Scientific Digest : Journal of Applied Engineering
"Scientific Digest : Journal of Applied Engineering" appears to be a publication focused on research and advancements in the field of applied engineering. Journals like this typically feature articles, studies, and reviews that cover various engineering disciplines, including but not limited to mechanical, civil, electrical, and software engineering. Editor- In-Chief : Dr. AMAR GERA Title proper : SCIENTIFIC DIGEST : JOURNAL OF APPLIED ENGINEERING ISSN : 2348-4802 Subject : Multidisciplinary Publisher : SCIENTIFIC DIGEST : JOURNAL OF APPLIED ENGINEERING Dates of publication : 2013 Frequency : By Monthly Language : English Country : India Medium : Online Indexed by : ROAD, THE KEEPERS Impact Factor (IF): 6.734 Submit Manuscript : joae.editor@gmail.com Title DOI of the Journal : http://doi.org/10.70864/issn.2348-4802
ScientificDigest: Journal of Applied engineering
Human-Computer Interaction (HCI) is shifting towards natural, contactless Interfaces where gestures play a vital role. Finger Counting, a fundamental hand gestures, has applications in education, assistive technology, and touchless device control. Traditional systems use sensor gloves or contour-based image processing, which are either expensive or unreliable under changing conditions. This paper proposes a real-time finger counting system using Python, Opencv and mediaPipe. The system detects hand marks from a webcam feed and applies a landmark-based algorithm to count extended fingers accurately. The implementations achieve ~96% accuracy across varied backgrounds and lighting conditions.
Computer Vision, Finger Requirement, OpenCV, MediaPipe, Hand tracking, FingerCounting
Copyright (c) 2025 Scientific Digest : Journal of Applied Engineering
AN ANALYSIS OF THE BNPL (BUY NOW PAY LATER) TREND IN MODERN INDIA WITH INSIGHTS FROM MUMBAI
The idea of Buy Now Pay Later (BNPL) ages back in time; the period when small retail stores also called as ‘Kirana’ stores were the lifeline of each and every household. Maintaining an account book for each customer and tallying the same at the end of the month was the normal norm in those days. This gave a higher spending power or it gave them the liberty to spend over their capacity is a question worth researching. The same concept is now opted by the ‘online retail store’ and also ‘online payment applications’ likeAmazon, Freecharge, Paytmetc. The objective of this paper is to understand the awareness level among consumers regarding the brands offering BNPL trend. Further, the researchers are also trying to comprehend the impact of BNPL on the economy of India. Additionally, they are also trying to gauge the future of BNPL in a Tier I city. The researchers will be performing a convenience sampling with a sample size of approximately 100 consumers. According to the research findings majority of the people are aware about the concept of BNPL in Mumbai. Further, it is majorly used on e-commerce websites like Amazon and Flipkart. Further, the current status of the BNPL is not that significant and can prove to be a negative impact on the economy. This feature has proved to be a benefit; in order to increase the spending capacity of the consumer to a large extent. They are optimistic about the future of the same and that it will be a boost for the economy in the near future; if applied in the correct manner.
BNPL, consumers awareness, kirana stores, e-commerce websites
Visualization and Analysis of Dimensional Group Interactions: A 3D Plot of πa, πb, and Dependent Pi Term π₈(pt)
This study focuses on the visualization and interpretation of dimensionless group interactions using the Buckingham Pi theorem. Specifically, it explores the interdependence among dimensionless terms: πa (product of positive Pi terms), πb (product of negative Pi terms), and the dependent Pi term π₈ (pt). Through 3D surface plots, the research demonstrates how physical variables derived from empirical processes like wheat grinding can be compactly represented using Pi terms for improved analytical modeling. This approach allows for the simplification of complex relationships, optimization of system parameters, and generalization of experimental results across varying conditions.
Buckingham Pi Theorem, Dimensional Analysis, Pi Terms, 3D Surface Plot, Atta Chakki, Wheat Grinding, Process Modeling, πa, πb, Dependent Pi Term π₈.
Copyright (c) 2022 Scientific Digest : Journal of Applied Engineering
Data-Driven Prediction of Electrical Energy Efficiency from Consumer Behavior
Energy efficiency has been an important concern in India since the latter part of the twentieth century. The primary objective of this survey is to evaluate the level of energy efficiency awareness among Indian consumers. For this purpose, households and showrooms in two districts—Bengaluru Urban (Karnataka) and Ghaziabad (Uttar Pradesh)—were selected for data collection, including feedback from sellers. The collected data was analyzed to develop regression models aimed at predicting individual awareness of energy efficiency. The analysis focused on five key criteria to identify factors influencing consumer knowledge and behavior regarding energy-efficient practices. The survey results indicate that energy efficiency awareness in India remains relatively low, particularly in smaller households and less urbanized areas. Using a dataset comprising approximately 50 households and 25 showrooms, relationships between household energy use behaviors were estimated. The findings suggest that knowledge of energy consumption patterns and available energy-efficient technologies is positively associated with the adoption of energy conservation measures. Additionally, household characteristics, including age, education, and income, significantly influence energy use behaviors, with younger and more educated households showing a higher likelihood of adopting energy-efficient appliances and practices. These insights highlight the need for targeted awareness campaigns and educational initiatives to promote energy-efficient practices across diverse consumer groups in India
Electrical energy, Regression, R-squared, Energy saving, Energy conservation
INTELLIGENT AGRICULTURE EQUIPMENT MANAGEMENT THROUGH IOT-BASED MONITORING AND CONTROL
Our country is agricultural based with its main occupation. Yet today because of the rural to urban migration, there is a set-back to agriculture. To deal with this issue we then resort to smart farming methods through IoT. In this project, there are different features, such as GPS based remote controlled monitoring, moisture and temperature sensors, intruders scaring, security, leaf wetness and adequate irrigation facilities. It utilizes wireless sensor networks to record the soil properties and the environmental factors at a regular time. The sensor nodes are put at different positions within the farm. These parameters are controlled via any remote device or internet services and they are controlled by interfacing sensors, Wi-Fi and camera to microcontroller. This idea is developed as a product and handed to the welfare of the farmers. The significant source of human life is water. Water in agriculture field 80 per cent to 90 per cent. Since the growth in globalization and population water consumption also rises due to the daily rise. Challenge before all the countries is decreasing the amount of water used on farms and supplying fresh and healthy food. Automation is one of the significant role in human life today. The system does not only offer comfort but it also saves energy, efficiency and saves time. In case of a temperature alteration, humidity and current condition of rain of the environment these sensors detect the alteration in temperature and humidity and sends an interrupt sign to the raspberry Pi. The industries are currently operating with automation and control machines that are costly and inapplicable in a farm and a garden field. In this project we will develop a smart irrigation technology using Raspberry pi as the IOT. The water motor can be automatically controlled by using the system and it can also be used to monitor the growth of plant using webcam. Using WiFi network, we can use appropriate application to watch live streaming of farm on mobile phone. The primary core of the entire system is Raspberry pi.
ROLE OF NANO-SILICA IN ENHANCING THE DURABILITY OF CONCRETE: AN ANALYTICAL AND EXPERIMENTAL EVALUATION
This study assesses the sorptivity, water absorption, and acid resistance of the M70 grade concrete concrete that has been modified by the use of nano silica. As a strong reference mix, 10 percent silica fume and 11 percent fly ash were used to prepare the normal M70 concrete as partial replacements of cement. To improve its durability, it was added that nano silica was used as an extra partial substitute. Resistance to acids was evaluated by casting concrete cubes, curing them in 28 days, and exposing them to 5 percent sulfuric acid (H 2 SO 4) and 5 percent hydrochloric acid (H Cl) after 30 and 90 days, respectively. Water absorption test and sorptivity test were also undertaken according to the set procedures in order to test permeability and pore structure improvement. According to the findings, micro silica concrete showed low levels of degradation as compared to normal mix. Nano silica concrete lost weight of 2.12% in HCl and 2.95% in H2SO4 after exposure to acid of 90 days, compared with a normal mix, which lost 4.51% and 5.75% of its weight, respectively. In the case of nano silica concrete, the remaining compressive strength compared to the original was 93 and the normal mix constituted 86. The water absorption percentage decreased with 2.45% in the normal mix to 1.65 in nano silica concrete. Even the capillary suction and permeability became significantly lower, as it is observed that sorptivity decreased to 1.39 mm, water absorption decreased, and acid resistance increased, which are factors leading to the overall durability and service life of highstrength concrete.
Nano-silica, High-performance concrete, Durability, Acid resistance, Water absorption, Sorptivity, M70 grade concrete.
FROM TRADITIONAL ASSETS TO ALTERNATIVES: EMERGING TRENDS IN INVESTMENT STRATEGIES
The world is well and truly changing over the face of investments with a gradual and a steady transformation witnessed in moving towards asset classes other than equities and bonds. The selected trend toward the growth of investment strategies is revealed in this paper by discussing the rise of the investment in private equity and hedge funds, real estate, commodities and cryptocurrencies and the rise of ESG-driven instruments. The research will be based on secondary data gathered in global and emerging market situations regarding India in special. The study will focus on the portfolio diversification by institutional and retail investors in order to maximize their returns, risk hedging, and attaining sustainable goals. The results show that the investments in alternatives have increased significantly, with major allocations going through the private equity and real assets, and digital assets, as well as the ESG integration, as a still emerging trend. Artificial intelligence, block chain and tokenisation are new technological advancements and continue to improve accessibility and efficiency in the construction of portfolios. Nevertheless, these opportunities are counteracted by other challenges, such as the regulatory ambiguity, illiquidity and volatility. The paper shows that a hybrid investment approach (combining familiar stability with alternative diversification) can produce resilience, innovation and long-term value creation within a dynamic financial landscape
Alternative investments; Traditional assets; Investment strategies; Private equity; Hedge funds; Cryptocurrencies; ESG investing; etc
ADVANCING AI FOR COMPUTER-AIDED DETECTION SYSTEMS IN CHEST X-RAY DIAGNOSTICS
Chest X-ray diagnostics play a crucial role in detecting and diagnosing various respiratory conditions, including pneumonia, tuberculosis, lung cancer, and COVID-19. Radiologists analyze X-ray images to identify abnormalities, but the process is time-consuming and prone to human error. The history of chest X-ray diagnostics has evolved from manual interpretation to computer-assisted detection methods, enhancing accuracy and efficiency. Early prediction models relied on statistical techniques and rule-based systems, which lacked adaptability to complex patterns. Before AI advancements, diagnosis depended on radiologists' expertise, with methods such as manual film analysis and basic image processing techniques. These approaches required significant time, leading to delays in treatment. Several early computer-aided detection systems provided limited support, but they lacked precision and required constant human intervention. The increasing demand for rapid and accurate diagnoses, combined with the limitations of manual analysis, has driven the need for an AI-powered approach. Human dependency, misdiagnosis due to fatigue, and inefficiency in handling large volumes of data are significant challenges in conventional diagnostic methods. Machine learning models trained on vast datasets can accurately classify chest diseases, reducing diagnostic errors and improving patient outcomes. Integrating deep learning techniques with a web application enables automated, real-time predictions, allowing users to upload X-ray images and receive instant analysis. This system enhances accessibility and supports healthcare professionals in making informed decisions. By leveraging convolutional neural networks, image preprocessing, and predictive analytics, the proposed system significantly improves diagnostic accuracy, speed, and reliability, minimizing the burden on medical professionals while ensuring early disease detection
Wireless Sensor Network With Modified Sdn For Dynamic Load Balanced And Multipath Routing
Wireless Sensor Networks (WSNs) have undergone significant advancements over the past few decades, becoming a crucial component in various applications such as environmental monitoring, healthcare, and industrial automation. Traditionally, WSNs have relied on static routing protocols that lack adaptability to changing network conditions. This rigidity often results in congestion, energy inefficiency, and suboptimal overall network performance. Fixed routing paths frequently cause imbalanced load distribution, ultimately reducing the lifespan and efficiency of sensor nodes. A key limitation of conventional WSNs is their inability to accommodate dynamic network changes, including node failures, energy depletion, or environmental disruptions. These challenges lead to inefficient routing, redundant data retransmissions, and increased energy consumption. Moreover, traditional WSN architectures generally exhibit poor scalability, making it difficult to maintain consistent performance as the network size increases. Single-path routing, commonly used in these systems, further exacerbates the risk of data loss in the event of path failure or congestion. This research addresses the need for a more adaptive and efficient routing mechanism capable of responding to dynamic network conditions while ensuring load balancing and fault tolerance. The primary motivation is to enhance the reliability, energy efficiency, and scalability of WSNs, especially in large-scale deployments where conventional routing techniques fall short. To tackle these challenges, the proposed system integrates Software-Defined Networking (SDN) with WSNs. SDN introduces centralized control and real-time routing adaptation, enabling dynamic load balancing and multipath routing. This integration allows for more flexible and efficient traffic management, improved fault tolerance, and optimal resource utilization. By dynamically adjusting routes and distributing the network load, the system aims to overcome the limitations of traditional WSNs and deliver robust performance across a wide range of applications.
Wireless Sensor Networks (WSNs), Software-Defined Networking (SDN), dynamic load balancing, multipath routing, energy efficiency, fault tolerance, network scalability, centralized control, adaptive routing, traffic management.
SMART MAINTENANCE STRATEGIES FOR ELECTRICAL SYSTEMS USING MACHINE LEARNING
Predictive maintenance (PdM) is a proactive strategy aimed at anticipating equipment failures before they occur, thereby ensuring the reliable operation of electrical systems. By leveraging advanced data analytics and machine learning, PdM analyzes machinery conditions and performance metrics to enable timely and efficient maintenance. This approach is critical for minimizing downtime and reducing maintenance costs in industrial operations. Traditional maintenance strategies are typically either reactive or preventive. Reactive maintenance, or "run-to-failure," involves repairing equipment only after a breakdown, resulting in significant downtime and higher costs. Preventive maintenance, performed at regular intervals regardless of equipment condition, often leads to unnecessary servicing and does not account for actual wear and tear. Both methods fail to fully utilize the vast amount of operational data generated by modern machinery, missing opportunities to optimize maintenance schedules based on real-time equipment conditions. The primary limitation of traditional maintenance strategies lies in their inefficiency and cost. These approaches do not incorporate real-time data to accurately predict maintenance needs, leading to either excessive servicing or unexpected equipment failures. This highlights the need for a more intelligent system that can predict maintenance requirements based on actual equipment conditions. The motivation for this project is to enhance the efficiency and reliability of electrical equipment maintenance. Machine learning-based predictive maintenance can significantly reduce downtime, lower operational costs, and extend equipment lifespan. By using data from various sensors that monitor critical operational parameters, this project aims to predict potential failures and ensure maintenance is conducted only when necessary. The proposed system employs a Decision Tree Classifier to predict maintenance requirements using a comprehensive dataset of operational parameters. The dataset includes features such as RPM, motor power, torque, pressure, airflow, noise levels, temperature, and acceleration, along with categorical labels for components like bearings, water pumps, radiators, exhaust valves, and AC motors.
Predictive Maintenance (PdM), Machine Learning, Decision Tree Classifier, Sensor Data, Electrical Equipment Reliability
ADVANCED PREDICTIVE MAINTENANCE USING CATBOOST CLASSIFIER FOR FAULT DETECTION IN INDUSTRIAL EQUIPMENT
In the realm of industrial automation, predictive maintenance has emerged as a pivotal strategy to ensure operational efficiency, minimize equipment downtime, and reduce maintenance costs. This study presents an advanced predictive maintenance framework aimed at the early detection of mechanical faults in industrial equipment using machine learning techniques. The primary objective is to classify the operational status of machinery as either Normal or Fault based on sensor data and operational features. The process begins with data preprocessing, including missing value treatment, label encoding, and visual analytics such as class distribution plots, heatmaps, and count plots. These steps provided key insights into feature relevance, data imbalance, and potential anomalies, effectively guiding the modelling process. Comparative analysis revealed that while both Ridge and Cat Boost classifiers perform effectively, the Categorical Boosting (Cat Boost) classifier offers distinct advantages in handling complex categorical data, enhancing model interpretability, and improving training efficiency—making it a strong candidate for deployment in real-time industrial environments. The existing system, which employs a Ridge Classifier, demonstrated strong classification capabilities with a confusion matrix indicating 70,376 true positives, 188,119 true negatives, 4,636 false negatives, and 3,033 false positives. Although overall accuracy was high, the number of false negatives raised concerns due to the critical nature of undetected faults in industrial settings. To address this limitation, a proposed system was developed using the Cat Boost Classifier— a gradient boosting algorithm optimized for categorical feature handling and high-performance learning. The Cat Boost model achieved comparable results, with 70,936 true positives, 188,119 true negatives, and identical false negatives (4,636) and false positives (3,033) to the Ridge Classifier, indicating robust predictive power and consistent classification reliability.
Predictive Maintenance, CatBoost Classifier, Industrial Automation, Fault Detection, Sensor Data Analysis.
A MACHINE LEARNING FRAMEWORK FOR REAL-TIME EV BATTERY RUL PREDICTION AND CHARGING OPTIMIZATION
Electric Vehicle (EV) adoption is rapidly accelerating, with global EV sales surpassing 14 million units in 2023, representing a 35% increase over the previous year. Studies indicate that battery performance and energy prediction account for nearly 45% of operational inefficiencies in EVs, while accurate charging strategies can reduce energy waste by up to 30%. Despite this potential, existing systems often rely on manual estimations or simplistic rule-based approaches for battery monitoring, which suffer from inconsistent results due to human error, lack of adaptability to real-time data, and inability to model non-linear battery degradation behaviours. To overcome these limitations, this study proposes a machine learning-based predictive framework using Bagging with Decision Tree Regressor (DTR) to estimate the Remaining Useful Life (RUL) of EV batteries. The methodology begins with collecting high-dimensional EV battery datasets, followed by robust preprocessing including feature scaling, outlier removal, and unique value analysis. Baseline models such as Support Vector Regressor (SVR) and Deep Neural Network (DNN) are first applied for comparative analysis. The proposed ensemble model leverages the Bagging technique, which trains multiple DTRs on different bootstrap samples and averages their predictions to reduce variance and improve accuracy. The system is further integrated into a Django-based web application that handles user interaction, model training, and real-time prediction using uploaded battery data. The result is a scalable, interpretable, and highly accurate prediction tool that can significantly optimize EV charging schedules and battery life cycle management
AI-POWERED IMAGE-BASED POULTRY DISEASE DETECTION SYSTEM
Poultry farming contributes significantly to global food security, yet disease outbreaks continue to pose a major threat to flock health and productivity. Traditional diagnostic methods rely on visual inspection by farm personnel and confirmatory laboratory tests—such as bacterial cultures or PCR assays—which are time-consuming, costly, and often available only in centralized facilities. Consequently, delays in diagnosis can allow pathogens to spread rapidly through large flocks, leading to high morbidity and mortality rates, economic losses, and increased use of antibiotics. The problem is that many farmers lack immediate access to veterinary services or laboratory infrastructure, particularly in rural regions. Early symptoms of diseases like coccidiosis, Newcastle disease, and avian influenza can be subtle and easily overlooked, making manual screening unreliable. Even when signs become overt, transporting samples to a lab and waiting days for results delays intervention, allowing infections to escalate. Existing image-based solutions often require high computational resources or lack flexible, user-friendly interfaces, limiting their adoption among small- to mediumscale producers. There is a clear need for a rapid, accurate, and accessible diagnostic tool that can operate directly on-farm without specialized equipment. Such a system should leverage recent advances in deep learning to automatically recognize disease patterns from digital images, minimizing dependence on expert veterinarians and centralized labs. By integrating pre-trained convolutional neural networks—such as InceptionV3, MobileNetV2, and VGG16—into a unified application, it becomes possible to harness robust feature extractors while maintaining lightweight computational requirements. Embedding this functionality within a Tkinter-based GUI ensures that users with minimal technical expertise can upload images, initiate analysis, and receive near–real-time diagnostic feedback. In summary, this research addresses critical gaps in poultry disease management by developing a transfer learning–based, desktop application tailored to the needs of farmers. By focusing on automated image classification and an intuitive interface, the system aims to reduce diagnostic turnaround time, minimize economic losses, and enhance on-farm decision-making for disease control.
INTELLIGENT FAULT DETECTION IN ELECTRONIC CIRCUITS USING AI FOR INDUSTRY 4.0
Electrical faults like short circuits, open circuits, fouling, and scaling pose significant challenges in India’s power distribution systems, leading to frequent outages and safety hazards. With over 1.4 billion people depending on electricity, power interruptions cost billions annually. The objective is to develop an efficient machine learning-based fault detection system to accurately classify electrical faults Traditional manual fault detection relies on periodic physical inspections by technicians who visually or electrically check equipment for faults. This process includes using handheld meters, visual observation, and trial-and-error testing to identify fault types and locations. It is labor-intensive, slow, and often inaccurate due to human error and environmental conditions, leading to delayed repairs and increased system downtime. The traditional fault detection system suffers from slow response times and limited accuracy due to reliance on manual inspections. Human errors, lack of real-time monitoring, and insufficient data analysis capability cause frequent misdiagnosis of faults, resulting in prolonged outages and higher maintenance costs. The motivation behind this research is to overcome the traditional system’s slow, error-prone fault detection by leveraging machine learning. The proposed system uses a Support Vector Classifier (SVC) to automatically classify electrical faults into categories: 'Short_circuit', 'Nominal', 'Open_circuit', 'Fouling', and 'Scaling'. The machine learning model processes sensor data and identifies subtle patterns distinguishing each fault type with high accuracy. This automation accelerates fault detection and minimizes human error, enabling prompt maintenance actions. Additionally, SVC’s kernel functions allow it to handle complex nonlinear relationships in the data, improving diagnostic reliability. By continuously learning from new data, the system adapts to evolving conditions, offering a scalable and robust solution to enhance power system stability and reduce outage durations
AI-ENABLED MECHANICAL HEALTH MONITORING AND FAILURE FORECASTING SYSTEM
Failure prediction in mechanical components is essential to enhance reliability, safety, and operational efficiency in industrial systems. Traditionally, maintenance strategies have relied on fixed schedules and rule-based systems, which often lead to unexpected breakdowns or unnecessary part replacements. These conventional methods are limited by their inability to adapt to dynamic operating conditions, process real-time data effectively, or detect early signs of failure. As industrial systems grow in complexity and scale, the need for more accurate and intelligent failure prediction methods has become critical. This project addresses the shortcomings of traditional systems by adopting machine learning-based approaches that can learn from historical and real-time data to identify hidden patterns and predict failures before they occur. The significance of this work lies in its potential to enable proactive maintenance strategies, reduce unplanned downtime, extend the lifespan of components, and optimize overall maintenance efforts. By transitioning from reactive to predictive maintenance, industries can achieve greater cost savings and operational resilience. This study aims to explore the application of machine learning techniques in developing efficient, adaptive models for failure prediction, ultimately contributing to smarter and more dependable industrial systems
OPTIMIZING MOVIE RECOMMENDATIONS: A WEIGHTED CLASSIFICATION AND COLLABORATIVE FILTERING APPROACH
The evolution of movie recommendation systems began in the late 1990s alongside the emergence of ecommerce platforms and online streaming services. Early systems primarily utilized collaborative filtering, which based recommendations on user behavior and shared preferences. Traditionally, movie suggestions came from friends, family, critics, or television programs, relying heavily on word-of-mouth, printed reviews, and broadcasted opinions. However, this conventional method lacked personalization and scalability, often resulting in recommendations that did not align with individual preferences. The shift toward machine learning-based recommendation systems was driven by the need for more accurate, scalable, and personalized solutions that could adapt to diverse user tastes. These modern systems analyze vast volumes of data to detect hidden patterns and provide tailored viewing suggestions. The proposed recommendation system incorporates collaborative filtering, content-based filtering, and hybrid models to enhance accuracy and user satisfaction. Collaborative filtering identifies similar user behavior to suggest relevant content, while content-based filtering leverages movie attributes such as genre, cast, and director to match user interests. Hybrid approaches combine both strategies to address limitations and improve overall performance. The system continuously collects and analyzes user interactions, including viewing history, ratings, and search activity, while applying advanced machine learning algorithms such as matrix factorization and deep learning to uncover latent features and evolving patterns. Additionally, it can integrate supplementary data sources like social media engagement and demographic information to further refine recommendations. This comprehensive approach delivers a seamless and personalized user experience, significantly boosting engagement and satisfaction on platforms like Netflix and Amazon Prime.
Movie recommendation system, collaborative filtering, content-based filtering, hybrid model, machine learning, user preferences, personalization, streaming platforms, matrix factorization
FAULT-PROOFING THE GRID: MACHINE LEARNING-BASED ENSEMBLE MODELS FOR TRANSMISSION LINE FAILURE DETECTION
Transmission line failures pose significant risks to the reliability and safety of electrical power systems, often resulting in costly outages and prolonged system downtimes. Early and accurate detection of potential failures is crucial to ensure uninterrupted power delivery and facilitate efficient maintenance planning. In this project, we present a robust classification framework for transmission line failure prediction using ensemble machine learning techniques, with a focus on enhancing predictive accuracy and model robustness. The study begins with comprehensive Exploratory Data Analysis (EDA) to uncover underlying patterns and relationships within the dataset. Using statistical summaries, correlation analysis, and visualization techniques, key features influencing transmission line failures are identified. This analysis is instrumental in refining the dataset and improving model performance. A Decision Tree Classifier is initially employed as the baseline model due to its simplicity and interpretability. However, it demonstrates limitations such as overfitting and poor generalization on complex or noisy data. To address these challenges, we propose a Random Forest Classifier as the improved model. By aggregating predictions from multiple decision trees, the Random Forest significantly enhances classification accuracy, mitigates overfitting, and better captures feature interactions. Experimental results show that the Random Forest Classifier consistently outperforms the Decision Tree across all major performance metrics, confirming its effectiveness in accurately predicting transmission line failures. This project underscores the value of ensemble methods in critical infrastructure applications and demonstrates how data-driven approaches can be leveraged to build intelligent fault detection systems in the power sector.
SMART ENERGY OPTIMIZATION IN URBAN BUILDINGS USING AI AND IOT SENSOR ANALYTICS
Smart homes, equipped with intelligent sensors and automation systems, offer substantial opportunities to optimize energy usage while enhancing occupant comfort. This study explores the influence of external weather conditions on energy consumption patterns in smart residential environments. The dataset spans 4.5 months and includes temperature and humidity readings captured via ZigBee-based wireless sensor networks, alongside energy usage data collected through m-bus energy meters. Supplementing this with meteorological data from Chievres Airport in Belgium allows for a comprehensive analysis of the correlation between environmental factors and household energy behavior. Traditional energy management systems typically operate on static schedules and historical usage patterns, making them ill-equipped to adapt to real-time weather fluctuations, which often results in suboptimal energy efficiency and increased operational costs. To address these challenges, this research proposes a data-driven approach using regression analysis to model the dependency between weather parameters and energy consumption. By leveraging predictive modeling, the system aims to forecast energy demand in response to changing weather conditions, enabling dynamic control of HVAC and other high-consumption appliances. This adaptive strategy enhances energy efficiency, reduces waste, and supports sustainable urban living. The findings from this study contribute to the advancement of intelligent energy management in smart homes, offering scalable solutions for future eco-friendly residential infrastructure.
Smart homes, energy efficiency, weather patterns, IoT sensors, regression analysis, predictive modeling, sustainable energy management
DETECTING DEEPFAKES ON SOCIAL MEDIA USING DEEP LEARNING AND FASTTEXT-BASED TWEET ANALYSIS
Deepfake technology, which utilizes artificial intelligence to create manipulated media, poses a significant threat to the integrity of information on social media platforms. In India, the prevalence of deepfake content has surged, particularly within political and entertainment domains, where AIgenerated videos and fake news have gone viral, leading to widespread misinformation. This research aims to develop a robust AI-based model capable of accurately detecting deepfake content on social media, with a specific focus on identifying machine-generated tweets using FastText embeddings. Traditional detection methods relied on human moderation, fact-checking agencies, and manual filtering of posts using predefined rules and keyword matching. However, these approaches are timeconsuming, error-prone, and lack the scalability needed to manage the vast volume of online content in real time. Consequently, harmful and misleading information can spread rapidly before detection or removal. Given the growing impact of social media in shaping public opinion, this study is motivated by the need to combat misinformation and uphold the credibility of online discourse. Deep learning models offer a promising solution by automating the detection of deepfakes. In this approach, FastText embeddings are used to convert tweets into meaningful word vectors, which are then classified by deep learning models to determine whether they are human- or AI-generated. This method ensures real-time detection, enhanced accuracy, and greater scalability compared to conventional techniques.
Deepfake detection, social media, FastText embeddings, deep learning, machinegenerated tweets, misinformation, AI-generated content.
A STUDY ON THE CONSUMPTION OF OTT PLATFORMS AMONG YOUTH WITH REFERENCE TO LEONAS IT SOLUTIONS PVT LTD
The rapid rise of Over-The-Top (OTT) platforms has transformed the way media content is consumed, particularly among the youth segment. With the availability of affordable data, smartphones, and smart devices, platforms such as Netflix, Amazon Prime Video, Disney+ Hotstar, and regional streaming services have gained significant traction in India. This study focuses on the consumption patterns of OTT platforms among youth with reference to Leonas IT Solutions Pvt. Ltd., a technology-driven company that employs a large number of young professionals who actively engage with digital media. The objective is to analyze their preferences, usage frequency, motivational factors, and the impact of OTT platforms on their lifestyle, entertainment choices, and social interactions. The research highlights the growing inclination of youth towards on-demand content, binge-watching behavior, and the shift from traditional media consumption such as television to digital platforms. The findings are expected to provide valuable insights into how organizations, marketers, and OTT providers can design strategies to cater to the evolving preferences of young audiences
A STUDY ON MARKETING STARTEGIES OF FLIPKART BASED ON ELECTRONIC GOODS
INTERNET became more powerful and basic tools for every person's need and the way people work by integrating various online management tools using internet, innovative companies have set up the payment system for taking customer orders, facilitate making of payments, customer service, collection of marketing data, and online feedback respectively. These activities have collectively known as e-commerce or Internet commerce. Online shopping made so easy for everyone with their product variations and simple way to buy things. An attempt has been made to critically examine various corporate and business level strategies of two big e-tailers and those are Flipkart and Amazon. Comparison have been done considering e-commerce challenges, their business model, funding, revenue generation, growth, survival strategies, Shoppers‟ online shopping experience, value added differentiation, and product offerings. Both these big players made their own mark in India, but who is going to be ultimate winner or be the top one is going to be. A comparative study of Flipkart.com with one of the close competitor Amazon.com delivers the information about the different strategies to succeed in e-commerce market and different opportunities available in India
A Study Of Marketing Strategy Of Hero Moto Corp
The automobile sector in India is highly competitive, with two-wheeler manufacturers facing constant challenges in terms of market share, customer loyalty, and innovation. Hero MotoCorp, one of the largest two-wheeler manufacturers in the world, has maintained its market leadership through effective marketing strategies, product innovation, and strong distribution networks. This study aims to analyze the marketing strategies of Hero MotoCorp with a focus on product positioning, pricing, promotional activities, and customer relationship management. The research also explores how the company adapts to changing consumer preferences, rising fuel costs, and competition from both domestic and global players. Findings suggest that Hero MotoCorp’s success lies in its ability to adopt a customer-centric approach, wide product portfolio catering to different income segments, aggressive rural penetration, and impactful promotional campaigns such as celebrity endorsements and sports sponsorships. Furthermore, the company’s emphasis on affordability, fuel efficiency, and after-sales service plays a significant role in retaining customer loyalty. However, the study also highlights challenges such as the need for electric vehicle innovation, global expansion, and sustaining leadership amidst disruptive technologies. This study concludes that Hero MotoCorp’s marketing strategy is a blend of traditional and modern techniques that focus on value creation, affordability, and strong brand positioning. The insights gained from this study can be useful for policymakers, marketers, and other automobile firms aiming to understand competitive strategies in the two-wheeler industry.
A STUDY ON COMPETENCY MAPPING Tech Mahindra
People are an organization's most important and distinctive resource in the competitive world of today. Every individual possesses unique resources that cannot be duplicated .Consequently, companies must use this knowledge to gain a competitive edge. Through competence mapping, the company can learn more about a worker's abilities, character, and general performance. This method aims to continuously assess performance on both an individual and group level. It is employed to ascertain the essential characteristics (knowledge, abilities, and attitudes) needed to carry out organizational tasks successfully. The categories, capabilitiesbuilding strategies, and goals of competency mapping are examined in this study
competency, proficiency, and mapping of proficiency
Performance appraisal is a systematic process of evaluating employee performance against predetermined standards to assess their contribution toward organizational goals. It serves as a key tool in human resource management by identifying strengths, weaknesses, and potential areas of development for employees. The appraisal process not only facilitates fair evaluation but also provides a basis for promotions, rewards, training, and career planning. Effective performance appraisal enhances employee motivation, accountability, and productivity, thereby aligning individual performance with organizational success. However, challenges such as bias, lack of transparency, and inconsistent evaluation methods often limit its effectiveness. Thus, adopting modern appraisal techniques such as 360-degree feedback, continuous performance monitoring, and data-driven evaluation methods can lead to more accurate and constructive outcomes.
Career Planning And Development- RELIANCE LIFE INSURANCE
HR practices are the means through which human resources personnel can develop the leadership of staff. This occurs through the practice of developing extensive training and motivational programs, such as devising systems to direct and assist management in performing on going performance appraisals. Talent management touches on all key HR areas, from hiring to employee on-boarding and from performance management to retention. The purpose is to increase performance. It also aims to motivate, engage, and return employees to make them perform better. Organizations are made up of creating value through business activities. An organization must make sure that it has a continuous and integrated process for recruiting, training, managing, and compensating these people. This means that the talent management is aimed at improving the performance of the business. Productive and engaged employees makes a business profitable. Good talent management is, therefore the ultimate HR practice that helps a company to win in the marketplace. A talent management strategy is the plan of action to optimize the employee performance. The basic assumption here is the employees who have all the resources to do their job well, perform better. This is backed up by the research
EFFECTIVENESS OF TRAINING AND DEVELOPMENT
Training and development are critical functions within organizations, aimed at enhancing employee skills, knowledge, and overall performance. In today’s competitive business environment, organizations must invest in continuous learning programs to maintain productivity, adaptability, and innovation. This study examines the effectiveness of training and development initiatives in improving employee performance, job satisfaction, and organizational growth. It evaluates various training methods, including on-the-job training, workshops, e-learning, and mentoring programs, to identify their impact on skill enhancement and career progression. The research also explores the relationship between employee engagement, motivation, and the outcomes of training interventions. Using both qualitative and quantitative approaches, data was collected from employees across different sectors to assess the practical applicability of training programs. The findings indicate that well-structured training and development not only improve individual competencies but also contribute significantly to organizational efficiency and competitive advantage. Furthermore, the study highlights challenges such as training relevance, resource allocation, and employee participation, providing insights into optimizing training strategies for maximum effectiveness.
STUDY ON E-RECRUITMENT - EDLOGIX SOFTWARE SOLUTIONS PVT. LTD
The rapid digitization of HR processes has positioned e-recruitment as a strategic lever for organizations seeking speed, scale, and quality in talent acquisition. This study examines the design, adoption, and effectiveness of e-recruitment practices at Edlogix Software Solutions Pvt. Ltd., a mid-sized IT services firm operating in a highly competitive labor market. The research investigates how Edlogix integrates job portals, professional networks (e.g., LinkedIn), the company careers site, applicant tracking system (ATS) workflows, programmatic job advertising, employee referrals, and campus/hackathon pipelines into a unified hiring funnel. Core objectives include evaluating (i) sourcing channel performance (reach, cost, and candidate quality), (ii) funnel efficiency from application to offer (conversion and time-to-fill), (iii) candidate experience (communication, transparency, responsiveness), and (iv) compliance, data privacy, and diversity outcomes within digital hiring.A mixedmethods approach is employed: quantitative analysis of 12–18 months of ATS data (applications, screening outcomes, interview stages, offers, joins, and drop-offs) and cost records (cost-per-click, cost-per-applicant, cost-per-hire) is combined with qualitative insights from semi-structured interviews with recruiters, hiring managers, and recent hires. Key performance indicators (KPIs) assessed include time-to-hire, time-to-accept, cost-per-hire, source-of-hire mix, quality-of-hire proxies (early performance/retention within 6 months), candidate Net Promoter Score (cNPS), and offer-to-join ratio. Preliminary evidence suggests that e-recruitment has reduced average time-to-hire and sourcing costs while expanding reach to passive candidates; however, challenges persist around resume overload, automated screening precision, interview scheduling latency, and offer drop-offs in hot skill categories. The study also identifies friction points in candidate experience related to fragmented communications and limited status visibility, and highlights the need for stronger employer branding content on the careers site and job descriptions tailored to skills rather than titles.The paper proposes a practical roadmap for Edlogix: refine job ads via skills taxonomies and SEO, implement structured screening rubrics inside the ATS, introduce asynchronous assessments for early funnel filtering, automate calendaring, use real-time dashboards for funnel health, and deploy targeted nurturing journeys to reduce offer declines. Further, it recommends integrating DEI analytics, ensuring GDPR/DPDP Act–aligned consent flows, and instituting feedback loops (cNPS surveys, hiring manager scorecards) for continuous improvement. Overall, the study concludes that a data-driven, candidate-centric e-recruitment model can deliver superior hiring velocity, lower costs, and improved quality-of-hire for Edlogix, strengthening its talent advantage in the software sector.
AN EXAMINATION OF IT WORKERS' STRESS MANAGEMENT TECHNIQUES
The Information Technology (IT) sector is recognized as one of the most dynamic, fast-paced, and demanding work environments in the modern economy. IT professionals often face long working hours, project deadlines, high client expectations, rapid technological changes, and continuous learning requirements, all of which contribute to elevated levels of occupational stress. Prolonged stress can negatively impact not only employee health and well-being but also job performance, creativity, and organizational productivity. This study examines the various stress management techniques adopted by IT workers, with a focus on both organizational strategies and individual coping mechanisms.The research explores common stressors in the IT sector, including work overload, job insecurity, role ambiguity, lack of work-life balance, and continuous connectivity due to digital communication. Findings from previous studies highlight that IT workers employ a variety of techniques to mitigate stress, such as time management, relaxation exercises, physical activity, mindfulness practices, social support, and professional counseling. In addition, organizations are increasingly implementing structured interventions such as flexible working hours, employee wellness programs, stress management workshops, team-building activities, and supportive leadership practices to reduce burnout and enhance resilience.The study also emphasizes the role of digital solutions in stress management, such as the use of meditation apps, AI-driven wellness tools, and online counseling platforms, which are particularly relevant in a post-pandemic, hybrid-working environment. Moreover, cultural and individual differences play a key role, as coping mechanisms vary depending on personality traits, organizational culture, and personal resilience.Overall, the examination reveals that effective stress management is multi-dimensional, requiring both individual-level practices (self-care, mindfulness, exercise, skill enhancement) and organizational-level interventions (supportive work culture, workload management, flexible policies). The study concludes that a holistic approach that combines personal coping strategies with institutional support mechanisms is essential to sustain the health, productivity, and job satisfaction of IT professionals in today’s competitive and rapidly evolving digital economy.
A Enrichment and Employee Performance, Motivation At HCL Tech
Job enrichment can be defined as the goal of achieving performance of an individual. It’s very important to first understand the importance of job enrichment as a significant tool to manage as well as nurture the performance of employees who are successful. The enriched job comprises of components such as significance of task, the identification of tasks and variety of skills that help in improving the performance of the employees and also boosts their energy for performing vigorously. In this study we will try to focus our energy on the relationship of job enrichment as well as the performance of an individual.The impact of motivation on performance is an area of research and a number of discussions have been done on the same. This study will identify the role of motivation on the employee performance. The independent variables which are used for assessing motivation include development as well as training, recognition, reward, delegation of authorities, etc. and performance of the employee is the dependent variable.
Job enrichment, employee performance, employee motivation, employee productivity, Critical Review
A STUDY AND ANALYSIS OF JOB EVALUATION PRATICES
The arising time sees an example shift in the situation of the human material in some institution. Hardware and program stretch to form main part of some arrangement, either it belongs to the new frugality or the traditional individual. However, the arrangement of the new saving likewise involves (other than fittings and operating system) the news plan and the skin good namely the human. This complete reversal in the position of HR be able the idea of JOB EVALUATION yet necessary, while JOB EVALUATION itself is a comparably traditional idea. Its main has happened considerably reinforced all along ancient times ten of something. Human capability administration (HRM) is a comparably new label for the range of ideas and practices complicated in directing community. It is outlined and detailed in a type of (consistently antagonistic) habits. This excerpt from Human Resource Management in a Business Context presents the idea of human source administration. It outlines HRM as a principles of nation administration and determines a foundation for allure duty inside implausible story framework. People administration- Human system administration has not 'make public of nowhere' HRM has fascinated plans and methods from any of extents. In effect, it is a combination of ideas and ideas tense from over a of one hundred years of administration belief and commerce research. KEYWORDS: Job judgment, Satisfaction