECHNIQUES: A REVIEW
items: REINFORCEMENT LEARNING STRATEGIES FOR ENERGY EFFICIENT CLUSTER HEAD SELECTION IN WSNS
items: INTERVIEW HUB – AN AI-POWERED REAL-TIME MOCK INTERVIEW PLATFORM USING VOICE AGENT
items: PERSONALIZED LEARNING PATHWAYS FOR STUDENTS USING ADAPTIVE AI AND LIMITED EDUCATIONAL DATA
items: TEST CASE SELECTION TECHNIQUES FOR SOFTWARE PRODUCT LINE USING AI TECHNIQUES: A SURVEY
items: ADOPTION OF EMERGING EDUCATIONAL TECHNOLOGIES IN URBAN PRIVATE SCHOOLS: A NETWORK-DRIVEN APPROACH TO FUTURE LEARNING
items: AI-DRIVEN STUDENT PROFILING FOR DROPOUT, EMPLOYABILITY, AND CAREER PATH PREDICTION VIA HYBRID MODELING

Preface
type: item
description: We hereby are delighted to announce that Jaypee University Anoopshahar, UP, India in association with SAGE University, Indore, India, has hosted the eagerly awaited and much coveted “International Conference on Artificial Intelligence for Sustainable Development” (ICAISD-2025) in Hybrid Mode. The conference was able to attract a diverse range of engineering practitioners, academicians, scholars and industry delegates, with the reception of abstracts including more than 150 authors from different parts of the world. The committee of professionals dedicated towards the symposium is striving to achieve a high-quality technical program with track on Computing, Artificial Intelligence for Sustainable Development. Therefore, a lot of research is happening in the above-mentioned track and its related sub-areas. More than 100 full-length papers have been received, among which the contributions are focused on theoretical, computer simulation-based research, and laboratory-scale experiments. Amongst these manuscripts, 25 papers have been included in the Adroid Conference proceedings after a thorough two-stage review and editing process. All the manuscripts submitted to the ICAISD-2025 were peer-reviewed by at least two independent reviewers, who were provided with a detailed review proforma. The comments from the reviewers were communicated to the authors, who incorporated the suggestions in their revised manuscripts. The recommendations from two reviewers were taken into consideration while selecting a manuscript for inclusion in the proceedings. The exhaustiveness of the review process is evident, given the large number of articles received addressing a wide range of research areas. The stringent review process ensured that each published manuscript met the rigorous academic and scientific standards. It is an exalting experience to finally see these elite contributions materialize into a book volume as ICAISD-2025 Adroid Conference proceedings entitled “International Confer…
subject: Articles
  topic: section
creator: Nishant Shrivatsava
creator: Jitendra Saxena
creator: Aditya Khamparia
creator: Dr. Mahmood Afzal Hussain
creator: Anand Kadukuntla
rights: Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
date: 2025-11-25
publication date: 2025-11-25
doi: 10.63503/c.acset.2025.preface

COMPARATIVE ANALYSIS OF LSTM, ARIMA, AND LINEAR REGRESSION FOR E-COMMERCE SALES PRICE PREDICTION
type: item
description: In the rapidly evolving landscape of e-commerce, accurate price prediction and sales forecasting have become vital for operational efficiency, inventory management, and strategic decision-making. This study presents a comprehensive methodology employing preprocessing techniques, normalization strategies, data splitting, and advanced modeling—specifically Linear Regression, Long Short-Term Memory (LSTM), and AutoRegressive Integrated Moving Average (ARIMA) to predict product sales prices using an E-Commerce Sales dataset. The raw data undergoes missing-value handling and column pruning to enhance relevancy. Subsequently, normalization is applied through both MinMax and Standard Scaler techniques to ensure scale uniformity. The preprocessed dataset is then partitioned into training and test sets, facilitating both model evaluation and predictive capability assessment. Performance is quantified using key metrics—Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) accompanied by comparative visualizations. Our findings demonstrate the strengths and trade-offs of each algorithm in forecasting accuracy, showcasing their potential for real-world e‑commerce applications. By illustrating the comparative performance of traditional statistical techniques and deep learning models, the study provides a roadmap for stakeholders to choose appropriate forecasting tools aligned with their business needs.
subject: Articles
  topic: section
subject: E-Commerce
  topic: keywords
subject: Sales Price Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Time Series Forecasting
  topic: keywords
subject: LSTM (Long Short-Term Memory)
  topic: keywords
subject: ARIMA
  topic: keywords
creator: Hare Ram Sah
creator: Apoorv Vyas
rights: Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
date: 2025-11-25
publication date: 2025-11-25
starting page: 34
ending page: 43
doi: 10.63503/c.acset.2025.4

CONDITIONAL FASTSPEECH 2 FOR LOW-RESOURCE INDIAN ACCENT SYNTHESIS: A PHONETIC ADAPTATION APPROACH
type: item
description: In essence, text-to-speech (TTS) applications that read aloud text are becoming more and more crucial for digital accessibility in order to access digital content. The issue is that they struggle to understand Indian languages and our regional accents (when peaks English with Indian accent features). This paper discusses the FastSpeech2 system. To it, we applied an accent embedding layer. It can speak in three different languages thanks to this layer: standard Hindi, Indian English, and Bhojpuri, a low-resource language. In order to identify accent-appropriate pronunciations for a particular accent, particularly the less common Bhojpuri ones, we employed a transformer-based LLM-adapted tool for speech and phonetic mapping. Graphemes to phonemes (G2P) mapping is accomplished by this adaptive phonetic mapping module! We tested it, and the findings show notable advancements. Enhancement of 27.5 Essential Points in Plain English for the Bhojpuri Accent The primary issue is that low-resource languages and a variety of Indian accents perform poorly on current text-to-speech (TTS) systems. Our Method: By incorporating an Accent Embedding Layer, we enhanced a TTS system (FastSpeech2). What it can do: It can now communicate in Standard Hindi, Bhojpuri, and Indian English. Method: To modify pronunciations for those distinct accents, we employed a large language model (LLM). As a result, human listeners judged the voices as natural, and it speaks Bhojpuri much more accurately.
subject: Articles
  topic: section
subject: Text-to-Speech
  topic: keywords
subject: Accent Synthesis
  topic: keywords
subject: FastSpeech 2
  topic: keywords
subject: Indian English
  topic: keywords
subject: Bhojpuri
  topic: keywords
subject: Phonetic Mapping
  topic: keywords
subject: LLM
  topic: keywords
subject: Speech Processing
  topic: keywords
creator: Ritesh Kumar Yadav
creator: Divyanshi Srivastava
creator: Sweacha Verma
creator: Chaudhary Surya Prakash
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 230
ending page: 235
doi: 10.63503/c.acset.2025.25

PERSONALIZED HEALTH RECOMMENDATION SYSTEM USING MACHINE LEARNING
type: item
description: The collection of health data has become ubiquitous. Wearable devices, fitness applications, and digitized health records culminate into a plethora of health data. While the volume of data continues to grow, the utilization of data in personalizing health recommendations remains scarce. Most recommendations offered are simple, generic, and often misguided. Machine learning can bridge this gap. Unlike advanced rule based systems, modern machine learning systems can dynamically adjust their recommendations as a person’s health status shifts. The focus of this project is the development of a machine learning-based, personalized health recommendation system. It has the ability to integrate disparate data sources to provide meaningful and actionable individual recommendations. These recommendations could stem from fitness wearables, medical history, data on sleep, dietary patterns, and additional health variables. Equally as important is the system’s emphasis on privacy, and trustworthiness, designed to promote sustained reach. Personalization is more flexibility but systems also need to incorporate principles of privacy, trustworthiness, and fairness in order to foster system adoption. The goal of such a system is to transform the healthcare process from reactively where issues are addressed after they arise — to proactive and preventative. With tailored recommendations, people can better understand and detect potential health issues, reducing the need for costly interventions. This is no longer merely an advancement in technology; it is an improvement to an intelligent system of care that puts the users — now more enlightened about their health — at the center of the process.
subject: Articles
  topic: section
subject: Personal Health
  topic: keywords
subject: Health Recommendation Engines
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Data Analysis
  topic: keywords
subject: Data Domain
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Health Data and Lifestyle Choices
  topic: keywords
subject: Preventive Health
  topic: keywords
subject: Human-Centered Design
  topic: keywords
subject: Live Recommendations
  topic: keywords
subject: Device Wearables
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Health Surveillance
  topic: keywords
creator: Ritik Goswami
creator: Anubhav Mishra
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 223
ending page: 229
doi: 10.63503/c.acset.2025.24

HEART DISEASE PREDICTION USING MACHINE LEARNING WITH PYTHON
type: item
description: In every year, heart disease is one of the major causes of death for millions of people all over the world. Therefore, diagnosis and risk prediction play a crucial role in reducing its impact on human life. This research presents a data-driven approach that helps in predicting heart diseases using multiple machine learning algorithms and techniques, implemented in Python. The study focuses on effective and accurate data preprocessing, exploratory analysis, feature selection, and model optimization to achieve reliable and accurate prediction of heart disease, using models such as Logistic Regression, Random Forest, and Gradient Boosting. We have compared all these models based on their performance and accuracy metrics. The research findings show that ensemble-based models exhibit superior accuracy and robustness compared to traditional classifiers. The proposed framework highlights the potential of machine learning as a clinical decision-support tool, offering a cost-effective and accessible method for early detection of cardiovascular risk.
subject: Articles
  topic: section
subject: Heart Disease Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Ensemble Models
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Python
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
creator: Gautam Kumar
creator: Harshvardhan Thakur
creator: Pradeep Kumar
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 212
ending page: 222
doi: 10.63503/c.acset.2025.23

A NOVEL WASTE MANAGEMENT MODEL USING IOT IN SMART CITIES
type: item
description: The rapid increase in urbanization, industrialization, and population growth has caused an unprecedented surge in waste generation. Traditional waste management systems, which depended on fixed collection schedules and inefficient routing, are struggling to cope with the escalating volume and complexity of waste. The existing waste is also harmful to our health and environment. Accumulated waste serves as an ideal breeding ground for bacteria, insects, and flies, increasing the risk of food-borne illnesses. Decomposing waste releases toxic gases such as Carbon Dioxide, Methane, and Nitrous Oxide, contributing to air pollution and related health hazards. Hazardous waste like electronic items and plastic disrupts ecosystems, and improper segregation of waste at household and industrial levels remains a major challenge, adding significant pressure on already overburdened landfills. This article proposes a multifaceted approach that utilizes Internet-of-Things-powered self-sustainable smart bins, each monitored via web interfaces accessible to the general public to promote transparency and accountability. The model also encourages proper disposal of domestic waste using a reward-based mechanism powered by blockchain technology while emphasizing sustainable recycling techniques such as material flow mapping and other technological assessments.
subject: Articles
  topic: section
subject: Smart Bins
  topic: keywords
subject: Waste Segregation
  topic: keywords
subject: Environmental Monitoring
  topic: keywords
subject: Sustainable Recycling
  topic: keywords
creator: Abhik Patra
creator: Hritam Basu
creator: Prantik Roy
creator: Ranajit Saha
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 205
ending page: 211
doi: 10.63503/c.acset.2025.22

IOT POWERED SMART IRRIGATION SYSTEM: BALANCING PERFORMANCE AND ENERGY EFFICIENCY
type: item
description: Countries are working towards making agriculture more sustainable by integrating various technologies to improve its operations. Implementing improvements in irrigation systems is crucial for water-use efficiency and contributes to Goal 6 and Goal 6.4 of the United Nations Sustainable Development Goals (SDGs). This research paper aims to highlight the contribution of smart irrigation using the Internet of Things (IoT) and sensing systems to the Sustainable Development Goals. This study is based on a qualitative design and focuses on secondary data collection methods. Automated irrigation systems are essential for water conservation, and improvements can play a significant role in reducing water use. Agricultural and farming technologies are also being integrated with IoT and automation to make the entire process more effective and efficient. Sensor systems have helped farmers better understand their crops, reduce environmental impacts, and conserve resources. These advanced systems enable efficient water management, as well as effective soil and weather monitoring. Irrigation systems are identified as positive contributors to optimized irrigation systems, which can lead to increased use of ongoing research and development focused on enhancing sustainable operations and reducing costs. Finally, the challenges and benefits of implementing sensor-based irrigation systems are discussed. This review will help researchers and farmers better understand irrigation technologies and provide adequate perspectives for irrigation activities.
subject: Articles
  topic: section
subject: Irrigation
  topic: keywords
subject: Sustainable Development Goals
  topic: keywords
subject: Smart Irrigation
  topic: keywords
subject: Agriculture
  topic: keywords
creator: Rudra Kishore
creator: Shivli Singh
creator: Ritick Pandey
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 197
ending page: 204
doi: 10.63503/c.acset.2025.21

HOUSE PRICE PREDICTION USING LINEAR REGRESSION
type: item
description: Prediction of prices of houses is very important for buyers, sellers, and planners in the real estate market. In this paper, it is explained how Linear Regression can be used for estimating and predicting the prices of houses using the Ames Housing Dataset. Ames Housing Dataset includes data of 1460 houses and their 81 different features or attributes about the houses. We first cleaned missing data of the dataset using simple methods like replacing them with median or most common values. Then we trained the Linear Regression Model and tested it. Our model resulted in 0.82 score for R², 22417 for MAE, and an RMSE of about 36,879. From this, we understand that even a simple model like Linear Regression can work well in predicting prices of houses. In the end, we also discussed various studies related to our work and also included how we can improve this system in the future.
subject: Articles
  topic: section
subject: House Price Prediction
  topic: keywords
subject: Linear Regression
  topic: keywords
subject: Data Mining
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Ames Housing Dataset
  topic: keywords
creator: Ames Housing Dataset
creator: Adarsh Sahani
creator: Kundan Paswan
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 189
ending page: 196
doi: 10.63503/c.acset.2025.20

AI-DRIVEN DYNAMIC COVER CROP MODEL FOR SUSTAINABLE AGRICULTURE
type: item
description: The intensive use of chemical fertilizers, pesticides, and mono-cropping systems in modern agriculture has led to significant environmental degradation, soil fertility loss, and decreased biodiversity. Cover crops, which are non-cash crops grown to improve soil health and fertility, offer a promising solution to these challenges. However, their adoption is limited due to various constraints, including economic, knowledge, andmanagement barriers. This study aims to investigate the role of cover crops in sustainable agriculture, identify the barriers to their adoption, and propose strategies for integrating them into different farming systems. Our proposed AI-Driven Adaptive Cover Crop Model (AIDCCM) leverages artificial intelligence, real-time data analytic, and predictive algorithms to optimize cover crop selection, management, and integration into sustainable agriculture. The model workflow involves data collection, analysis, implementation, monitoring, andfeedback loop. The advantages of AIDCCM include precision, scalability, adaptability, farmer- friendliness, and sustainability. We propose a phased implementation strategy, including pilot projects, partnerships, and training programs. AIDCCM has the potential to revolutionize sustainable agriculture by optimizing cover crop integration, addressing ecological, economic, and social challenges, and ensuring a more resilient and sustainable future for global agriculture.
subject: Articles
  topic: section
subject: Sustainable Agriculture
  topic: keywords
subject: Cover Crops
  topic: keywords
subject: AI Driven Model
  topic: keywords
subject: Soil Health
  topic: keywords
subject: Data Analytic
  topic: keywords
subject: Barriers to Adoption
  topic: keywords
subject: Sustainability Impact
  topic: keywords
subject: Climate Adaptability
  topic: keywords
creator: Piyush Kumar Gupta
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 182
ending page: 188
doi: 10.63503/c.acset.2025.19

METAHEURISTIC-ASSISTED HYBRID DEEP LEARNING FRAMEWORK FOR SKIN CANCER DETECTION, SEGMENTATION, CLASSIFICATION, AND SEVERITY PREDICTION
type: item
description: Skin cancer constitutes the most prevalent form of carcinoma, posing a significant threat to public health, with melanoma recognized for its particularly high mortality rate. The timely recognition of this pathology is crucial for the enactment of effective treatment modalities; nevertheless, traditional diagnostic techniques often face challenges due to deficiencies in image clarity and the complexities inherent in visual discrimination. This study introduces an advanced deep learning approaches designed to achieve superior segmentation and categorization of cutaneous neoplasms, with a distinct focus on the assessment of severity. Furthermore, the research includes a comprehensive analysis of the severity classifications of the identified cancers. Altogether, the potential of advanced deep learning methodologies to transform the landscape of skin cancer diagnostics is apparent, offering an integrative approach that enhances early detection, improves classification accuracy, and supports severity assessment, ultimately contributing to superior patient management and outcomes.
subject: Articles
  topic: section
subject: Skin Disease Detection
  topic: keywords
subject: Classification
  topic: keywords
subject: Preprocessing
  topic: keywords
subject: Dermoscopic Images
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Punam R. Patil
creator: Ritu Tandon
creator: Rajesh Kumar Nagar
creator: Bhushan V. Patil
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 173
ending page: 181
doi: 10.63503/c.acset.2025.18

UNCOVERING HIDDEN BIASES IN MACHINE LEARNING MODELS: A STEP TOWARD ETHICAL AI
type: item
description: With the widespread use of artificial intelligence (AI) systems in everyday applications, ensuring fairness has become a critical aspect of system design. AI models increasingly influence high-stakes decisions in domains such as finance, hiring, and criminal justice, where biased outcomes can have severe societal consequences. Recent developments in machine learning and deep learning have begun to address these challenges, emphasizing the need for systematic bias mitigation strategies.This study presents a structured framework for bias detection and mitigation in machine learning, aiming to reconcile predictive performance with algorithmic fairness. Using the UCI Adult dataset as a case study, the framework adopts a dual-phase approach: preprocessing with Reweighing to correct historical imbalances and postprocessing with Threshold Optimization to adjust decision thresholds for protected and unprotected groups. This combination addresses both data-level and decision-level disparities without requiring complete model retraining.The framework was evaluated across four supervised models—k-Nearest Neighbors, Decision Tree, Logistic Regression, and Random Forest. Mitigated models achieved substantial reductions in Demographic Parity Difference and Equal Opportunity Difference, alongside improved Disparate Impact Ratios, while maintaining competitive accuracy, precision, recall, and F1-score. These findings demonstrate that fairness-aware interventions can reduce group-level bias without critical performance loss, contributing to the development of trustworthy and socially responsible AI systems.
subject: Articles
  topic: section
subject: Algorithmic Bias
  topic: keywords
subject: Fairness in Machine Learning
  topic: keywords
subject: Bias Mitigation
  topic: keywords
subject: Ethical AI
  topic: keywords
subject: Demographic Parity
  topic: keywords
subject: Equal Opportunity
  topic: keywords
subject: Disparate Impact Ratio
  topic: keywords
creator: Aniket Maity
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 162
ending page: 172
doi: 10.63503/c.acset.2025.17

WOMEN SAFETY ANALYTICS: A FRAMEWORK FOR PROTECTING WOMEN FROM THREATS
type: item
description: Across the globe, women's safety is a major problem. Concerns over hazards in online environments, emotional injury, and physical threats are still on the rise. In addition to harassment and other forms of exploitation on digital platforms, gender-based violence has escalated. Such patterns necessitate proactive measures to protect women. In any setting, every woman should feel secure and safe. The Women's Safety App is a dependable online resource. In an emergency, it provides prompt assistance. Users can send alerts to trustworthy contacts with only one tap. They can also call for an ambulance or speak with the local authorities. When time is of the essence, this arrangement provides immediate assistance. Real-time location updates are provided by the app. Friends and family are always informed of the user's location. The app has a number of built-in functions. One has to do with an intelligent SOS system. It has settings for automatic triggering and a panic button. GPS and network signals are used for real-time tracking. Notifications are sent using geofencing as necessary. Network priority allows emergency calls to connect quickly. Multiple contacts receive pre-programmed distress messages. Sharing a location is simple. Coordination of ambulances happens quickly. The national emergency services are directly linked. The software assists in preventing hazards before they arise, in addition to issuing alarms. Women can discreetly exit unpleasant circumstances with the use of a false call feature. The app's ultimate goal is to promote a generally safer and more attentive workplace. Women can go about their daily lives with more confidence and calm.
subject: Articles
  topic: section
subject: Women’s Safety
  topic: keywords
subject: Emergency App
  topic: keywords
subject: Real-Time Tracking
  topic: keywords
subject: SOS Detection
  topic: keywords
subject: Microphone Access
  topic: keywords
creator: Punam R. Patil
creator: Rajeshwari Vijay Shukla
creator: Pranali Nandusing Tawar
creator: Harshali Namdev Mahajan
creator: Diksha Krushna Sonawane
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 151
ending page: 161
doi: 10.63503/c.acset.2025.16

EARLY DETECTION OF NATURAL DISASTER: FOCUS ON FLOODS IN INDIA
type: item
description: Floods represent one of the most severe and recurring natural disasters in India, accounting for substantial human, economic, and environmental losses every year. With nearly 40 million hectares of land prone to flooding, India is among the most flood-affected countries in the world. The dense populations along major river basins such as the Ganges, Brahmaputra, and Godavari make communities highly vulnerable to inundation, property damage, agricultural disruption, and long-term displacement. Early detection and timely warning are therefore critical in reducing loss of life and minimizing the socioeconomic burden caused by flood events. [7], [12] Need for Early Detection Traditional flood forecasting methods, such as rainfall-runoff models and statistical trend analysis, provide valuable insights but often suffer from limitations in accuracy and lead time. These methods struggle to capture nonlinear interactions between rainfall, river discharge, soil saturation, and topography. Moreover, delays in disseminating alerts mean that even when forecasts are generated, affected communities may not have enough time to act. [1], [10], [12]
subject: Articles
  topic: section
subject: Flood detection
  topic: keywords
subject: early warning system
  topic: keywords
subject: remote sensing
  topic: keywords
subject: hydrological modeling
  topic: keywords
subject: IoT
  topic: keywords
subject: machine learning
  topic: keywords
subject: India
  topic: keywords
subject: disaster risk reduction
  topic: keywords
creator: Chhavi Teotia
creator: Ishita Bansal
creator: Nancy Gupta
creator: Agam Kaushik
creator: Punit Mittal
creator: Sharyar Malik
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 142
ending page: 150
doi: 10.63503/c.acset.2025.15

AI-DRIVEN DETECTION OF LUNG CARCINOMA: LEVERAGING DEEP LEARNING IN CHEST CT ANALYSIS
type: item
description: Lung diseases encompass a range of conditions that impair lung function and disrupt the respiratory system. Among these, lung carcinoma remains one of the leading causes of mortality worldwide. Early detection significantly improves survival prospects, as diagnosing lung cancer at an initial stage is crucial for effective treatment. Over time, early identification has contributed to an increase in the average survival rate for lung cancer patients, rising from 14 percent to 49 percent. Computed tomography (CT) imaging plays a critical role in this diagnostic process, offering higher sensitivity and accuracy compared to traditional X-ray imaging. Several imaging methods that complement one another. A deep neural network for lung cancer detection. The ability to detect cancer using CT pictures has been created and tested. In order to classify the lung picture as whether it's benign or malignant, a densely linked convolutional neural network (DenseNet) and adaptive learning can help. The researchers utilized a dataset of 201 lung scans, with 90 percent of the pictures being positive. 10 percent of the data are utilized for testing and classification, while the rest are used for training. The proposed approach obtained an accuracy of 97.50% in tests, according to the results.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: CNN
  topic: keywords
subject: CT scans
  topic: keywords
subject: Lung Cancer
  topic: keywords
subject: AI
  topic: keywords
creator: Ritu Tandon
creator: Ati Jain
creator: Narendra Pal Singh Rathore
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 136
ending page: 141
doi: 10.63503/c.acset.2025.14

E-FARMING: A SMART AGRICULTURE SUPPORT AND MARKET INTEGRATION PLATFORM
type: item
description: Methodology: Schematic illustration of an integrated farming system and management quality control Farmers frequently deal with a number of problems that limit their ability to be lucrative and productive. Unpredictable weather patterns like droughts, floods, and unseasonable rainfall frequently result in significant agricultural losses. Farmers are especially vulnerable to economic volatility since market price fluctuations make it difficult for them to predict the returns on their produce. Lack of timely and accurate information about crop prices, weather, and potential consumers exacerbates these difficulties by pushing many farmers to use outdated methods that are typically less profitable and inefficient. The Nt-framework-model In order to tackle these urgent concerns, this study presents E-Farming is a state-of-the-art platform designed specifically to empower farmers with modern technology for market integration and smart agriculture support. E-Farming incorporates real-time weather forecasts, dynamic crop pricing information, and a comprehensive buyer-seller networking system into an easy-to-use user interface. By including these components, the platform helps farmers make informed decisions regarding crop selection, harvesting dates, and sales strategies, reducing the risks related to weather fluctuations and market uncertainty. From a technical standpoint, E-Farming uses a mix of contemporary web technologies, such as PHP, Node.js, and React.js, to provide accessible and responsive user interfaces for reliable server-side functionality, as well as MySQL/MongoDB for effective data management and storage. RESTful APIs enhance communication between the client and server, guaranteeing smooth data flow. The platform's design places a high priority on security, with HTTPS protocols and JWT (JSON Web Token) authentication offering safe access and data protection. Additionally, high availability, scalability, and dependability are guaranteed via cloud-based deploy…
subject: Articles
  topic: section
subject: Smart Agriculture
  topic: keywords
subject: E-Farming
  topic: keywords
subject: Market Integration
  topic: keywords
subject: React.js
  topic: keywords
subject: Node.js
  topic: keywords
subject: MongoDB
  topic: keywords
subject: Weather Forecasting
  topic: keywords
subject: JWT
  topic: keywords
subject: RESTful API
  topic: keywords
creator: Deepak Gupta
creator: Dev Baliyan
creator: Dhruval Chauhan
creator: Himanshu Chauhan
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 130
ending page: 135
doi: 10.63503/c.acset.2025.13

SECURE ACCESS CONTROL VIA BIOMETRIC VERIFICATION, TOTP, AND BLACKLIST ENFORCEMENT
type: item
description: As cyber security threats continue to evolve, the limitations of conventional authentication mechanisms have become increasingly evident, necessitating the adoption of advanced, multi-factor security frameworks. This research introduces a robust access control architecture that seamlessly integrates biometric facial recognition, Time-based One-Time Password (TOTP) verification, and a dynamic blacklist enforcement mechanism. The system's first line of defense leverages real-time facial detection and recognition to ensure accurate and efficient identification of authorized users based on unique biometric traits. To counter risks such as credential compromise and biometric spoofing, the framework incorporates TOTP as a secondary authentication factor, employing secure cryptographic algorithms to generate user- specific, time-sensitive codes. Furthermore, the inclusion of a dynamic blacklist protocol enables the prompt identification and restriction of access to unauthorized or previously flagged entities. This component is augmented with automated alerting and detailed logging to support real- time monitoring, auditing, and incident response. Experimental evaluations demonstrate that the proposed system achieves high authentication accuracy, enhanced resistance to spoofing and replay attacks, and increased operational robustness. Its modular and scalable design facilitates seamless integration into diverse high-security environments, including governmental, corporate, and critical infrastructure settings. By uniting biometric verification, cryptographic token-based authentication, and proactive access control, this framework presents a comprehensive and resilient solution to contemporary access management challenges.
subject: Articles
  topic: section
subject: Biometric Authentication
  topic: keywords
subject: Face Recognition
  topic: keywords
subject: Time-based One Time Password (TOTP)
  topic: keywords
subject: Multifactor Authentication (MFA)
  topic: keywords
subject: Blacklist Enforcement
  topic: keywords
creator: Srishti Tilkar
creator: Gaurav Shrivastava
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 119
ending page: 129
doi: 10.63503/c.acset.2025.12

A NOVEL APPROACH TO SECURITY OF INTERNET OF THINGS
type: item
description: Using simple communication protocols, the Internet of Things (IoT) links billions of objects, from sensors to smart appliances. Applications in smart cities, industry, healthcare, and transportation are made possible by this interconnectedness, but it also poses new security risks. IoT devices are particularly vulnerable to assaults because, in contrast to traditional computing systems, they frequently have limitations with regard to processing power, memory, battery life, and bandwidth. An example of this type of assault is the Hello Flood attack, in which a malevolent node bombards the network with an overwhelming number of "hello" packets. The following consequences result from normal IoT nodes responding to these packets, which they interpret as genuine neighbor discovery messages: Denial of Service (DoS) (disturbance of legitimate communication), Network congestion (bandwidth consumed by malicious traffic), and Resource exhaustion (nodes wasting energy replying to fake requests). Conventional defense Techniques like machine learning-based intrusion detection systems or strong cryptography. Due to their high computational and energy consumption, detection is inappropriate for limited IoT contexts. Your work presents a trust-based detection and isolation approach to close this gap. With this method, every network node is tracked and given a trust value determined by its actions. Reduced trust is applied to nodes that exhibit aberrant behavior, such as sending out an excessive number of hello packets. A node is deemed malicious and removed from the routing process whenever its trust score drops below a certain level. Network Simulator-2 is used to implement and assess the plan (NS-2). Among the important performance indicators are: The delay is the amount of time it takes for data packets to get to their destination. Throughput: How quickly data is successfully transferred across a network. The percentage of dropped or lost packets is known as packet loss. Brought…
subject: Articles
  topic: section
subject: Internet of Things
  topic: keywords
subject: Hello Flood Attack
  topic: keywords
subject: Trust-based Security
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: NS-2
  topic: keywords
creator: Vivek Kumar
creator: Vishal Kohli
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 108
ending page: 118
doi: 10.63503/c.acset.2025.11

BLOCK-CHAIN INTEGRATION IN REAL ESTATE: A TECHNOLOGICAL SHIFT
type: item
description: Despite being one of the most important asset classes in the world, real estate transactions are still difficult, expensive, and ineffective. With the use of distributed ledgers and smart contracts, block-chain technology has become a disruptive force that can solve these issues by bringing efficiency, transparency, and immutability. This study explores the use of block-chain technology to real estate, emphasizing how it might improve trust, lower intermediary costs, enable fractional ownership, and address liquidity concerns. In addition to analyzing block-chain development platforms like Ethereum and Hyperledger, a thorough literature review has been carried out. The suggested system makes use of smart contracts to expedite rental agreements, investment tokenization, and property ownership transfers. Offers some directions for further study to facilitate broader adoption. Although there are still issues with scalability, interoperability, and regulations, the results show increased efficiency, security, and transparency when compared to older methods. This study offers a thorough analysis of block-chain adoption in the real estate industry and suggests directions for further research to facilitate broader implementation.
subject: Articles
  topic: section
subject: block-chain
  topic: keywords
subject: Real Estate
  topic: keywords
subject: Smart Contracts
  topic: keywords
subject: Distributed Ledger
  topic: keywords
subject: Transparency
  topic: keywords
subject: Hyperledger
  topic: keywords
subject: Ethereum
  topic: keywords
creator: Shivam Singhal
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 96
ending page: 107
doi: 10.63503/c.acset.2025.10

LEAFDOC: AN END-TO-END SYSTEM FOR REAL-TIME PLANT DISEASE DIAGNOSIS AND ADVISORY USING GENERATIVE AI
type: item
description: Plant diseases significantly impact global agriculture, leading to large-scale yield losses and threatening food security. Existing deep learning models provide diagnostic accuracy but lack integrated remediation guidance. This paper presents LeafDoc, an end-to-end system combining EfficientNet-B0-based classification and a generative AI advisory engine to deliver real-time disease diagnosis and management recommendations. Achieving 98.7% classification accuracy, the system outperforms existing solutions like Plantix and Agrio. Methodology, motivation, system architecture, and comparative evaluation with existing models are presented in detail.
subject: Articles
  topic: section
subject: Computer Vision
  topic: keywords
subject: Plant Pathology
  topic: keywords
subject: Generative AI
  topic: keywords
subject: MERN Stack
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Agricultural Technology
  topic: keywords
subject: Decision Support Systems
  topic: keywords
creator: Ritik Kumar
creator: Prashant Yadav
creator: Ujjwal Singh
creator: Prakhar Gupta
creator: Preksha Pratap
creator: Punit Mittal
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 85
ending page: 95
doi: 10.63503/c.acset.2025.9

SKIN DISEASE PREDICTION BASED ON MEDICAL IMAGES USING DEEP LEARNING TECHNIQUES: A REVIEW
type: item
description: Skin diseases are common these days. Ignoring these diseases can lead to development of skin cancer in the current situation. In recent years, deep learning has evolved as an emerging technique for predicting disease more efficiently in the early stages as compared to other techniques. This greatly benefits from the application of image examination technique which is broadly beneficial to Medical Services. In Dermatology’s intricacy and high cost make it challenging to diagnose. Using deep learning dermatoscopic images can be detected at an early stage. Early-stage detection of skin lesions is useful to predict whether the lesion can lead to cancer and then it can be treated before it spreads throughout the different organs of body. The growing incidence of skin conditions worldwide calls for the creation of automated, scalable, and precise diagnostic tools to support primary care physicians and dermatologists.This paper provides a survey on some of the prevalent skin diseases focusing on their symptoms, prediction and accuracy via focusing on recent advancement of using deep learning techniques.The purpose of this study is to provide researchers and clinicians a collective understanding of how deep learning methods can improve diagnostic accuracy, reduce manual workload, and support clinical decision-making in dermatology.
subject: Articles
  topic: section
subject: Deep learning
  topic: keywords
subject: Convolutional Neural Networks
  topic: keywords
subject: Benign
  topic: keywords
subject: Malignant
  topic: keywords
subject: Skin cancer (skin disease)
  topic: keywords
creator: Divya
creator: Ranjna Jain
creator: R. Girija
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 77
ending page: 84
doi: 10.63503/c.acset.2025.8

REINFORCEMENT LEARNING STRATEGIES FOR ENERGY EFFICIENT CLUSTER HEAD SELECTION IN WSNS
type: item
description: Wireless Sensor Networks (WSNs) are important for numerous IoT applications. The limitation of the universal applications of WSN is due to the constrained energy reservoirs of the sensor nodes. For data communication, clustering of sensor nodes is an established scheme for minimizing the energy expenditure by appointing Cluster Heads (CHs) responsible for data aggregation and relay. This paper describes an approach for developing the cluster and CH selection dynamically for WSNs, leveraging Reinforcement Learning (RL), specifically Q-learning. The framework of this paper integrates residual energy level of a node and localized node density into the RL state space, empowering individual nodes to make perceptive decisions regarding their roles. Moreover, the CH selection mechanism prioritizes nodes that not only exhibit elevated Q-values for the CH function but are also optimum distance from the Base Station (BS), thereby minimizing cumulative transmission energy. The simulations show that this RL-driven, adaptive paradigm effectively equilibrates energy consumption throughout the network, resulting in an extended network lifespan and improved energy efficiency compared to traditional methodologies.
subject: Articles
  topic: section
subject: Wireless Sensor Networks
  topic: keywords
subject: Cluster Head Selection
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Q-learning
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Network Lifetime
  topic: keywords
subject: Node Density
  topic: keywords
creator: Ruchi Sharma
creator: Sachin Patel
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 66
ending page: 76
doi: 10.63503/c.acset.2025.7

INTERVIEW HUB – AN AI-POWERED REAL-TIME MOCK INTERVIEW PLATFORM USING VOICE AGENT
type: item
description: In today’s highly competitive job market, the ability to excel in interviews is critical for career success. Traditional mock interview methods, though beneficial, are constrained by limited access to professional mentors, scheduling conflicts, subjective feedback, and lack of scalability [1], [2]. The emergence of Artificial Intelligence (AI), Natural Language Processing (NLP), and speech-to-text technologies has paved the way for immersive, automated, and personalized interview preparation platforms [3], [4]. This research introduces INTERVIEW HUB, an AI-powered real-time mock interview system designed to simulate realistic interview scenarios, evaluate responses intelligently, and provide structured, unbiased feedback [5]–[7].
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Mock Interview
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Voice Agent
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Interview Preparation
  topic: keywords
creator: Anubhav Singh
creator: Anubhav Tyagi
creator: Divyansh Agarwal
creator: Deepak Som
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 56
ending page: 65
doi: 10.63503/c.acset.2025.6

PERSONALIZED LEARNING PATHWAYS FOR STUDENTS USING ADAPTIVE AI AND LIMITED EDUCATIONAL DATA
type: item
description: Adaptive systems based on Artificial Intelligence (AI) are highly dependent on the paradigm of personalized education, which focuses on tailoring learning experiences to the individual needs of students. The major obstacle to effective and widespread adoption of these systems is that they require high volumes of student interaction data. This reliance poses a critical issue in data-sparse settings, as with new students (the cold-start problem), on niche or advanced courses with limited enrolment, or where data privacy rules are highly restrictive. As a result, the possibility of real personalization is not realized in many cases of practical education. To overcome this inherent setback, a new Hybrid Meta-Transfer Learning (HMTL) framework is presented. HMTL framework is designed to create effective customized learning pathways with minimal initial data. It does so by a synergistic, two-stage architecture: a Transfer Learning component, which is used to construct a robust, generalized Foundational Knowledge Core with data-rich source domains, and a Meta-Learning-based Rapid Adaptation Engine, which is then used to specialize this Core to a new student using only a small number of initial interactions. An extensive simulation shows that the suggested framework outperforms baseline models by a very large margin in making predictions about student performance as well as in terms of the efficiency of the produced learning pathways. This paper introduces the HMTL framework with its architecture, mathematical formulation, and empirical validation, showing a major step forward in the development of a really personalized learning experience using minimal educational data.
subject: Articles
  topic: section
subject: Personalized Learning
  topic: keywords
subject: Adaptive AI
  topic: keywords
subject: Limited Data
  topic: keywords
subject: Data Sparsity
  topic: keywords
subject: Meta-Learning
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Student Modeling
  topic: keywords
subject: Educational Data Mining
  topic: keywords
creator: Sanchita Kiran
rights: Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
date: 2025-11-24
publication date: 2025-11-24
starting page: 44
ending page: 55
doi: 10.63503/c.acset.2025.5

TEST CASE SELECTION TECHNIQUES FOR SOFTWARE PRODUCT LINE USING AI TECHNIQUES: A SURVEY
type: item
description: Software Product Line Engineering (SPLE) is an approach in the field of software engineering where industrial production techniques are applied, and software development is gradually altered. Reuse and maintaining the traceability of variant features of all products belonging to the same product line family are fundamental requirements. The ideal approach to software development is to utilize software engineering techniques to precisely and successfully regulate it. In this research, we will describe previous work of different researchers related to test case selection and software product lines, focusing more on the TCS technique, its benefits, and its challenges.
subject: Articles
  topic: section
subject: software product line
  topic: keywords
subject: techniques of test case selection
  topic: keywords
subject: feature model
  topic: keywords
subject: challenges of TCS
  topic: keywords
creator: Amit Kumar
creator: Pratap Singh
creator: Satendra Kumar
rights: Copyright (c) 2025
date: 2025-11-24
publication date: 2025-11-24
starting page: 21
ending page: 33
doi: 10.63503/c.acset.2025.3

ADOPTION OF EMERGING EDUCATIONAL TECHNOLOGIES IN URBAN PRIVATE SCHOOLS: A NETWORK-DRIVEN APPROACH TO FUTURE LEARNING
type: item
description: This study explores how rapidly evolving educational technologies are influencing teaching and learning practices in urban private schools in India. Using two contrasting Delhi-based institutions—one operating with a fully digitized model and the other employing a blended approach—the research evaluates the integration of platforms such as Schoology, Embibe, Raz-Kids, ICT 360, and TeachNext. A mixed-methods design was applied, involving survey responses from 60 teachers and 80 students, along with interviews with two principals. Results indicate that technology-enhanced instruction improves student engagement, fosters self-directed learning, and increases teacher confidence in digital pedagogy. At the same time, the findings highlight persistent challenges, including infrastructure constraints, uneven training quality, and access disparities. The outcomes contribute practical recommendations for policymakers and school leaders aiming to implement inclusive, future-oriented digital learning environments.
subject: Articles
  topic: section
subject: Educational technology
  topic: keywords
subject: Digital learning
  topic: keywords
subject: ICT 360
  topic: keywords
subject: Schoology
  topic: keywords
subject: Embibe
  topic: keywords
subject: Raz-Kids
  topic: keywords
subject: TeachNext
  topic: keywords
subject: Private education
  topic: keywords
subject: Blended learning
  topic: keywords
creator: Haritika
creator: Anita Beniwal
creator: Poonam Beniwal
rights: Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
date: 2025-11-24
publication date: 2025-11-24
starting page: 13
ending page: 20
doi: 10.63503/c.acset.2025.2

AI-DRIVEN STUDENT PROFILING FOR DROPOUT, EMPLOYABILITY, AND CAREER PATH PREDICTION VIA HYBRID MODELING
type: item
description: In the era of Outcome-Based Education (OBE) and employability-driven learning, educational institutions face increasing pressure to assess student readiness beyond academic performance. This study presents a hybrid machine learning framework that integrates rubric-based scoring, supervised classification, and unsupervised clustering to predict holistic student outcomes. A comprehensive dataset of 1500 undergraduate students (2015–2025) from Tamil Nadu, India, was used, encompassing academic scores, aptitude results, behavioral traits, communication skills, leadership qualities, and employability indicators. Six classifiers—Decision Stump, Hoeffding Tree, J48, LMT, Random Tree, and RepTree—were evaluated using 10-fold cross-validation. J48 achieved the highest accuracy of 99.1%, followed by LMT and RepTree. Additionally, K-Means clustering was employed to uncover natural groupings of students into four categories: Industry-Ready, Higher Studies Aspirants, Dropout Risk, and Entrepreneurial Potential. The proposed hybrid model enhances prediction accuracy, supports early interventions, and provides actionable insights for institutional decision-making. This work aligns with national education policies and global employability standards, offering a scalable framework for data-driven student evaluation and career readiness planning.
subject: Articles
  topic: section
subject: Educational Data Mining (EDM)
  topic: keywords
subject: Outcome-Based Education (OBE)
  topic: keywords
subject: Student Employability
  topic: keywords
subject: Classification Algorithms
  topic: keywords
subject: Rubric-Based Evaluation
  topic: keywords
subject: K-Means Clustering
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Holistic Assessment
  topic: keywords
subject: Dropout Prediction
  topic: keywords
subject: Industry Readiness
  topic: keywords
creator: Vijayakumar K
creator: Naveen A
rights: Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
date: 2025-11-24
publication date: 2025-11-24
starting page: 1
ending page: 12
doi: 10.63503/c.acset.2025.1