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LiDAR 3D object detection based on improved PillarNet 9

LiDAR 3D object detection based on improved PillarNet

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Huang, Xinde Shanghai Institute of Technology School of Computer Science and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

To address the limitations of current methods in detecting small objects, such as pedestrians and cyclists, within autonomous driving scenarios, we propose a novel 3D object detection algorithm based on an improved PillarNet architecture. These limitations often manifest as reduced recognition accuracy and increased susceptibility to false positives and negatives. The proposed method helps to capture key contextual information and spatial relationships between point cloud columns by adding a global attention mechanism to the pair. In addition, the addition of residual convolution helps to enhance the feature extraction capability of the network This comprehensive strategy expands the receptive field for point cloud features and bolsters the model's robustness in recognizing small objects within intricate environments. The effectiveness of the proposed method is validated through evaluations on the KITTI dataset. Compared to the baseline PillarNet, our improved algorithm achieves a 2.5% increase in the average detection accuracy for the cyclist category, demonstrating the efficacy of our approach. © 2024 IEEE.

关键词： Object detection

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A Novel Framework for Iris Disease Detection Using MobileNetV2 and CNN with Streamlit Interface 10

A Novel Framework for Iris Disease Detection Using MobileNet...

引用

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

作者： Abinay Shri, J. Denisha, M. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350353068

'Iris Disease Detection' project is a comprehensive approach to iris health assessment that combines deep learning techniques with image processing methodologies. The system consists of a Streamlit-based web application and a Mobile Net V2 and CNN Classifier, which use OpenCV and KMeans clustering for effective image analysis. The dataset, derived from medical imaging platforms, includes hundreds of high-resolution photos of the iris, annotated with detailed diagnostic information about a wide range of iris health problems. The project includes both normal and diseased irises, as well as those with cataracts, glaucoma, and iritis. The Streamlit web application provides a user-friendly interface for uploading iris images, which undergo preprocessing, grayscale conversion, and Gaussian blur before applying the Hough Circles method to detect the iris region. The system generates visualizations, including the original image, identified iris, and segmented representation achieved through KMeans clustering. The machine learning component enhances the accuracy of 95 percentage and reliability of iris detection, offering a holistic solution for iris disease detection. © 2024 IEEE.

关键词： Deep learning

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IoT-Enabled Smart Parking System using Machine Learning for Real-Time Parking Prediction 4

IoT-Enabled Smart Parking System using Machine Learning for ...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Suresh, A. Chandu, P.M.S.S. Bose, S. Subhash Chandra Dhamini, V. Narayana, P. Adi Balaji, K. Dilli Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Puttur India

ISBN: (纸本)9798350352931

Because of the growing number of cars and the inadequate parking infrastructure, urban parking management has become more and more crucial. To predict parking spot availability in IoT-enabled intelligent systems, this study assesses the performance of many machine learning (ML) models, including decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and extra trees classifier (ETC). F-score, accuracy, recall, and precision were used to gauge performance. The DT model's 88.63% accuracy and 87.61% F-score demonstrated its low predictive power. With an F-score of 91.45% and greater accuracy of 92.30%, RF proved appropriate for medium-sized facilities. XGBoost performed better, capturing intricate parking patterns with 93.87% accuracy and 93.50% F-score. With 93.00% accuracy and 92.74% F-score, ETC performed similarly to XGBoost and provided computational advantages for big datasets. ETC and RF are dependable in medium-sized settings, however XGBoost is advised for large-scale settings. © 2024 IEEE.

关键词： Random forests

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Automated Sorting of Date Fruits: Integrating Deep Learning and Discriminant Correlation Analysis 2

Automated Sorting of Date Fruits: Integrating Deep Learning ...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Theerthesha, N.O. Pannaga, B.M. Priya, K.M. Naik, Ranjita Laxman Sahana, R. Department of Computer Science & Engineering Adichunchanagiri Institute of Technology Karnataka Chikkamagaluru India

ISBN: (纸本)9798350383867

This paper suggests a new method for classifying various types of date fruit. It combines supervised and unsupervised deep learning approaches. The main idea behind this approach is to automate the sorting process, which usually depends on manual labour. It is very costly and time- consuming. By using deep neural networks - particularly the VGG-F convolutional neural network and a network called PCANet - this new method aims to simplify the classification process. Discriminant Correlation Analysis (DCA) is used to blend features learned from both networks. It also helps in reducing the dimensionality, but with low computational complexity. This blend of supervised and unsupervised techniques allows for a better understanding of the data, which could lead to enhanced accuracy in classification. To assess the efficacy of the new method, a fresh benchmark dataset is introduced. This dataset consists of images of date fruit from around 20 different varieties. The dataset stands out due to its size and diversity, making it a valuable resource for the research community. It is also shared publicly to aid further research and bench marking. The experimental outcomes show the success of the DCA approach and how the fused features complement each other. Moreover, the proposed method surpasses various relevant methods, hence proving its efficiency in date fruit classification. In conclusion, this paper presents a unique method for classifying date fruit. It utilizes both supervised and unsupervised deep learning techniques, showing promising results through experiments on a vast and diverse dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Quorum-based Tier Approach: Integrating PBFT and PoS in a Hybrid Consensus Mechanism 3

Quorum-based Tier Approach: Integrating PBFT and PoS in a Hy...

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3rd International Conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Abinivesh, S. Isravel, Deva Priya Dhas, Julia Punitha Malar Division of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331532420

Blockchain technology is an advanced system centered around the concept of a shared and immutable ledger, emphasizing decentralization, security, and transparency. Transactions on a blockchain are securely linked through cryptographic hashes, forming blocks. A fundamental component of blockchain is the consensus mechanism, which comes in various types, each with its own advantages and limitations. For instance, Proof of Work (PoW) faces challenges related to energy consumption, while Proof of Stake (PoS) is susceptible to certain attacks. This paper proposes a hybrid consensus mechanism that integrates Practical Byzantine Fault Tolerance (PBFT) and Proof of Stake (PoS) using a quorum-based tier approach. This new hybrid model combines the refined features of both PoS and PBFT to create a hierarchical structure for reaching consensus. Nodes are categorized into high-tier and low-tier groups, with the consensus process primarily involving high-tier nodes. Low-tier nodes serve as backups in situations like network issues or node failures. Through experimentation it is evident that the hybrid consensus mechanism achieves a balance between decentralization and energy efficiency. The results demonstrate that this newly developed mechanism outperforms several existing consensus algorithms in terms of throughput and latency. © 2024 IEEE.

关键词： Decentralized control

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Punjabi Poetry Genre Classification Using Graph Neural Networks 1

Punjabi Poetry Genre Classification Using Graph Neural Netwo...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Saini, Ujjval Dutta, Kamlesh Sahoo, Subramanyam National Institute of Technology Hamirpur Computer Science and Engineering Department Hamirpur India

ISBN: (纸本)9798350307795

Punjabi poetry, famous for its multi-faceted culture and language, is unique in posing a challenge to computational linguistics and machine learning. A wide range of factors including writing styles, authors, emotions, and historical periods involved in the classification of Punjabi poems requires an innovative approach that takes full advantage of GNNs'(Graph Neural Networks) power. In this research, a dataset of 400 Punjabi poems curated from various online sources, was divided into four different genres: Festival;Humorous;Spirituality;and Patriotism. Our methodology involves thorough preprocessing, constructing an innovative text graph based on word probability(PMI - Pointwise mutual exclusion) and intra-poem weightings (TF IDF). It reflects intricately all those complex links between words, their cooccurring patterns with other words as well as their relationship to any given poem. It is then reconstructed as a graph-based representation in which words and their relations are expressed explicitly through the nodes of the graph. Next, text GCN(Graph Convolutional Networks) is used to analyze and classify the poems using graph structures. Unlike traditional classification methods, our GNN model gets much better results and has achieved an accuracy of 82.50%. © 2024 IEEE.

关键词： Classification (of information)

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Exploring the Performance of You only Look Once Model for Object Detection in VQA Images

Exploring the Performance of You only Look Once Model for Ob...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Asudani, Deepak Suresh Nagwani, Naresh Kumar Singh, Pradeep National Institute of Technology Department of Computer Science and Engineering Chhattisgarh Raipur India

ISBN: (纸本)9798331517953

Identifying objects is among the most crucial and complex problems in computer vision. The effectiveness of object detection tasks has significantly increased due to advancements in deep learning architectures. Object detectors are classified into single-stage (SSD) and two-stage (TSD). SSD model architecture evaluates the spatial region proposals in a single pass. TSDs employ a more complex architecture to focus on selective region proposals. Object detection is an essential component of visual question answering (VQA), which requires algorithms to respond to text-based inquiries about images. While many object detection algorithms are available, real-time application is still somewhat difficult. This paper explores the object detection approaches for VQA systems through a comparative analysis of various YOLO variants. The study compares the effectiveness of YOLOv8s and YOLOv8n architectures against TSD approaches in detecting object positions with bounding boxes. Experiments were conducted on the MS-COCO and two versions of the PASCAL-VOC (2007 and 2012) datasets. The paper outlines the data preprocessing steps required to convert source datasets to the YOLO model format. The experimental evaluations on the PASCAL-VOC dataset indicate that the TSD Faster-RCNN model, using ResNet101 pretrained architecture, achieves the highest mAP of 78.80. In contrast, the SSD YOLOv8s model achieves the highest mAP of 89.63 using the CSPDarkNet53 model architecture. YOLO is essential for real-time object detection, allowing VQA systems to process images in a single forward pass. The study concludes that the SSD YOLOv8s model efficiently identifies objects and generates bounding boxes with higher accuracy compared to other models. © 2024 IEEE.

关键词： Real time systems

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Video Harmony: Enhancing Emotional Well-being with Video Recommendations for Children with ASD 15

Video Harmony: Enhancing Emotional Well-being with Video Rec...

引用

15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raj, R. Imman pushpa Raj, A. Samson Arun Department of Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798331300579

Today's autistic children find it difficult to deal with people's emotions socially and tend to isolate themselves. VideoHarmony is an innovative approach designed to help children with Autism Spectrum Disorder (ASD) maintain their emotional health. Using convolutional neural networks and Haar Cascade to power advanced facial emotion recognition, the system can accurately understand facial expressions in real time. The video recommendation feature creates playlists that dynamically adjust based on the child's detected emotions through collaborative and content-based filtering algorithms. The user interface offers customizable options and an easy-to-use emotion panel for caregivers or parents. With the help of studies, user comments and observations, the positive effects of the system on the emotional states and activities of children are shown. The secret to VideoHarmony's success is its ability to provide personalized and responsive emotional support by combining cutting-edge technology with an intuitive user experience. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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Multi-Musical Instrument Recognition Neural Network 6

Multi-Musical Instrument Recognition Neural Network

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6th World Symposium on Artificial Intelligence, WSAI 2024

作者： Timouri, Bardia Aulakh, Ravdeep Cruz, Gathrean Dela British Columbia Institute of Technology Dept. of Computer Science Vancouver Canada

ISBN: (纸本)9798350374162

Artificial intelligence continues to evolve, particularly in the realms of natural language processing (LLMs), image generation, and task automation. Despite these advancements, multi-musical instrument recognition remains a challenging area with limited effective solutions. Addressing this, our research introduces an innovative methodology using a convolutional neural network (CNN) embedded within an artificial neural network framework. This method utilizes the OpenMIC-2018 dataset, meticulously refined for our purposes. We process entire songs by converting them into mel-spectrograms, which are instrumental in distinguishing subtle variations in pitch, dynamics, and timbre. Our approach sets a new benchmark in the field, adeptly identifying and categorizing up to 10 distinct musical instruments within complex audio recordings. It boasts an impressive F1 score of 56%. This significant achievement not only advances audio signal processing but also highlights the versatility and effectiveness of CNNs in handling sophisticated tasks like multi-instrument recognition. Our work serves as a stepping stone for future explorations in audio recognition, potentially paving the way for more nuanced and accurate audio analysis in various applications, from music production to sound engineering. © 2024 IEEE.

关键词： Spectrographs

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Lora-Based Data Transmission System for Smart Buildings 1

Lora-Based Data Transmission System for Smart Buildings

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Kumar, Sunny Verma, Karan National Institute of Technology Delhi Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

The necessity of ease and convenience for users, a Smart Building with complex applications needs to be operated through a centrally automated system such that the data can be transmitted over a long range. Hence, a Long-Range technology is needed to extend the low-power communication standard of an effective range of 13-15 Km with coverage across the entire city using a single Long Range (LoRa) gateway. Presently, developing countries use cellular technologies such as 3G, LTE, and 5G, but these technologies have their limitations because of their less coverage and high power consumption. Therefore, this paper aims to compress LoRa data before transmission to the LoRa gateway using deep learning method. Also, Channel-Net is trained to represent the noise in the channel. A Deep Neural Network (DNN) architecture is required to compress and decompress the LoRa data. Moreover, in the spectrogram analysis of the chirps, the LoRa technology is used to differentiate different device signals. The results show an error in the compression-decompression Auto Encoder and the classification of devices. © 2024 IEEE.

关键词： Gateways (computer networks)

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