Air pollution has become a major environmental concern affecting human health and climate worldwide. To address this issue, this paper proposes an Air Pollution Monitoring and Prediction System based on the Internet of Things (IoT). The system employs smart sensors to measure key air quality parameters such as CO?, CO, NO?, SO?, and particulate matter (PM2.5 and PM10) in real time. The collected data is transmitted to a cloud platform via wireless communication modules for storage, visualization, and analysis. Machine learning algorithms are then applied to predict future pollution levels based on historical and environmental data trends. The system provides real-time alerts and air quality index (AQI) updates through web and mobile interfaces, enabling authorities and citizens to take timely actions. By integrating IoT with predictive analytics, the proposed framework offers a scalable, low-cost, and efficient solution for continuous environmental monitoring and sustainable urban management.

Keywords : Internet of Things (IoT), Air Pollution Monitoring, Air Quality Index (AQI), Machine Learning, Environmental Prediction, Smart Cities

Authors : Bosubabu Sambana

Title : Air Pollution Monitoring and Prediction System using Internet of Things

Volume/Issue : 2020;2(3 (May - June))

Page No : 50 - 56

The rapid growth of wireless communication has led to an increased demand for radio spectrum, resulting in inefficient spectrum utilization and congestion in conventional networks. Cognitive Radio Networks (CRNs) offer a promising solution by enabling dynamic and intelligent spectrum access through spectrum sensing, learning, and adaptation. This paper focuses on intelligent spectrum allocation in CRNs using machine learning and optimization techniques to enhance spectral efficiency and reduce interference. The proposed system enables secondary users to opportunistically utilize underused frequency bands without disrupting primary users. Algorithms such as reinforcement learning and fuzzy logic are applied to predict spectrum availability and allocate channels dynamically based on real-time network conditions. Simulation results show that intelligent allocation mechanisms improve throughput, minimize latency, and ensure fair spectrum sharing among users. This study contributes to developing adaptive, self-learning communication systems capable of addressing spectrum scarcity in next-generation wireless networks.

Keywords : Cognitive Radio Networks, Spectrum Allocation, Machine Learning, Dynamic Spectrum Access, Wireless Communication, Spectrum Efficiency

Authors : Pudi Ganesh

Title : Intelligent Spectrum Allocation in Cognitive Radio Networks

Volume/Issue : 2020;2(3 (May - June))

Page No : 46 - 49

Accurate classification of cancer data plays a vital role in early diagnosis, effective treatment planning, and patient survival prediction. This paper presents a comparative analysis of various machine learning classification algorithms applied to cancer datasets. The study evaluates algorithms such as Decision Tree, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), and Naïve Bayes to determine their performance in identifying cancer types or malignancy levels. Data preprocessing techniques, including normalization and feature selection, are applied to improve model accuracy and reduce noise. The models are trained and tested using benchmark cancer datasets, such as the Wisconsin Breast Cancer Dataset (WBCD), to ensure reliability. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the effectiveness of each algorithm. Experimental results reveal that ensemble-based and kernel-based models achieve higher predictive performance compared to simple classifiers. This study demonstrates the importance of selecting an appropriate classification algorithm for cancer data analysis and supports the integration of machine learning in medical diagnosis systems.

Keywords : Cancer Data, Classification Algorithms, Machine Learning, Data Analysis, Predictive Modeling, Medical Diagnosis

Authors : T. Chalapathi Rao

Title : Comparision of Classification Algorithms using Cancer Data

Volume/Issue : 2020;2(3 (May - June))

Page No : 41 - 45

Phishing is one of the most prevalent cyber threats, where attackers deceive users through fraudulent websites or URLs to steal sensitive information such as passwords and financial data. This paper presents a Machine Learning (ML)-based approach for phishing URL detection to enhance cybersecurity and protect users from online scams. The proposed system extracts various lexical, host-based, and content-based features from URLs, including domain length, presence of special characters, HTTPS usage, and URL age. These features are analyzed using supervised learning algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression to classify URLs as legitimate or phishing. Data preprocessing and feature selection are performed to improve model accuracy and reduce computational complexity. Experimental results show that ensemble and hybrid ML models outperform traditional blacklist-based methods in detection accuracy and adaptability. The system can be integrated into browsers or email filters to provide real-time protection against phishing attacks.

Keywords : Phishing Detection, Machine Learning, URL Classification, Cybersecurity, Feature Extraction, Online Threat Prevention

Authors : YEGIREDDI RAMESH

Title : Phishing URL Detection using Machine Learning

Volume/Issue : 2020;2(3 (May - June))

Page No : 32 - 40

Driver fatigue and drowsiness are major causes of road accidents worldwide, emphasizing the need for an efficient and reliable detection system. This paper presents a Drowsiness Detection System based on Convolutional Neural Networks (CNNs), designed to monitor driver alertness in real time. The proposed system captures facial images using a camera and analyzes visual features such as eye closure, yawning frequency, and head position. A CNN-based model is trained to classify the driver’s state as active or drowsy by learning spatial and temporal patterns from the image dataset. The system generates alerts through visual or audio notifications when signs of fatigue are detected, helping to prevent potential accidents. Experimental evaluation demonstrates high accuracy and robustness of the proposed model under varying lighting and environmental conditions. The integration of deep learning with computer vision provides a cost-effective and automated solution for enhancing road safety and driver assistance technologies.

Keywords : Drowsiness Detection, Convolutional Neural Networks (CNN), Driver Safety, Computer Vision, Fatigue Monitoring, Deep Learning

Authors : YEGIREDDI RAMESH

Title : Drowsiness Detecting System using Convolutional Neural Networks

Volume/Issue : 2020;2(3 (May - June))

Page No : 28 - 31

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes that operate collaboratively to monitor and transmit environmental or structural data. However, due to their limited power supply and computational capacity, energy efficiency remains one of the most critical challenges in WSN design. This paper presents the development of a power-efficient mechanism aimed at extending the network lifetime while maintaining reliable data transmission. The proposed approach focuses on optimizing energy consumption through techniques such as efficient clustering, adaptive routing, and dynamic duty cycling. By intelligently selecting cluster heads and minimizing redundant data transmission, the system significantly reduces overall energy usage. Simulation results demonstrate that the proposed mechanism achieves improved energy balance among nodes, lower packet loss, and enhanced network stability compared to conventional protocols. This study provides an effective framework for building sustainable WSNs suitable for various applications, including environmental monitoring, industrial automation, and smart city systems.

Keywords : Wireless Sensor Network (WSN), Energy Efficiency, Clustering, Routing Protocols, Network Lifetime, Power Optimization

Authors : Satish Dekka

Title : Development of Power Efficient Mechanism for Wireless Sensor Network

Volume/Issue : 2020;2(3 (May - June))

Page No : 22 - 27

Academic integrity is a critical aspect of modern education, and detecting unethical practices such as cribbing or plagiarism has become a major challenge in digital learning environments. This paper presents a study on the cognizance (awareness and detection) of cribbing using data excavation techniques, also known as data mining. The proposed approach employs text mining, pattern recognition, and similarity analysis to identify instances of copied or suspicious content in academic submissions. By analyzing large datasets of student records, assignments, and examination scripts, the system extracts relevant features and applies algorithms such as cosine similarity, clustering, and classification to detect content overlap and behavioral patterns. The use of data excavation enables automated, efficient, and accurate cribbing detection, reducing manual efforts and enhancing academic transparency. Furthermore, the system can generate analytical reports to help educators understand trends in academic misconduct and implement preventive strategies. The proposed framework contributes to maintaining fairness and integrity in educational evaluation systems.

Keywords : Data Excavation, Cribbing Detection, Academic Integrity, Text Mining, Plagiarism Analysis, Pattern Recognition

Authors : S. Phani Kumar

Title : Cognizance of Cribbing using Data Excavation

Volume/Issue : 2020;2(3 (May - June))

Page No : 18 - 21

The stock market is a dynamic and complex system influenced by numerous economic, social, and political factors. Accurate prediction and analysis of stock market trends have become essential for investors and financial institutions to make informed decisions. This paper presents a study on the erudition (learning) and contemplation (analysis) of stock market services using data mining techniques. The proposed approach utilizes historical market data, company performance metrics, and investor sentiment to uncover hidden patterns and correlations. Data mining algorithms such as regression analysis, clustering, and classification are applied to predict price movements and assess market behavior. The integration of data preprocessing, feature extraction, and model evaluation enhances the accuracy and reliability of predictions. The system can assist traders in identifying profitable investment opportunities and risk factors. The study demonstrates that combining data mining with analytical models provides valuable insights into market fluctuations, supporting intelligent decision-making in financial services.

Keywords : Data Mining, Stock Market Analysis, Prediction Models, Financial Analytics, Pattern Recognition, Investment Decision-Making

Authors : Srinadh Unnava

Title : Erudition and Contemplation of Service for Stock Market using Data Mining

Volume/Issue : 2020;2(3 (May - June))

Page No : 15 - 17

Rapid urbanization and population growth have led to severe traffic congestion, delays, and increased pollution in modern cities. To address these challenges, this paper proposes an Intelligent Traffic Management System (ITMS) based on the Internet of Things (IoT). The system integrates smart sensors, cameras, and wireless communication technologies to monitor real-time traffic flow, vehicle density, and signal status across intersections. The collected data is transmitted to a centralized cloud platform for processing and analysis. Using this information, the system dynamically controls traffic signals to optimize vehicle movement, reduce waiting time, and minimize fuel consumption. Additionally, IoT-enabled alert mechanisms provide early warnings about accidents, roadblocks, or emergency vehicle movement to ensure smoother traffic operations. The proposed system enhances road safety, improves transportation efficiency, and contributes to the development of sustainable smart cities. Experimental simulations demonstrate that IoT-based traffic control significantly outperforms traditional fixed-timer systems in managing congestion and reducing travel time.

Keywords : Internet of Things (IoT), Intelligent Traffic Management, Smart Cities, Real-Time Monitoring, Traffic Optimization, Cloud Computing

Authors : A V N CHANDRA SEKHAR

Title : Intelligent Traffic Management System using Internet of Things

Volume/Issue : 2020;2(3 (May - June))

Page No : 7 - 13

Railway transportation plays a vital role in public and goods movement, but accidents caused by track faults remain a major safety concern. This paper presents an Internet of Things (IoT)-based Railway Track Fault Detection System designed to monitor and identify track irregularities in real time. The proposed system uses smart sensors such as vibration, ultrasonic, and temperature sensors to detect cracks, misalignments, or other structural defects on the railway track. The collected data is transmitted through wireless communication modules to a cloud-based platform for continuous monitoring and analysis. When a fault is detected, the system immediately alerts maintenance authorities via mobile or web notifications, enabling timely inspection and repair. The integration of IoT technology ensures high accuracy, low maintenance, and scalability, reducing manual inspection efforts and minimizing the risk of accidents. Experimental results demonstrate that the proposed system provides an efficient, cost-effective, and reliable solution for enhancing railway safety and operational efficiency.

Keywords : Internet of Things (IoT), Railway Safety, Fault Detection, Smart Sensors, Real-Time Monitoring, Predictive Maintenance

Authors : A V N CHANDRA SEKHAR

Title : Railway Track Fault Detection System by using Internet of Things

Volume/Issue : 2020;2(3 (May - June))

Page No : 1 - 6