Adroid Conference Series: Engineering and Technology
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
Adroid Connectz Private Limited (Adroid Publishing)
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 Conference on Artificial Intelligence for Sustainable Development” (ICAISD-2025). All the contributing authors owe thanks from the organizers of ICAISD-2025 for their interest and exceptional articles. We would also like to thank the authors of the papers for adhering to the time schedule and for incorporating the review comments. We wish to extend my heartfelt acknowledgment to the authors, peer-reviewers, committee members and production staff whose diligent work put shape to the ICAISD-2025 proceedings. We especially want to thank our dedicated team of peer-reviewers who volunteered for the arduous and tedious step of quality checking and critique on the submitted manuscripts. The management, faculties, administrative and support staff of the college has always been extending their services whenever needed, for which we remain thankful to them. Lastly, we would like to thank Adroid Conference proceedings for accepting our proposal for publishing the ICAISD-2025 conference proceedings.
Copyright (c) 2025 Adroid Conference Series: Engineering and Technology
COMPARATIVE ANALYSIS OF LSTM, ARIMA, AND LINEAR REGRESSION FOR E-COMMERCE SALES PRICE PREDICTION
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.
CONDITIONAL FASTSPEECH 2 FOR LOW-RESOURCE INDIAN ACCENT SYNTHESIS: A PHONETIC ADAPTATION APPROACH
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.
PERSONALIZED HEALTH RECOMMENDATION SYSTEM USING MACHINE LEARNING
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.
HEART DISEASE PREDICTION USING MACHINE LEARNING WITH PYTHON
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.
A NOVEL WASTE MANAGEMENT MODEL USING IOT IN SMART CITIES
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.
IOT POWERED SMART IRRIGATION SYSTEM: BALANCING PERFORMANCE AND ENERGY EFFICIENCY
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.
HOUSE PRICE PREDICTION USING LINEAR REGRESSION
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.
AI-DRIVEN DYNAMIC COVER CROP MODEL FOR SUSTAINABLE AGRICULTURE
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.
METAHEURISTIC-ASSISTED HYBRID DEEP LEARNING FRAMEWORK FOR SKIN CANCER DETECTION, SEGMENTATION, CLASSIFICATION, AND SEVERITY PREDICTION
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.
UNCOVERING HIDDEN BIASES IN MACHINE LEARNING MODELS: A STEP TOWARD ETHICAL AI
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.
WOMEN SAFETY ANALYTICS: A FRAMEWORK FOR PROTECTING WOMEN FROM THREATS
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.
EARLY DETECTION OF NATURAL DISASTER: FOCUS ON FLOODS IN INDIA
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]
AI-DRIVEN DETECTION OF LUNG CARCINOMA: LEVERAGING DEEP LEARNING IN CHEST CT ANALYSIS
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.
E-FARMING: A SMART AGRICULTURE SUPPORT AND MARKET INTEGRATION PLATFORM
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 deployment on platforms like Firebase or AWS, enabling the platform to support a big user base without experiencing performance deterioration. Crop management techniques are optimized by preliminary simulations. These improvements show how the platform can revolutionize conventional farming practices, bridging the technological and agricultural divide and promoting a more lucrative and sustainable agricultural ecosystem.
SECURE ACCESS CONTROL VIA BIOMETRIC VERIFICATION, TOTP, AND BLACKLIST ENFORCEMENT
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.
A NOVEL APPROACH TO SECURITY OF INTERNET OF THINGS
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 on by malevolent behavior. Energy consumption: the aggregate of the nodes' energy usage throughout the simulation. By reducing latency (communication speeds up after malicious nodes are isolated), minimizing packet loss (malicious traffic is dropped before damaging the network), increasing throughput (more legitimate packets reach their destination), and improving energy efficiency (nodes no longer waste energy on phony hello packets), the outcomes show how much better performance is achieved with the recommended approach.
BLOCK-CHAIN INTEGRATION IN REAL ESTATE: A TECHNOLOGICAL SHIFT
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.
LEAFDOC: AN END-TO-END SYSTEM FOR REAL-TIME PLANT DISEASE DIAGNOSIS AND ADVISORY USING GENERATIVE AI
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.
SKIN DISEASE PREDICTION BASED ON MEDICAL IMAGES USING DEEP LEARNING TECHNIQUES: A REVIEW
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.
REINFORCEMENT LEARNING STRATEGIES FOR ENERGY EFFICIENT CLUSTER HEAD SELECTION IN WSNS
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.
INTERVIEW HUB – AN AI-POWERED REAL-TIME MOCK INTERVIEW PLATFORM USING VOICE AGENT
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].
PERSONALIZED LEARNING PATHWAYS FOR STUDENTS USING ADAPTIVE AI AND LIMITED EDUCATIONAL DATA
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.
TEST CASE SELECTION TECHNIQUES FOR SOFTWARE PRODUCT LINE USING AI TECHNIQUES: A SURVEY
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.
ADOPTION OF EMERGING EDUCATIONAL TECHNOLOGIES IN URBAN PRIVATE SCHOOLS: A NETWORK-DRIVEN APPROACH TO FUTURE LEARNING
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.
AI-DRIVEN STUDENT PROFILING FOR DROPOUT, EMPLOYABILITY, AND CAREER PATH PREDICTION VIA HYBRID MODELING
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.