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Braille–Latin conversion using memristive bidirectional associative memory neural network

Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12511-12534页

作者： Vaidyaraman, Jayasri Thyagarajan, Abitha K. Shruthi, S. Ravi, V. School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The successful implementation of ANNs in software—particularly in the fields of deep learning and machine learning—has spiked an interest in designing hardware architectures that are custom-made to implement ANNs. Several categories of ANNs exist. The two-layer bidirectional associative memory (BAM) is a particular class of hetero-associative memory networks that is extremely efficient and exhibits good performance for storing and retrieving pattern pairs. The memristor is a novel hardware element that is well-suited to modelling neural synapses because it exhibits tunable resistance. In this work, in order to create a device that can perform Braille–Latin conversion, we have implemented a circuit realization of a BAM neural network. The implemented hardware BAM uses a memristor crossbar array for modelling neural synapses and a neuron circuit comprising an I-to-V converter (resistor), voltage comparator, D flip-flop, and inverter. The efficiency of the implemented hardware BAM was tested initially using 2 × 2 and 3 × 3 patterns. Upon successfully verifying the ability of the implemented BAM to store and retrieve simple pattern pairs, it was trained for a pattern-recognition application, namely mapping Braille alphabets to their Latin counterparts and vice versa. The performance of the implemented BAM network is robust even on the introduction of noise. The application can recognize the input patterns with accuracies of 100% in either direction when tested with up to 30% noise. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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Real-Time Pothole Detection at night to prevent road accidents using YOLOV5 6

Real-Time Pothole Detection at night to prevent road acciden...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Mandal, Gouranga De, Anurag Dey, Arindam Gangarapu, Sudeepthi Chekuru, Sharmila Rani Nandini, Chekuri Vellore Institute of Technology School of Computer Science and Engineering Andhra Pradesh 522237 India Vellore Institute of Technology School of Computer Science and Engineering with Specialization in Data Analytics Andhra Pradesh 522237 India

ISBN: (纸本)9798350394702

This article proposes a technique to create a pothole detection system that can operate both during the day and at night. This model was developed using the real-time object identification YOLO (You Only Look Once) technique. The potholes are located using a pre-trained model with YOLO v5. In its creation, the PyTorch framework is utilized. The first YOLO model created using the PyTorch framework is YOLOv5, which is a lot more user-friendly and lightweight. In a manner similar to real-time object detection systems, the proposed method displays potholes in real time and emphasizes them with boundary boxes for accurate identification. The accuracy achieved with this method is 97% during the day and 92% at night which is found to be satisfactory. © 2023 IEEE.

关键词： Image acquisition

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A study on automated toll collection: Towards the utilisation of RFID-based system

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International Journal of Cloud Computing 2022年第1期11卷 89-100页

作者： Kannan, Naresh Goyal, Ranjan Goel, Dhruv Anitha, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The toll collection is becoming a major problem on the highways leading to large waiting queues. Toll gates installed on highways result in increased waiting time and fuel usage. In this paper, a study on automated toll collection system using radio frequency identification (RFID)-based system is presented, which provides fast identification of vehicles and toll collection. Using this system, the identification can be done just by slowing down the speed of the vehicle when it is passing from the toll plaza. The RFID reader scans the RFID tag or card and deducts the amount from it. The research analysed the system by proposing a mechanism and implementing an example scenario, which considered the random arrival of different vehicles. This technique is also compared with other existing mechanisms such as number plate recognition and barcode-based passes, which showed the need to utilise this technique for toll collection. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Radio frequency identification (RFID)

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Real-Time Heuristic-Based Detection of Attacks Performed on a Linux Machine Using Osquery

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SN computer science 2022年第5期3卷 405页

作者： Ahamed, Sarfaraz Lakshmanan, Ramanathan Department of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IoT Vellore Institute of Technology Vellore India

With the increase in Unix-based operating system for web servers and IoT devices, it has become crucial to detect attacks that are performed on these critical devices. Detection can be done at multiple layers of David Bianco's Pyramid of Pain [http://***/2013/03/***] which consists of the following layers: TTPs, Tools, Network/Host Artifacts, Domain Names, IP Address, and Hash Values. As majority of recent work focuses on machine learning to help detect attack, our focus of this paper is detection of attacks predominantly at the TTPs, Tools, and Network/Host Artifacts levels using heuristic-based detection. This will allow us to provide detection in depth to machine learning models by detecting known bad that is sometimes missed by machine learning models. Using osquery, we were able to create a real-time heuristic-based detection script for Linux. This script takes in each log from the osquery and tries to match against various conditions to detect initial connections, lateral movement, and privilege escalation. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Attacker’s behaviour Heuristic HIDS Host-based intrusion detection Real-time detection Unix

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A Systematic Review of Federated Learning Mechanism for The Internet of Medical Things 1

A Systematic Review of Federated Learning Mechanism for The ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aravind, P. Jerlin, M Asha Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Ghaziabad201204 India Medicine University of Luxembourg Faculty of Science Technology Department of Mathematics Luxembourg

ISBN: (纸本)9798350353648

The rapid evolution of healthcare technology, no-tably the integration of the Internet of Medical Things (loMT), has revolutionized patient care, diagnosis, and treatment method-ologies. However, this progress introduces substantial challenges, primarily concerning the security, privacy, and interoperability of sensitive medical data. Federated Learning, a decentralized ma-chine learning approach, emerges as a possible solution to address these difficulties since it allows for collaborative model training without requiring raw patient data exchange. This systematic review explores and evaluates various FL mechanisms tailored for 10MT applications. The review examines existing literature, encompassing the amalgamation of loT and healthcare, the challenges faced in traditional health care systems, and the role of FL in mitigating these challenges. It highlights applications, privacy-preserving techniques, security measures, advantages, limitations, and potential enhancements in utilizing FL within 10MT environments. The review also presents a structured methodology for evaluating FL paradigms, encompassing performance metrics, privacy/security analysis, advantages/limitations assessment, and comparative analysis. Moreover, the paper pro-poses an innovative framework for federated learning in 10MT, detailing its architecture, modules, algorithms, functionalities, ethical considerations, and expected impact. Through a comprehensive analysis and synthesis of existing literature and proposed strategies, this review aims to provide insights for health care stakeholders in selecting and implementing optimal FL strategies for collaborative medical advancements while upholding stringent data privacy and security standards in 10MT. © 2024 IEEE.

关键词： Federated learning

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Deep Neural Network Based Smart Intrusion Detection and Alerting System 10

Deep Neural Network Based Smart Intrusion Detection and Aler...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Karthikeyan, N. Mahapatro, Soumya Ranjan Akash, R. Rakesh Teja, P.V. Vellore Institute of Technology School of Computer Science and Engineering Chennai Campus Tamil Nadu India Vellore Institute of Technology School of Electronics and Comm. Engineering Chennai Campus Tamil Nadu India

ISBN: (纸本)9798350353068

With the increasing prevalence of surveillance systems, the need for an automated system that detects the presence of intruders from the surveillance camera and send an alert message to the user immediately has become very crucial. The research paper aims to provide a deep learning (DL) model-based solution to accurately detect the presence of human figures in the frame. The approach includes using YOLOv8 (You Only Look Once - Version 8) object detection model to detect human figures, paired with the implementation of the Non-Maximum Suppression technique to remove duplicate bounding boxes around humans. Additionally, Gaussian Noise Reduction technique has been used to remove Gaussian blur from the image for obtaining a clearer image of human figure present in the frame. The research paper demonstrates a procedure to quickly send alert message to the user when a human figure is detected in the camera using the Telegram application. The paper also demonstrates the implementation of frame skipping method based on periodic intervals for optimizing the computational efficiency. In addition to this, the paper also demonstrates a comparative study between the frequently used human detection algorithms namely YOLOv8, YOLOv4, SSD and HOG and provides statistical proof on how YOLOv8 outperforms the other standard models. © 2024 IEEE.

关键词： Intrusion detection

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Machine Learning Based Earlier Identification of Liver Disease Using Ultrasound Images 1st

Machine Learning Based Earlier Identification of Liver Disea...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Saravanakumar, C. Prakash, M. Deepak Kumar, A. Ashokkumar, C. Department of Computer Engineering Government Polytechnic College Purasawalkam Chennai600012 India School of Computer Science and Engineering Vellore Institute of Technology Vellore632014 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai603203 India

ISBN: (纸本)9783031759567

Liver disease begins in the ovaries and is especially hazardous for women. As a result, abnormal cells can potentially spread to other organs. Liver disease is a type of dangerous development that affects the ovaries in females, and it is difficult to detect at an early stage, which is why it remains one of the leading causes of death from disease. Unquestionable confirmation of intrinsic and natural components is crucial for the development of novel systems to detect and eliminate hazard. We propose the detection and categorization of liver illness in ultrasound pictures using hybrid machine learning techniques. Using Machine Learning Algorithms and a vote-based classifier to classify liver tissues as fatty or normal based on ultrasound picture attributes and voting. Our created method provides four major contributions: first, the classification of liver pictures as normal or fatty is performed without a segmentation phase. Secondly, compared to our suggested effort, the datasets in earlier works were inadequate. The third contribution is a collection of 26 characteristics. On the basis of the presented methods, sigmoid radial basis function neural network (SRBFNN) and Gray-Level Co-Occurrence Matrix are the recovered features (GLCM). The fourth contribution is the voting classifier that is utilised to determine the type of liver tissue. Fruit Fly Optimization concludes the categorization process (FFO). We have achieved a classification accuracy of 98.24% using our proposed approach. Convolutional Neural Network (CNN), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Naive Bayes are compared with our proposed system (NB). The experimental results demonstrate that the classification accuracy of our suggested system is superior to that of the leading classification strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Radial basis function networks

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Predictive Classification Model of Crop Yield Data Using Artificial Neural Network 5

Predictive Classification Model of Crop Yield Data Using Art...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Kanagaraj, R. Elakiya, E. Kumaresan, A. Selvakumar, J. Suganya, J. Shruthi, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India Vellore Institute of Technology Department of Computer Science and Engineering Chennai India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Coimbatore India Mailam Engineering College Mailam India Bosch Global Software Technologies Coimbatore India

ISBN: (纸本)9798350321425

Agriculture is under tremendous pressure to produce more productivity with fewer resources as a result of the expanding global population. Decision-making along the entire supply chain needs to be improved considering competing economic, environmental, and societal demands. Data from satellites, drones, sensors, and other sources must be processed in order to make informed judgments. However, sophisticated machine learning and data analytics methods must be used due to the complexity and volume of the data. This initiative covers crucial duties in yield prediction and precision agriculture. Management zones are areas of a field that have high inter-regional variability and low intra-regional variability, dividing the field into uniform zones for which agronomic practices should be modified independently. For fertilizer optimization and post-harvest logistics, yield prediction is crucial. In order to make up for the nutrients that have been taken out of the soil, fertilizer type and quantity must be adjusted accordingly. The yield data is also essential for enhancing harvesting, shipping, storage, and sales. So, utilizing web scraping and convolution neural networks, the proposed model has been forecast yields from input satellite photos. The result of investigations is illustrated that the artificial neural network algorithm is used to predicting the agricultural data rules and powerfully decrease the error rate from crop yield data. © 2023 IEEE.

关键词： Neural networks

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Intelligent Street Light Management System using Solar Panels 3

Intelligent Street Light Management System using Solar Panel...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Venkatraman, Akila Ruchitha, Arutla Sellamuthu, Suseela Shalini, Dappu Reddy, Pulsani Dinesh Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Vellore Institute of Technology Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518592

The conventional street lighting system is used during night time. It consumes more amount of power and there is major energy demand arises. The energy saving methods and lighting control arises to overcome the problem. The smart street light project aims to improve the energy efficiency and urban safety by using sensor- equipped street lights. It can adjust their brightness automatically based on levels of ambient light and the presence of vehicles and pedestrians. Additionally, the project incorporates the central management system implemented by using the ESP 32 module and BLYNK cloud, that allows for remote monitoring and control of street lights. The implementation of this technology can lead to significant cost saving, improves security for the cities and rise the overall quality of life for residents. Vehicle detection system is implemented to control the street lights when vehicle is detected. LED's (Light Emitting Diode) doesn't consume large amount of electricity. By the combination of both LED's and LDR's (Light Dependent Resistor) the intensity of light can be controlled and save more electricity. To make a step towards green energy, solar panels are used to power the street lights. The increased standard of living and growth of cities lead to the complex lighting system with automatic and intelligent control schemes. © 2024 IEEE.

关键词： Energy efficiency

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waRLOCK: Countering Ransomware and Data Leak 2

waRLOCK: Countering Ransomware and Data Leak

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Gupta, Soumyo Deep Kumar, Rajesh School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Tamil Nadu Chennai600127 India

ISBN: (纸本)9798350383652

In an era marked by the evolution of cybercrime from isolated hackers to highly organized enterprises, the surge in ransomware attacks presents a pressing concern. Enter RLOCK (pronounced as warlock), a meticulously crafted system that employs a novel reverse monitoring approach, actively transforming data to self-destruct, offering proactive protection against ransomware and data leaks. RLOCK revolutionizes the fight against ransomware by implementing continuous monitoring of specific files, prioritizing data resilience in hostile environments. Additionally, the challenge of protection against data leaks is met with a proactive solution. Imagine the peace of mind that comes with the knowledge that, even in the event of a data breach, the compromised information will self-destruct when accessed on an unauthorized system. Through RLOCK's data transformation, data can discern its environment, triggering self-destruction upon authentication failure. Discover a new paradigm in cybersecurity with RLOCK. © 2024 IEEE.

关键词： Information leakage

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