bluuebunny/crossref_metadata_2025 huggingface dataset:
College Management System
Authors:   Final Year CSE, Department of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India, Raj Amartya , Roy Kundan ,   Final Year CSE, Department of CSE Sengunthar Engineering College (Autonomous),  Tiruchengode, India, Kumar Nitish ,   Final Year CSE, Department of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India, M. Dr.Sakthivel ,   Professor & Head (CSE),  Sengunthar Engineering College (Autonomous), Tiruchengode, India
Published: April 2024

This paper is aimed at developing an Online Intranet College Management System (CMS) that is of importance to either an educational institution or a college. The system (CMS) is an Intranet based application that can be accessed throughout the institution or a specified department. This system may be used for monitoring attendance for the college. Students as well as staffs logging in may also access or can be search any of the information regarding college. Attendance of the staff and students as well as marks of the students will be updated by staff. This system (C.M.S) is being developed for an engineering college to maintain and facilitate easy access to information. For this the users must be registered with the system after which they can access as well as modify data as per the permissions given to them. CMS is an intranet-based application that aims at providing information to all the levels of management within an organization. This system can be used as a knowledge/information management system for the college. For a given student/staff (technical/non-technical) can access the system to either upload or download some information from the database.
Analyzing the States of Brain under Anesthesia Using ANES-metanet based on Deep Learning Models
Authors:   Department of Computer Science and Engineering,  Sengunthar Engineering College (Autonomous), Tirenchengode, India, M. Kalaivani , M. Dr.Sakthivel ,   Professor and Head (CSE), Sengunthar Engineering College (Autonomous), Tirenchengode, India
Published: April 2024

Monitoring the depth of unconsciousness during anesthesia is beneﬁcial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time .Continuous monitoring of electroencephalogram (EEG) recordings in humans under general anesthesia (GA), local anesthesia (LA) and regional anesthesia (RA) has demonstrated that changes in EEG dynamics induced by an anesthetic drug are reliably associated with the altered arousal states caused by the drug. This observation suggests that an intelligent, closed-loop anesthesia delivery (CLAD) system operating in real-time could track EEG dynamics and control the infusion rate of a programmable pump to precisely maintain unconsciousness. In this process we propose a deep learning algorithm to analysis the anesthesia from EEG signal. So the deep learning algorithms are Convolution Neural Network, Long Short Term Neural Network and Artificial Neural Network will applied and analyze EEG signal and it generates the results in the form of metrics like accuracy, precision, recall and f1-score.
Finger Print Voting System
Authors:   UG Student, Dept of CSE Sengunthar Engineering College (Autonomous), Tamil Nadu, India, Kumar Govind , Anam Mozakker ,   UG Student, Dept of CSE Sengunthar Engineering College (Autonomous), Tamil Nadu, India,, Sajid Md ,   UG Student, Dept of CSE Sengunthar Engineering College (Autonomous), Tamil Nadu, India,, M. Dr.Sakthivel ,   Professor & Head, Dept of CSE Sengunthar Engineering College (Autonomous), Tamil Nadu, India
Published: April 2024

The field of biometrics, or the identification of people based on physiological or behavioral characteristics [6], has recently received a significant amount of attention in current research as a result of its applicability in the fields of information technology and security. With the increased usage of PIN numbers and passwords in everyday life, the vulnerabilities of these two technologies are becoming more apparent (e.g. password-cracking). Biometric authorization’s strengths are that it requires the user to be present and that it eliminates the hassles of passwords and PIN’s (or can be used in parallel with these for added security). Specifically, online identity verification, ATM machines, and building entrance authorization, are all areas where it is very useful to implement an automatic identification system.
Big Data Visualisation of Heart Disease Prediction Using High Level Ada Boosting Algorithm and Gradient Boosting Algorithm
Authors:   PG Scholar, Department of CSE, Sengunthar Engineering College (Autonomous),  Tiruchengode, Tamil Nadu, India, D Nivedhidha , B. Dr.Sujatha ,   Dean Academics,  Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, India
Published: April 2024

Machine learning and artificial intelligence have been found useful in various disciplines during the course of their development, especially in the enormous increasing data in recent years. It can be more reliable for making better and faster decisions for disease predictions. So, machine learning algorithms are increasingly finding their application to predict various diseases. Constructing a model can also help us visualize and analyze diseases to improve reporting consistency and accuracy. This article has investigated how to detect heart disease by applying various machine learning algorithms. The study in this article has shown a two-step process. The heart disease dataset is first prepared into a required format for running through machine learning algorithms. Medical records and other information about patients are gathered from the UCI repository. The heart disease dataset is then used to determine whether or not the patients have heart disease. Secondly, Many valuable results are shown in this article. The accuracy rate of the machine learning algorithms, such as Logistic Regression, Support vector machine, K-Nearest-Neighbours, Adaboost, and Gradient Boosting Classifier, are validated through the confusion matrix. Current findings suggest that the Logistic Regression algorithm gives a high accuracy rate of 95% compared to other algorithms. It also shows high accuracy for f 1-score, recall, and precision than the other four different algorithms. However, increasing the accuracy rates to approximately 97% to 100% of the machine learning algorithms is the future study and challenging part of this research.
Emergency Fuel Ordering and Management System for Vehicles
Authors:   Final Year CSE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, India, Varma Harish , R Chanthuru ,   Final Year CSE,  Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, India, J Aravindh ,   Final Year CSE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, India, M. Dr.Sakthivel ,   Professor, Dept of CSE,  Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, India
Published: April 2024

In today’s fast paced world, monitoring systems are necessary to track the changes in the environment for better understanding of current scenarios and predictions thereof. The same is true for fuel tanks in vehicles as well. By keeping strict track of fuel intake and consumption, vehicles can be made more fuel and cost efficient. This can be done using remote monitoring and data collection systems deployed at the site of the fuel storage tank. When an agent starts filling petrol in your bike/car, the flow sensor is activated. Capacitance is used in the suggested IoT gadget to measure the amount of fuel. The driver receives a notification through mobile application when the vehicle's Fuel reaches a low level.
Secure Date Storage Using ECC Encryption with Verifiable Data Sharing in Cloud
Authors:   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India, S Aswin , V Chandru ,   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India, Raj Mathan ,   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India, Kumar K.Ashok ,   Asst. Professor, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, India
Published: April 2024

Cloud data storage has become increasingly popular, but the security and privacy of data stored in the cloud remain major concerns. Cloud computing is the most feasible approach for implementing Digital Twin services since it has prodigious advantages. It provides on demand services, computing resources, ubiquitous network access, etc., making it suitable for the next-generation information technology architecture. In cloud-assisted data storage environments, the data owners generate data from physical assets and disseminate it to the cloud server.
Secure DNA Data Storage with Similarity Search in Cloud Environments
Authors:   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous) Tiruchengode,Tamil Nadu,INDIA, E Arunkumar , S Dhivanesh ,   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous) Tiruchengode,Tamil Nadu,INDIA, S Manjokumar ,   Final Year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous) Tiruchengode,Tamil Nadu,INDIA, G. Dr.Jayamurugan ,   Associate Professor, Dept. of CSE Sengunthar Engineering College (Autonomous) Tiruchengode,Tamil Nadu,INDIA
Published: April 2024

DNA similarity search has been widely applied in human genomic studies including DNA marking, genomic sequencing and genetic disease prediction. Meanwhile, with the explosive growth of data, users are increasingly inclining to store DNA data on the cloud for saving local cost. However, the high sensitivity of DNA data has forced the government to strictly control its acquisition and utilization. One potential solution is to encrypt DNA data before outsourcing them to the cloud. Nevertheless, private DNA similarity query has been an active research issue, state-of-the-art results are still defective in security, functionality and efficiency. In this paper, we propose EFSS, an Efficient and Fine-grained Similarity Search scheme over encrypted DNA data. In specific, firstly, we design an approximation algorithm to efficiently calculate the edit distances between two sequences. Secondly, we put forward a novel Boolean search strategy to achieve complicated logic queries such as mixed “AND” and “NO” operations on genes. Thirdly, data access control is also supported in our EFSS through a variant of polynomial based design. Moreover, the K-means clustering algorithm is exploited to further improve the efficiency of execution. In the end, security analysis and extensive experiments demonstrate the high performance of EFSS compared with existing schemes.
Smart Ambulance Service and Tracking Applications for Critical Needs
Authors:   Final year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, E Harini , Priya.V Mohana ,   Final year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, V Sathyapriya ,   Final year CSE, Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, G. Dr.Jayamurugan ,   Associate Professor/CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

This abstract introduces a Smart Ambulance Service (SAS) along with its accompanying service application designed to address critical needs in emergency medical care. The SAS incorporates cutting-edge technologies such as Internet of Things (IoT) and real-time data analytics to enhance the efficiency and effectiveness of emergency response systems. The SAS aims to revolutionize traditional ambulance services by leveraging IoT devices installed within ambulances. These devices collect vital signs of patients en route to the hospital, such as heart rate, blood pressure, and oxygen levels, and transmit this data in real-time to healthcare providers. This real-time data transmission allows medical personnel to prepare for the patients arrival, making the transition from ambulance to hospital smoother and reducing delays in treatment. Furthermore, the service application is user-friendly interface accessible to both emergency responders and hospital staff. It provides a centralized platform for managing ambulance dispatch, tracking ambulance locations and monitoring patient conditions remotely. Emergency responders can input patient information, vital signs, and the nature of the emergency directly into the application, ensuring that hospital staffs are well- informed before the ambulance arrives.
Duplicate Detection on Cloud Data with Re-Encryption Based Data Sharing
Authors:   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu,INDIA, Bommannan Sri , P Prabakaran ,   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous),  Tiruchengode, Tamil Nadu,INDIA, S Gokulraj ,   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu,INDIA, S Ilavarasi ,   Assistant Professor/ CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu,INDIA
Published: April 2024

Cloud data storage has gained immense popularity in recent years due to its scalability and accessibility. However, the efficient management of data in the cloud, especially with the ever-increasing volume of data, remains a significant challenge. Data deduplication is a crucial technique to reduce storage costs and enhance data management efficiency. It also concerns the secure cloud data storage and sharing. Here implement a novel approach to de duplication-based cloud data storage using chunk-based partitioning. In this approach, data is divided into fixed-size chunks, and a deduplication mechanism is applied to identify and eliminate redundant chunks. The process of duplicate detection leading to improved storage efficiency and reduced data redundancy. It not only conserves storage space but also enhances data retrieval performance by reducing the volume of data to be transferred over the network. Proposed system also enhance the security of cloud data storage by employing Base64 encoding for data representation and implementing re-encryption techniques based on the Advanced Encryption Standard (AES) for secure data sharing. The Base64 encoding methodology is employed to transform binary data into a text format, rendering it less susceptible to unintended manipulations during data transmission and storage. This encoding mechanism ensures data integrity and facilitates its secure storage in cloud environments. By utilizing AES encryption, data shared among users is re-encrypted with unique keys, ensuring that only authorized recipients can access and decrypt the shared information. The proposed approach significantly enhances data confidentiality and mitigates risks associated with unauthorized access, there by fortifying the security of cloud-based data sharing.
Find Missing Person Using Artificial Intelligence
Authors:   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, M R Arunvasu , k Ajay ,   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, C Kavin ,   Final year, CSE / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, . S.Ilavarasi ,   Assistant Professor / Dept. of CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

Face recognition is a biometric grounded technology that mathematically charts particular person’s or individual’s facial features and stores all that data as a face print. By using this fashion, the information of the face of a person is saved mathematically or in the format of graphs in the database, which is used for detecting that particular face. Face recognition model in our system will find a match of that person in the database. If a match is found, it will be notified to the police and the guardian of that person. The face recognition model in our system will try to find a match in the database with the help of Tensor Flow Face recognition algorithm. It is performed by comparing the face encodings of the uploaded image to the face encodings of the images in the database. If a match is found, it will be notified to the police and the people related to that person along with the position of where the person is found. Face recognition models in Deep and Machine Learning are primarily created to ensure the security of identity. There are several frameworks used in building a face recognition model and one of them is Tensor Flow. The Tensor Flow face recognition model has so far proven to be popular. Using Tensor Flow to build face recognition and discovery models might bear trouble, but it is worth it in the end. As mentioned, Tensor Flow is the most used Deep Literacy framework and it has pre-trained models that fluently help with image bracket.
Question Paper Generator
Authors:   Final Year CSE, Dept of CSE  Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, Y M Shriyaa , K Sowmiya ,   Final Year CSE, Dept of CSE  Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, K Thamilarasi ,   Final Year CSE, Dept of CSE  Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, P Sangeetha ,   Assistant Professor/CSE Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

The automatic question paper generator (AQPG) is a software tool designed to streamline and automate the process of generating examination question papers. It leverages algorithms and databases to create balanced, fair, and diverse sets of questions based on predefined parameters such as topic, difficulty level, and question type. By incorporating machine learning techniques, natural language processing, and data mining, AQPG can analyze vast amounts of educational content to ensure the integrity and quality of the generated questions. This abstract provides an overview of the AQPG system, highlighting its significance in reducing manual effort, enhancing efficiency, and maintaining the integrity of assessment practices in educational institutions.
The Efficient Employee Attendance Tracking and Payroll Calculation with Geo-Fencing Technology
Authors:   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, V Gunasivam , J Kiran ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, velan Senkathir ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, S Hemalatha ,   Assistant Professor, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

The Efficient Employee Attendance Tracking and Payroll Calculation with Geo-fencing Technology project aims to simplify the process of managing employee attendance and payroll using advanced technology. The proposed system utilizes the latest geo-fencing technology, Google Map API or JS geolocation API for location tracking, and PHP and MYSQL for backend development. The system offers separate dashboards for the admin and the employee, each with unique features and functionalities.
E-Commerce Mobile Applications for Visually Impaired Persons
Authors:   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, S Akalya , K Jeevajothi ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, P D Swetha ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, D Usharani ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, S. Hemalatha ,   Assistant Professor, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

A Mobile app design that allows visually impaired and blind users to access and shop without any assistance. The system gives voice instruction to users and an input is requested from the user. The Voice Command-Based Product Information System for Blind People is an innovative solution designed to empower visually impaired individuals with easy access to product information.
YouTube Spam Comment Detection
Authors:   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, C.B Dharun , Nouman MD ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, K. Surendiran ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, Maheshraj Perumala ,   Final Year CSE, Dept of CSE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

This project presents a successful implementation of an advanced YouTube spam comment detection system, significantly reducing spam across the platform. Utilizing NLP, machine learning, and community reporting, the system offers a dynamic approach to content moderation. The report delves into ethical considerations, highlighting the complexity of balancing spam combat with user freedom. Serving as a notable case study, it provides a valuable blueprint for addressing spam-related challenges on expanding social media platforms, enhancing the overall user experience.
Peer-to-Peer Network Node Distribution Analysis Using Block chain
Authors:   Final Year / Department of Information Technology, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, S Archana , Mary Princy ,   Final Year / Department of Information Technology, Sengunthar Engineering College (Autonomous),  Tiruchengode, Tamil Nadu, INDIA, S Sangami ,   Final Year / Department of Information Technology, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, V Mahalakshmi ,   Final Year / Department of Information Technology, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

This project uses block chain technology to propose a decentralized access control method for anonymous authentication and secure cloud data storage. The suggested plan improves privacy by guaranteeing that users can authenticate themselves to the cloud without disclosing who they are. Block chain integration improves system security by removing single points of failure and enabling robust, decentralized access management. The plan handles user revocation, enables fine-grained access control, and successfully thwarts replay assaults. Users have similar communication, processing, and storage overheads to centralized methods when creating, editing, and reading material that is stored on the cloud. Our method is decentralized, which sets it apart from conventional models and makes it a useful and effective way to store data securely on the cloud for applications that need a high degree of trust.
Secured and Privacy in Exam Cell Management System Using Blockchain Mechanism with Advanced Encryption Standard (AES) Algorithm
Authors:   UG Student, Dept of IT Sengunthar Engineering College (Autonomous) Namakkal, Tamil Nadu, India, N Dhiwakar , E Boopathy ,   UG Student, Dept of IT Sengunthar Engineering College (Autonomous) Namakkal, Tamil Nadu, India, Y Indrajeeth ,   UG Student, Dept of IT Sengunthar Engineering College (Autonomous) Namakkal, Tamil Nadu, India, V Rahul ,   UG Student, Dept of IT Sengunthar Engineering College (Autonomous) Namakkal, Tamil Nadu, India
Published: April 2024

This study proposes a novel approach to enhance the security and privacy of Exam Cell Management Systems (ECMS) by integrating blockchain technology with the Advanced Encryption Standard (AES) algorithm. Traditional systems face challenges such as data breaches and unauthorized access, necessitating innovative solutions. Our proposed system utilizes blockchain's decentralized and immutable nature to securely record and verify examination-related transactions. Each transaction is encrypted using AES before being added to the blockchain, ensuring confidentiality and tamper-proofing. The decentralized nature of blockchain enhances transparency and accountability, while AES encryption provides robust data protection. Experimental results demonstrate significant improvements in data security, privacy, and integrity compared to traditional systems. This approach offers a promising solution for educational institutions to mitigate the risks associated with data breaches and unauthorized access, thereby ensuring the integrity and trustworthiness of the examination process.
Building a Full Stack Solution for Seamless Device Connectivity and Data Management
Authors:   Professor, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, TamilNadu, India, P. Dr.Rameshkumar , S.R. Bharanii ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, TamilNadu, India, S Mohanram ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, TamilNadu, India, R. Prasanth ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, TamilNadu, India, R Venkatesh ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, TamilNadu, India
Published: April 2024

This paper involves developing a full-stack IoT application with a cloud server. The project encompasses defining the project scope, selecting suitable hardware, developing firmware/software for IoT devices, setting up a cloud server with IoT services, configuring data ingestion and storage, building a backend application for data processing, creating a frontend UI/dashboard, establishing bidirectional communication, conducting testing, deploying the applications, and implementing continuous monitoring and optimization. By following these steps, the project aims to create a comprehensive IoT solution that collects, analyzes, and visualizes data from connected devices, enabling remote monitoring and control.
Artificial Intelligence Based CCTV-Accident Detection System
Authors:   Associate Professor, Department of ECE, Sengunthar Engineering College,  Tiruchengode, Tamilnadu, A Rahul , B Boopathi ,   UG Students, Department of ECE,  Sengunthar Engineering College, Tiruchengode, Tamilnadu, E Karuna ,   UG Students, Department of ECE, Sengunthar Engineering College, Tiruchengode, Tamilnadu, prabhu V.Murali ,   UG Students, Department of ECE, Sengunthar Engineering College, Tiruchengode, Tamilnadu
Published: April 2024

Urban infrastructure and public safety are severely hampered by traffic congestion and accidents. This research suggests an enhanced traffic light control system that makes use of deep learning methods and IoT integration to address these problems. The system computes vehicle density in real-time and indicates smart light conditions at traffic signals by combining the object identification algorithm of YOLOv3 with Internet of Things sensors. The secret to the system is the use of YOLOv3 for precise vehicle identification and categorization in video feeds recorded by security cameras. The technology can effectively identify cars, collisions, and violent incidents on the road by utilising YOLOv3. In order to enable quick emergency response, the system sends notification alerts via GSM connection to neighbouring police stations and hospitals upon detection. In addition, the suggested system uses Internet of Things devices to calculate the number of vehicles at crossings. Traffic signal timings can be dynamically adjusted thanks to this real-time data on vehicle flow, which improves traffic flow and lessens congestion. IoT integration also makes it possible for traffic signals to have smart light indication, which adjusts signal timings in response to current traffic circumstances to improve efficiency and safety.
IOT Based Water Quality Detection for Sugar Factory
Authors:   Professor, Department of ECE,  Sengunthar Engineering College (Autonomous) Tiruchengode, TamilNadu, India, M. Baskaran , K Meiyarasan ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, TamilNadu, India, M. Sridharan ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, TamilNadu, India, R.B. Suriya ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, TamilNadu, India
Published: April 2024

This paper proposes an IoT-based water quality detection system tailored for sugar factories. Employing sensors and microcontrollers, it continuously monitors crucial parameters like pH, turbidity, and dissolved oxygen levels. Data is transmitted wirelessly to a central server for real-time analysis and alerts. This system enhances operational efficiency by enabling timely interventions to maintain water quality standards, ensuring optimal production processes.
Enhancing Security with Arduino uno-Powered Patrolling Robot
Authors:   Assistant professor, Department of ECE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, M. Arunkumar , Prakash S.Hari ,   UG Students, Department of ECE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, S. Jayasurya ,   UG Students, Department of ECE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, G. Santhoshkumar ,   UG Students, Department of ECE, Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

The development of a patrol robot using an Arduino Uno, ultrasonic sensors, PIR sensors, and the ESP32-CAM module represents an innovative approach to surveillance and automation. This autonomous robot is designed to navigate and secure designated areas efficiently. The ultrasonic sensors enable obstacle avoidance, while the PIR sensors detect motion accurately. The ESP32-CAM module provides real-time video surveillance capabilities. The robot is controlled and monitored remotely through a user-friendly interface, making it ideal for applications such as home security, monitoring, and automated patrolling. Future enhancements may involve integrating advanced navigation algorithms, and cloud-based data analysis to further expand its capabilities and adaptability.
Smart Irrigation and Pesticides System for Enhancing Harvesting in Agriculture Using IoT
Authors:   Asst.Prof, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchencode, Tamil Nadu, INDIA, . Gowthamani , Babu R.Arun ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchencode, Tamil Nadu, INDIA, V. Rishikesh ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchencode, Tamil Nadu, INDIA, R Sankar ,   UG Student, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchencode, Tamil Nadu, INDIA
Published: April 2024

Agriculture plays a vital role in the economy of many countries, serving as a primary source of food. However, traditional farming practices often face challenges such as inefficient water usage, over-reliance on pesticides, and inconsistent monitoring of crop conditions. In recent years, the Internet of Things (IoT) has emerged as a promising technology to address these challenges by providing real-time monitoring and control of agricultural processes. This leads in excessive water use, which has an impact on agricultural output. This integrated system means that farmers only need to perform little manual monitoring. The opening and closing of the reservoir and field gates, as well as the verification of the plant's age and the matching optimal water level necessary for that time of plant age, are all automated. Farmers may use their mobile phones to check the water level in the field since they are continually receiving updates on the field work. The most labour-intensive and time consuming activity in agriculture is water management. In our project, the water level in the agriculture harvest sting is measured, and if it is lower than the required level, the paddy field is automatically irrigated for the water source or tank using a water pump. It can comprehensively monitor air temperature and humidity, light intensity, ultraviolet light and other environmental elements. In specific use, the agricultural production environment monitoring system starts from the actual needs of the plantation zone and is assembled according to the environmental elements it needs to monitor.
Smart Home Automation System
Authors:   Professor, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode,Tamil Nadu, India, C. Dr.Aarthi , E Keerthana ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode,Tamil Nadu, India, T Pavithra ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode,Tamil Nadu, India
Published: April 2024

In fast moving world, where people are very busy in their own things, there is a need for automation in all activities, not only industries and machineries; even home automation is of prime importance. As of now, for automation purposes we use different wireless technologies and that process uses a PC, voice processor etc., these have their own limitations and results in decreased accuracy. In order to provide more reliable and convenient automation we use android applications. Smart phones are becoming more popular now a days and each of us are having a smart phone in present situation. So by using dedicated applications on an android device complete automation can be done and this to a great level will decrease the drawbacks caused by other conventional wireless technological processes. It has a variety of advantages over conventional old microcontrollers such as RISC architecture, larger memory word, fast operation and a host of inbuilt features such as ADC, communication protocols etc., The project can be used to control any external environment where in the person may like to control all his surroundings such as fans, lights, TVs, etc; He may also like to open the door for someone or close the water tap.
Vehicle Headlight Automatic Bright and Dim Control System
Authors:   Professor, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, M Baskaran , S Naveen ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA, P Prakash ,   UG Student, Department of ECE, Sengunthar Engineering College (Autonomous), Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

Headlights of vehicles pose a great danger during night driving. The drivers of most vehicles use high, bright beam while driving at night. This causes a discomfort to the person travelling from the opposite direction and therefore experiences a sudden glare for a short period of time. This is caused due to the high intense headlight beam from the other vehicle coming towards the one from the opposite direction. In this paper, an automatic headlight dimmer which uses a sensor has been designed to dim the headlight of on-coming vehicles to avoid human eye effects. This automatically switched the high beam into low beam, therefore reducing the glare effect by sensing the light intensity value of approaching vehicle. Road accidents are a serious cause of concern and glare from headlamps of vehicles during night time create havoc for the drivers making driving really an uphill task and due to the undesirable situation, drivers meet with accidents. It’s a very hectic job for the drivers to manually control the beam of the headlamps during night from time to time when an oncoming vehicle is located within 150 meters of the other vehicle. So, it’s high time when a safety control unit should be installed in vehicles that can automatically dim the headlamps by detecting their intensity which, otherwise, might cause a serious trouble for the drivers. Our work proposes a cost effective and useful control unit which will automatically detect the excessive glare from headlamps and will give a signal to the oncoming vehicle causing the trouble to dim the lamps. As such, the driver’s unnecessary load will be reduced and driving will become safer and smoother during night.
Feed Forward Neural Network With Column-Wise Matrix–Vector Multiplication on FPGAs
Authors:   Professor / ECE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamilnadu, INDIA, C. Dr.Aarthi , S. Kowsalya ,   PG Scholar, Department of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamilnadu, INDIA
Published: April 2024

This article presents a reconfigurable accelerator for recurrent neural networks with fine grained column wise matrix vector multiplication (RENOWN).We propose a novel latency hiding architecture for recurrent neural network accelerator using column wise matrix vector multiplication instead of the state of the art row wise operation. This hardware (HW) architecture can eliminate data dependencies to improve the throughput of RNN inference systems. Besides, we introduce a configurable checkerboard tiling strategy which allows large weight matrices, while incorporating various configurations of element-based parallelism (EP) and vector-based parallelism (VP). These optimizations improve the exploitation of parallelism to increase HW utilization and enhance system throughput. Evaluation results show that our design can achieve over 29.6 tera operations per second (TOPS) which would be among the highest for field-programmable gate array (FPGA)-based RNN designs. Compared to state-of-the-art accelerators on FPGAs, our design achieves 3.7–14.8 times better performance and has the highest HW.
Survey on Power and Area efficient FIR Filter Architecture in Digital Encephalography System
Authors:   Professor / Dept of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, Kumar Dr.P.Ramesh , Sundar R.Shyam ,   PG Scholar, VLSI Design Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

In the process of Electroencephalogram (EEG) acquisition, the electrical signals from the brain are contaminated by numerous noise sources and artifacts. American Clinical Neurophysiology Society recommends digital filtering of acquired EEG signals with an upper cut-off frequency of 70 Hz before display in digital encephalography systems. We propose a linear symmetrical FIR filter architecture for filtering EEG signals using approximate computation techniques. Approximate computation techniques result in lower hardware resources and lower power consumption with some compromise in quality. Software tools used for this study include MATLAB (and its FDA tool) and Xilinx ISE 14.7. The chosen target platform is the Spartan-3e starter FPGA board. We propose the architecture for an Approximate Dadda Tree Multiplier. The proposed approximate multiplier consumes 25% lesser slices and 35.18% lower dynamic power than state-of-the-art approximate multipliers. The proposed FIR filter consumes 19.77% lesser LUTs and 34.97% lower power than state-of-the-art symmetric FIR filter architectures.
Environmental Impact Assessment of Chiplet-Based VLSI
Authors:   Associate Professor/ Dept of ECE Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA, A. Rahul , J Shripriyadarshini ,   PG Scholar/ECE, VLSI Design Sengunthar Engineering College (Autonomous) Tiruchengode, Tamil Nadu, INDIA
Published: April 2024

In this paper, we explore the revolutionary shift in VLSI design brought about by chiplet-based architectures, which are superior to the older, single-piece designs in many ways. This type of architecture not only boosts performance and allows for greater system expansion but also plays a significant role in promoting environmental sustainability. Chiplet-based designs lead to more resource-efficient, less wasteful, and more energy-efficient semiconductor technologies, steering the industry towards a more environmentally friendly path. The adoption of eco-chips enhances these environmental benefits, leading to a notable decrease in the carbon emissions associated with electronic devices and systems. This shift is pivotal in achieving the twin goals of advancing technology and upholding environmental stewardship in VLSI. We introduce HI as a strategy for achieving sustainable computing, creating chiplet-based systems that have a smaller carbon footprint than traditional System on Chips (SoCs). We also present ECO-CHIP, a tool we developed for estimating the carbon footprint of these systems, accounting for both their construction and operational phases, including the carbon costs of advanced packaging. In Section VI, we illustrate how ECO-CHIP can be used for breaking down systems into smaller parts and for exploring different design options, and we have integrated it with other tools for estimating the costs of chiplet-based designs. ECO-CHIP is freely available as open-source software and can be found in an anonymous repository [21]. We contend that ECO-CHIP will facilitate the creation of more environmentally friendly design practices for the next generation of complex systems.
Classification of Alzheimer’s Disease Detecting Using Deep Learning
Authors:   Student, Dept. of Medical Electronics Engineering, Sengunthar Engineering College (Autonomous), Namakkal, Tamil Nadu, INDIA, P Nandhini , Kumar Dr.R.Satish ,   Professor, Dept. of Electronics communication Engineering Sengunthar Engineering College (Autonomous), Namakkal, Tamil Nadu, INDIA, Sathik Raja Muhammadu ,   Professor, Dept. of Medical Electronics Engineering Sengunthar Engineering College (Autonomous), Namakkal, Tamil Nadu, INDIA
Published: April 2024

There are a wide range of reasons for dementia, yet Alzheimer's Infection is the most normal structure. As the condition advances, it restricts one's capacity to play out any errand without help, and the analysis timetable and maturing populace are supposed to make its commonness increment. The regular approaches to distinguishing Alzheimer's is tiring for the two patients, specialists where it includes recovering the previous clinical records and having Attractive Reverberation Imaging sweeps and even neuro actual testing which can be awkward for patients. An early determination of cerebrum infections has a major effect with regards to endeavouring to fix them. Our work has utilized profound learning (brain organizations) to identify Alzheimer's sickness sooner than expected by joining it with profound learning. As the got dataset from UCI Storehouse is vigorously imbalanced, we equitably conveyed the information between the classifications utilizing Destroyed. Then the model is prepared and tried with the ordered X-ray information for example extremely gentle, gentle, moderate and serious Promotion lastly remove elements to look at the outcomes. The outcomes we accomplished are contrasted and the past endeavours on recognition of Alzheimer's and emerged to be fundamentally more noteworthy with regards to accuracy and exactness.
Enhanced Diagnostics Based on Deep Learning for Varicose Vein Detection
Authors:   Student, Dept. of Medical Electronics Engineering, Sengunthar Engineering College (Autonomous), Namakkal, Tamil Nadu, INDIA, K Sugithayeni , Kumar Dr.R.Satish ,   Professor, Dept. of Electronics Communication Engineering, Sengunthar Engineering College (Autonomous),  Namakkal, Tamil Nadu, INDIA, Sathikraja Prof.Muhammadu ,   Professor, Dept. of Medical Electronics Engineering, Sengunthar Engineering College (Autonomous), Namakkal, Tamil Nadu, INDIA
Published: April 2024

Varicose veins, a prevalent vascular condition affecting a significant portion of the population, necessitate early detection for optimal intervention and improved patient outcomes. This study introduces a novel diagnostic approach for varicose vein detection, leveraging the capabilities of deep learning. Varicose veins represent a prevalent vascular condition that can lead to discomfort and complications if not identified and treated promptly. This research introduces an advanced diagnostic methodology for varicose vein detection, utilizing deep learning techniques. Our approach harnesses the capabilities of deep neural networks, specifically employing a convolution neural network (CNN) architecture. Trained on a diverse dataset of vascular images, the model demonstrates a high proficiency in identifying varicose veins by capturing nuanced patterns and subtle features. Fine-tuning through transfer learning ensures adaptability to variations in patient demographics and imaging conditions. To enhance diagnostic precision, our system incorporates advanced image processing techniques and feature extraction methods. The integration of image segmentation algorithms and anatomical feature analysis provides detailed insights into the extent and severity of varicose veins. Furthermore, the model is designed to accommodate multi-modal imaging data, incorporating information from various imaging modalities to enhance diagnostic accuracy. Validation of our approach is conducted on a comprehensive dataset encompassing a spectrum of varicose vein cases, including varying degrees of severity. The results underscore the superior performance of our deep learning-based diagnostic system, achieving notable sensitivity and specificity in varicose vein detection. This proposed enhanced diagnostic tool holds promise for healthcare professionals, streamlining the diagnostic process and facilitating early and accurate identification of varicose veins. The potential implications include improved patient outcomes through timely intervention and the development of personalized treatment strategies. As we continue refining and expanding our model, its integration into clinical practice could contribute to a more efficient and effective management of varicose vein disorders.
Block Chain Based Agri-Food Traceability with SHA-256 Algorithm
Authors:   UG Scholar, Department of Information Technology Sengunthar College of Engineering, Tiruchengode, India, C Sabarinathan , M Selvakumar ,   UG Scholar, Department of Information Technology Sengunthar College of Engineering, Tiruchengode, India, M Selvamurugan ,   UG Scholar, Department of Information Technology Sengunthar College of Engineering, Tiruchengode, India
Published: April 2024

Block chain technology has emerged as a promising solution for enhancing transparency and efficiency in various industries, including agriculture and food supply chains. This article examines the influence of block chain technology on agriculture, explores ongoing projects and initiatives, and assesses its potential implications, obstacles, and promises. Despite its potential to revolutionize the food supply chain by enabling transparent transactions among dispersed and untrustworthy parties without intermediaries like banks, several challenges hinder its widespread adoption. These challenges encompass technological limitations, educational gaps, and regulatory hurdles. This study provides insights into the current state of blockchain adoption in agriculture and offers critical perspectives on project maturity.