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The Design and Implementation of AI Based Smart Notice Board Using Advanced machine learning and Image Processing Techniques

The Design and Implementation of AI Based Smart Notice Board...

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international conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Md. Muhaiminul Islam Md. Rakib Hossain Farzana Akter Bhakti Kusum Barman Shuvo Malakar Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

In contemporary society, conventional notice boards encounter challenges in terms of accessibility and engagement, especially within educational institutions where effective information dissemination is vital. By implementing a Smart Notice Board that is powered by cutting-edge technologies like artificial intelligence (AI), machine learning (ML), image processing, and effective frontend-backend communication, this research offers a game-changing solution. The system surpasses traditional analog methods by integrating digital capabilities, allowing for real-time updates and efficient communication within a global network. AI-based facial recognition is used by the Smart Notice Board to improve user experience and deliver tailored information to individuals or particular groups. When a person appears in front of the camera, the system recognizes them and pulls up relevant notifications based on their profile, which reduces the amount of work teachers, staff, and students have to do to stay informed. The system includes advanced image processing techniques to identify individuals, so notice delivery accuracy is guaranteed. In addition, the integration of socket technology guarantees smooth communication between the backend server and frontend user interface, facilitating effective data storage and transfer. By eliminating the time constraints linked to conventional notice boards, this method enables administrators to remotely oversee and distribute content. Additionally, the server stores individual images for facial recognition and customized notice delivery. By combining AI for facial recognition, ML and image processing for visual data analysis, and an advanced frontend-backend technology for system architecture, this smart notice board project not only streamlines the distribution of information but also greatly improves communication in educational setting as well as lessens burdens and produce a flexible platform that can adapt to changing user requirements.

关键词： Visualization Accuracy Image processing Sockets Face recognition machine learning User interfaces

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The Role of Artificial Intelligence in Information Acquisition

The Role of Artificial Intelligence in Information Acquisiti...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： J.N. Singh Saurabh Saxena Riyanshu Saini Manali Chauan Kavita Saini School of Computing Science and Engineering Galgotias University Greater Noida UP India

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

Search engines use artificial intelligence to find potential leads. Early concepts in both fields are introduced in Bush and Turing’s publications, which are also utilized to define artificial intelligence and data mining in this article. The foundation for further exploration of intellectual notions of art and their application to information retrieval is a straightforward model of an information retrieval system. pattern recognition, representation, problem solving and planning, reasoning, and learning are among the concepts covered. This paper finishes by outlining future directions for artificial intelligence research in data mining systems.

关键词： Natural languages machine learning Organizations Writing Search engines Market research Knowledge management

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Parallel Agglomerative Hierarchical Clustering Algorithm Implementation with Hadoop MapReduce 1

引用

3rd international conference on machine learning, Advances in Computing, Renewable Energy and Communication, MARC 2021

作者： Maithri, C. Chandramouli, H. Department of CSE Kalpataru Institute of Technology Tiptur India Department of CSE East Point College of Engineering and Technology Bangalore India

ISBN: (数字)9789811928284

ISBN: (纸本)9789811928277

Current expectations on popular classification methods in today’s data mining software are increasing due to the large volumes of generated data from online interactive systems. Hierarchical agglomerative clustering (HAC) algorithm is a popular algorithm that is adopted for clustering both similarity and dissimilarity datasets. Solutions of data analytics with parallel and distributed architectures are promising models for storing, retrieving and analyzing large datasets. Hadoop MapReduce framework is a distributed and parallel platform to handle large datasets. Classification algorithms redefined to use features of Hadoop MapReduce method to provide an ability to solve the problem of clustering on large volume of data. In this paper, we are proposing an agglomerative hierarchical clustering with parallel implementation, using MapReduce approach for large dataset. It divides dataset into data blocks;each data block is classified to groups on different distributed data node of Hadoop MapReduce with agglomerative hierarchical clustering method. The implementation is fully MapReduce programming model, which will have nodes that pull task assignment and global status for determining their individual node status. The implementation defines an execution environment that facilitates algorithm execution over different datasets. The significance of this approach provide the proposed clustering model over the distributed environment with an increase in efficiency for large data analysis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： data mining

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Visual Microfossil Identification via Deep Metric learning 3rd

Visual Microfossil Identification via Deep Metric Learning

引用

3rd international conference on pattern recognition and Artificial Intelligence, ICPRAI 2022

作者： Karaderi, Tayfun Burghardt, Tilo Hsiang, Allison Y. Ramaer, Jacob Schmidt, Daniela N. Department of Computer Science University of Bristol Bristol United Kingdom Institutionen för geologiska vetenskaper Stockholm University Stockholm Sweden School of Earth Sciences University of Bristol Bristol United Kingdom

ISBN: (纸本)9783031090363

We apply deep metric learning for the first time to the problem of classifying planktic foraminifer shells on microscopic images. This species recognition task is an important information source and scientific pillar for reconstructing past climates. All foraminifer CNN recognition pipelines in the literature produce black-box classifiers that lack visualisation options for human experts and cannot be applied to open set problems. Here, we benchmark metric learning against these pipelines, produce the first scientific visualisation of the phenotypic planktic foraminifer morphology space, and demonstrate that metric learning can be used to cluster species unseen during training. We show that metric learning outperforms all published CNN-based state-of-the-art benchmarks in this domain. We evaluate our approach on the 34,640 expert-annotated images of the Endless Forams public library of 35 modern planktic foramini-fera species. Our results on this data show leading 92 % accuracy (at 0.84 F1-score) in reproducing expert labels on withheld test data, and 66.5 % accuracy (at 0.70 F1-score) when clustering species never encountered in training. We conclude that metric learning is highly effective for this domain and serves as an important tool towards expert-in-the-loop automation of microfossil identification. Key code, network weights, and data splits are published with this paper for full reproducibility. © 2022, Springer Nature Switzerland AG.

关键词： Computer vision

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Predicting Depression from Social Networking data using machine learning Techniques 3

Predicting Depression from Social Networking Data using Mach...

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3rd international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2021

作者： Bagga, Nandini Vashistha, Pratikshit Yadav, Palak Galgotias University CSE AI and ML Greater Noida India Galgotias University CSE Greater Noida India

ISBN: (纸本)9781665438117

In this era of COVID-19 pandemic, as more people self-isolate themselves, psychological health issues like depression, anxiety, and stress is an increasing concern all over the world. The purpose of this study is to investigate the data from social forums, where we found communities of depressed people sharing their thoughts and emotions in the forums, these forums also receive advices and support. In this paper, we will analyse the "depressed"text;by manipulating the data, extracting features, categorising, and try to understand what are the attributes of "depressed"text, and how we can "predict"whether a text should be marked as depressed or not. Using text analysis and text data mining techniques, the text obtained from the social forums was analysed and three different machine learning algorithms were used to predict depression. After cross validation overall accuracy of 99.69% was obtained as the best score using the proposed system. This study definitively answers the question regarding using human basic language and communication of personal experiences, for the prediction of depression and can be reached easily. Furthermore, not only actions, habits and behaviour of a person, text too can be used for accurate diagnosis of depression. © 2021 IEEE.

关键词： machine learning

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Hybrid CNN-GRU Model for Predicting Dysarthric Speech Using Deep learning Approaches

Hybrid CNN-GRU Model for Predicting Dysarthric Speech Using ...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Suresh Kumar Chelliah Arivazhagan N Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

This research study presents the novel method for dysarthria speech detection and classification. In order to create systems that are capable of accurately categorizing speech patterns that are impacted by dysarthria, it is vital to have speech detection and classification capabilities for dysarthria. The condition known as dysarthria, which is characterized by difficulties in articulation, phonation, resonance, and prosody, is a disorder that leads to a reduction in the intelligibility of speech. This method of classification necessitates the development of machine learning or deep learning models, more specifically a combination of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), in order to accomplish the objective of automatically classifying dysarthric speech into categories such as severity or presence/absence of dysarthria. The primary purpose of dysarthria speech categorization is to bring about the development of a reliable instrument that can be used for identifying, categorizing, and managing dysarthric speech. As an added benefit, the classification of dysarthric speech allows for the adaptation of treatment methods and the acquisition of information about the underlying causes of the condition. The individuals who are affected by this speech difficulty are the target audience for this strategy, which ultimately aims to enhance the quality of life of those individual. The study addresses the challenge of classifying dysarthric speech caused by neurological impairments using a Hybrid CNN-GRU model, achieving accuracies of 70.3% (high-severity), 38% (severe-moderate), 34.5% (moderate), and 10.3% (mild dysarthria).

关键词： Voice activity detection Deep learning Accuracy Transfer learning Speech recognition Speech enhancement Predictive models Feature extraction Robustness Convolutional neural networks

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UAV Dynamic Tracking Algorithm Based on Deep learning 3

UAV Dynamic Tracking Algorithm Based on Deep learning

引用

3rd international conference on machine learning, Big data and Business Intelligence, MLBDBI 2021

作者： Chen, Sifan Yang, Haoran Zhang, Ang Chen, Baihe Shu, Peng Xiang, Jin Lin, Chen College Of Physics And Information Engineering Fuzhou University Fuzhou China College Of Marine Culture And Law Jimei University Xiamen China

ISBN: (纸本)9781665417907

In recent years, unmanned aerial vehicle (UAV) has been widely used in target tracking tasks such as security, anti-terrorism, tracking and monitoring because of its wide and flexible perspective. At the same time, deep learning creates new conditions for UAV visual recognition and motion tracking because it has high recognition accuracy and fast recognition speed. This paper presents an UAV dynamic tracking algorithm based on deep learning. The algorithm mainly includes three parts: the PTZ control, relative position calculation and the UAV control. The main work includes: 1. The tracking target is identified through the deep learning network based on YOLOv3, and the PTZ angle is adjusted in real time by using the control method of combining the PID control with the time-delay control to reduce the impact of image transmission delay;2. The relative position between the UAV and the target is calculated by combining the UAV flight altitude, UAV attitude, PTZ angle and the coordinate of the target in the image. At the same time, the relative position prediction is added to estimate the relative distance between the UAV and the target at the next frame time as a reference value of the UAV speed, so as to improve the dynamic tracking ability;3. The relative position information is fed back to the flight control function, and the linear velocity of UAV is output by the PID controller. Through the simulation test in Airsim, it is proved that the proposed algorithm can effectively improve the stability and robustness of the UAV tracking system. © 2021 IEEE.

关键词： Time delay

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The Smart Handwritten Digits recognition Using machine learning Algorithm

The Smart Handwritten Digits Recognition Using Machine Learn...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Nachaat Mohamed R. Josphineleela Shankar Rakhamaji Madkar J. Vasantha Sena Badria Sulaiman Alfurhood Bhasker Pant Rabdan Academy UAE Homeland Security University Sains Malaysia Abu Dhabi CSE Panimalar Engineering College Tamil Nadu India College of Engineering Pune ECE Bharati Vidyapeeth (Deemed to be University) Pune Maharashtra India A.P.C.Mahalaxmi College for Women Thoothukudi India Department of Computer Sciences College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Saudi Arabia CSE Graphic Era Deemed to be University Dehradun Uttarakhand India

Never before have people relied so much on technology; now, machine learning and based on machine learning can do anything from classifying objects in photos to inserting sounds to old movies. Similar to this, handwritten word extraction is an important field of study and advancement with a wide range of potential outcomes. The capacity of a robot to accept and analyze understandable handwriting inputs via sources like paper files, pictures, smart phones, or other gadgets is called as handwriting, sometimes known at text recognition. Evidently, in this study, they have carried out handwritten digit recognition utilising Support Vector machines (SVM), Multi-Layer Number of neurons (MLP), and Deep Convolution Network (CNN) models with aid of MNIST dataset. To find the most accurate model for text detection, our major goal is to contrast the following models’ execution speed and correctness. The MNIST registry identity, which will be scrawled in numerals, may be recognised by the software. In machine language, the natural handwritten form may be recognised. The deep neural network is a machines educational approach that we use in this case. The nine integers in the MNIST data range from 0 to 9. It could be used to organise number and composition into groups that computers can understand. The main objective of this work is to guarantee reliable methods for identifying written finger numbers and simplifying and eliminating errors in banking processes. The MNIST information will be correctly identified by the system. Comparing the model’s precision and system performance is our main objective.

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SoBERT: A Soundex Based Enhancement of Robust Speech Transformer

SoBERT: A Soundex Based Enhancement of Robust Speech Transfo...

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3rd international conference on machine learning and data Engineering, ICMLDE 2024

作者： Ahlawat, Harsh Aggarwal, Naveen Gupta, Deepti UIET Panjab University Chandigarh India

Automatic Speech recognition (ASR) systems have gained significant attention due to their applications in various domains. Speech varies across regions, posing challenges for ASR and translation systems in real time. Even slight variation in pronunciation, intonation, and dialect can impact accuracy, making multilingual recognition and translation a persistent challenge particularly in case of India. This paper introduces SoBERT, a novel Soundex-based enhancement of robust speech transformer novel system, designed to reduce the Word Error Rate (WER) in speech transformer models. Additionally, it presents an analysis of an ASR system leveraging deep learning and the Transformer architecture, focusing on the OpenAI Whisper model. The SoBERT system pipeline includes the Whisper model, a probabilistic language model to identify the next most probable word and Soundex for recognizing similar words, thereby enhancing the overall accuracy of the Whisper model. Testing SoBERT on the most spoken banking everyday sentences resulted in an average reduction of 14-40% in WER compared to the existing Whisper model. The proposed SoBERT system demonstrates notable efficiency and accuracy, demonstrating the potential of the Whisper model in multilingual contexts. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Deep learning

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A Supervised machine learning Model for Predicting Adverse Drug Reactions Among Tuberculosis Patients in Zimbabwe

A Supervised Machine Learning Model for Predicting Adverse D...

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Information and Communication Technologies (ZCICT), Zimbabwe conference of

作者： Talent Manana Fungai Jacqueline Kiwa Martin Muduva Abid Yahya Shakemore Chinofunga Graduate Business School Chinhoyi University of Technology Chinhoyi Zimbabwe ICT and Electronics Department Chinhoyi University of Technology Chinhoyi Zimbabwe Industrial and Mechatronics Department University of Zimbabwe Harare Zimbabwe Electronics and Communications Systems Engineering Department International University of Science and Technology Palapye Botswana Information and Communications Technology Department Chinhoyi University of Technology Chinhoyi Zimbabwe

ISBN: (数字)9798331520335

ISBN: (纸本)9798331520342

The paper develops a supervised machine learning model to predict adverse drug reactions (ADRs) among tuberculosis (TB) patients in Zimbabwe, addressing a critical need for tools that enhance patient safety and optimize treatment outcomes. The model integrates multimodal data, including chest X-ray images and patient-specific information, to provide comprehensive risk assessments. The study employs the Cross Industry Standard Process for data mining (CRISPDM) framework, using Convolutional Neural Networks (CNNs) to analyze X-rays and ensemble learning techniques to incorporate clinical data. The model achieved high predictive accuracy, with training accuracy reaching 95% and validation accuracy stabilizing at 78%. The confusion matrix analysis demonstrated the model's ability to differentiate between various severity levels of ADRs, facilitating targeted interventions. The research underscores the potential of machine learning in medical diagnostics, particularly in settings with limited resources and highlights the need for future research to refine the model using larger and more diverse datasets. The findings have significant implications for improving TB treatment and patient outcomes in Zimbabwe.

关键词： Drugs Training Visualization Accuracy data analysis Tuberculosis X-rays Predictive models Safety X-ray imaging

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