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1 AI-Powered Facial Expression Analysis In Real Time: Unveiling Emotions 1

1 AI-Powered Facial Expression Analysis In Real Time: Unveil...

引用

1st IEEE International Conference on Electronics, Computing, Communication and Control Technology, ICECCC 2024

作者： Aishwarya, D. Sandeep, Ponduru Akhilesh Varma, Penmatsa V.P.S. Sathyabama Institute of Science and Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371802

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets. © 2024 IEEE.

关键词： Local binary pattern

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An Efficient Deep Learning Approach for Jute Pest Classification Using Transfer Learning 6

An Efficient Deep Learning Approach for Jute Pest Classifica...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Islam, Muhammad Tanvirul Rahman, Md. Sadekur Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Jute is considered as one of the most vital crops in the world. For some countries jute is the principal source of earnings and GDP. One of the primary elements influencing jute yield is jute pests. Accurate pest identification makes it possible to take prompt preventative action to minimize financial losses. Considering the fact, to classify jute pests, the study suggests different jute pest classification models, which are based on transfer learning. The best model offers high performance and resilience. A VCI-validated dataset comprising 7235 images has been utilized in the analysis. The dataset encompasses images classified into 17 distinct jute pest classes. The dataset is already divided into three categories train, test and validation. To increase the dataset size, data augmentation is applied to the training set. To improve performance, all the models were integrated with the transfer learning model. VGG 16, ResNetl0l, DenseNet201, InceptionV3, Xception, and MobileN etV2 were used to train the parameters on the publicly available ImageN et dataset followed by some customized dense layers. The models were assessed using different types of metrics, including confusion matrix, F1 score, precision, and recall. Compared to other models DenseNet201 outclassed other models, acquiring 97% accuracy. The fundamental information and technical support for jute pest classification are provided by this study. © 2024 IEEE.

关键词： Deep learning

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

引用

2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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Categorization of Brain Tumor on MR Images Using Deep Learning Models 2

Categorization of Brain Tumor on MR Images Using Deep Learni...

引用

2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Sree, Mundru Navya Singamaneni, Rajesh Sri, Modugula Kavya Velagapudi Ramakrishna Siddhartha Engineering College Dept of Computer Science Vijayawada India

ISBN: (纸本)9798350372847

Convolution Neural Network (CNN) algorithms have demonstrated notable capability in effectively analyzing human body image datasets obtained from MRI or CT with sufficient efficiency to direct medical operations. Although clinicians and radiologists can adeptly detect the presence of tumors and general abnormalities in brain scans, differentiating between specific tumor types typically requires significant time and effort. This study introduces an approach employing CNN algorithms for precise brain tumor classification into four distinct classes, leveraging deep learning models such as ResNet-50 and *** to classification, the dataset undergoes preprocessing using Adaptive Median Filter (AMF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) for denoising and contrast enhancement. The classification task involves categorizing images into four classes namely Glioma, Meningioma, Pituitary and Tumorless. The proposed methodology aims to streamline the analysis process by minimizing complexity while maximizing accuracy rates when compared to previous developments. © 2024 IEEE.

关键词： Tumors

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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through Automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 IEEE.

关键词： Sales

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Graph Neural Network Guided Music Mashup Generation

Graph Neural Network Guided Music Mashup Generation

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Wu, Xinyang Horner, Andrew Hong Kong University of Science and Technology Dept. Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350362480

Music mashups integrate elements from different songs to create surprising and engaging listening experiences. Typically, a mashup combines the vocal track of a base song with the instrumental tracks of complementary songs. Automating the production of mashups has been an area of research for decades. Traditional approaches utilize rule-based methods, such as matching tempo and harmonic similarity, to select optimal segments for mashup generation. More recent techniques leverage neural networks to classify segment compatibility. However, both approaches primarily focus on layering segments that are generally compatible, without ensuring their detailed integration and alignment with the vocal track. In contrast, we introduce a novel approach using Graph Neural Networks (GNNs) that learns to rearrange instrumental segments to better align with the base vocal track, resulting in more surprising and accurate music blends for mashup generation. Additionally, we conducted subjective listening tests to evaluate our generated mashups against a baseline model using the same song pairs and the original base songs, assessing our model's performance. Generated mashups used for evaluation can be found in https://***/***/. © 2024 IEEE.

关键词： Graph neural networks

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Metaheuristic Techniques Optimised LSTM Network for Improved Weather Prediction 2

Metaheuristic Techniques Optimised LSTM Network for Improved...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Mittal, Shweta Sangwan, Om Prakash Guru Jambheshwar University of Science and Technology Dept. of Computer Science and Engineering Hisar India

ISBN: (纸本)9798350360523

Tremendous amount of meteorological data is being generated on a daily basis from a number of sources such as weather stations, balloons, satellites, sensors etc. Timely weather prediction helps people plan everyday life events. Long Short Term Memory (LSTM) networks perform extremely well for capturing dependencies in time series datasets. Number of neurons in hidden layer highly impacts the performance of the LSTM network. The hit and Trial method for selecting the neurons consumes a lot of time and resources and might not lead to a global optimum solution. In this research work, two techniques, i.e. Genetic Algorithm optimized LSTM (LSTM-GA) and Artificial Bee Colony optimized LSTM (LSTM-ABC) have been proposed and implemented to automate the selection of the hidden layer's neurons for improved weather prediction. Proposed techniques have been implemented using DeepLearning4j library making the techniques scalable on clusters of machines. For evaluating the performance of proposed techniques, 15 years Brazil's weather dataset has been used. The experimental results proved that the proposed LSTM-GA and LSTM-ABC techniques have the reduced MAE value of 0.0074 and 0.0075 respectively while the LSTM network without any optimization has a relatively higher MAE value of 0.0086. © 2024 IEEE.

关键词： Long short-term memory

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A New Half-Bridge/Dual-Stacked-Switches Structured Electrolytic Capacitor-less AC/DC Bi-Directional On-Boad Charger for High-Voltage EV Battery

A New Half-Bridge/Dual-Stacked-Switches Structured Electroly...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Derakhshan, Siamak Lam, John York University Dept. Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

This paper introduces a novel half-bridge (HB)/dual-stacked-switches based electrolytic capacitor-less bidirectional AC/DC converter for high voltage (HV) electric vehicle (EV) systems. The proposed two-stage AC/DC converter incorporates a closed-loop Power Factor Correction (PFC) mechanism based on the dq model of the input LCL filter, ensuring high-quality input current with minimized Total Harmonic Distortion (THD) during both conversion and inversion operations of the converter. Moreover, the front-end HB PFC circuit operates in Continuous Conduction Mode (CCM) that results in the elimination of the high peak current in the switches. Additionally, a dedicated closed-loop control system is deployed in the dual-stacked-switches resonant converter to significantly reduce the output voltage ripple, thus eliminating the use of bulky electrolytic-type storage capacitors. Output voltage regulation is realized by using Variable Frequency Modulation (VFM). Soft-switching operation is guaranteed for all the switches and diodes using CLLC resonant circuit for both conversion and inversion modes of operation. The proposed circuit's performance is verified through a 1kW, 120Vrms/800Vdc proof-of-concept prototype. © 2024 IEEE.

关键词： Electrolytic capacitors

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Multivariate Analysis of Urban Air Pollution: Clustering and Patterns Across Major Asian Cities

Multivariate Analysis of Urban Air Pollution: Clustering and...

引用

2024 IEEE International Conference on Future Machine Learning and Data science, FMLDS 2024

作者： Rahman, Anika Khatun, Mst. Taskia Stamford University Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350391213

This study analyzes air pollution in Asian cities using the Global Air Pollution Data, consisting of 6,196 entries from 31 countries. Our primary goal is to identify pollution patterns through multivariate analysis and evaluate the effectiveness of six clustering algorithms: K-Means, Hierarchical Clustering, DBSCAN, Gaussian Mixture Models (GMM), Agglomerative Clustering, and Spectral Clustering. Performance was assessed using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index, WCSS, Cohesion, and Separation. The novelty of this work lies in the comparative analysis of these clustering methods on air pollution data, providing new insights into pollution dynamics across Asian cities. The analysis identified four distinct clusters- 'High Pollution', 'Moderate Pollution', 'Ozone-Dominated Pollution', and 'Low Pollution'- with K-Means proving to be the most effective. Significant disparities were found, particularly in South and East Asia, where countries like India, China, and Pakistan exhibited the highest pollution levels. Additionally, an examination of capital cities revealed specific pollution patterns and the primary pollutants-PM2.5, NO2, CO, and Ozone-aiding in identifying sources and affected regions. These findings underscore the need for targeted regional pollution control strategies. © 2024 IEEE.

关键词： Haze pollution

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Impact of Novel Pre-Processing Techniques on Retinal Images 2

Impact of Novel Pre-Processing Techniques on Retinal Images

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sahu, Deepti Kaur, Mandeep Sharda University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Glaucoma, a gradually developing eye condition that can damage the optic nerve, is one of the leading causes of irreversible blindness worldwide. The detection of glaucoma is required in its initial phases for the prevention of the disease. Several machine learning and deep learning models have been developed to detect the disease. These models require a preprocessed structured data set for better efficiency and accuracy of detection. The image datasets used for this purpose have been found with several issues of outliers and noise that limit the accuracy of the results. Hence, proper handling of the dataset in terms of pre-processing is vital. This paper presents a novel pre-processing technique for better glaucoma detection as it is necessary to remove outliers from the retinal images. The proposed 'EMF' model for pre-processing comprises Enhancement, Morphology, and Filtering of the images to get better results on the Retina. Drishti dataset has been used to evaluate the efficiency of the proposed technique. This dataset has several outliers that limit the accuracy, making it difficult to diagnose glaucoma. However, with the usage of the proposed technique, the dataset could be efficiently utilized for glaucoma detection. Compared to current state-of-the-art techniques, the findings from the proposed approach were shown to be better in terms of MSE, PSNR, and entropy. © 2024 IEEE.

关键词： Frequency domain Glaucoma Pre-processing Region of interest Retinal image Spatial domain

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