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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

3rd International Conference on Machine Intelligence and Sma...

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

ISBN: (纸本)9783031317224

The proceedings contain 59 papers. The special focus in this conference is on machine Intelligence and Smart Systems. The topics include: Chronic Kidney Disease Prediction and Interpretation Using Explainable AI;systematic Review and Analysis of Artificial Intelligence-Based Breast Cancer Classification and Detection;war of Tweets: Sentiment Analysis on Ukraine Russia conflict;Implementing HRRN for Evaluating Cloud Performance Using Reinforcement learning;using machine learning for Prediction of Obstructions for Indoor Location Systems;privacy Threats and Protection in Artificial Intelligence and machine learning;Combining Linguistic Information with BERT for Span Based End-to-End Aspect Based Sentiment Analysis;a Dimensionality Reduction Model: A Retrospective Approach on Dementia Triggering Parameters and Feature Ranking;effective Identification of Lung Diseases Using Few-Shot learning;comparative Study on Classification Based-Data Mining Techniques in Early Diabetes Prediction;optimize machine learning Model for Sentiment Analysis of Online Education During Covid-19 Pandemic;review on the Challenges and Future Directions of Deep learning-Based Techniques for Advance Prediction of Cardiac Attack;different Techniques for Detecting Plant Leaf Disease Using machine learning;proposed Framework of Extensive Humanoid Design Cycle and Recent Developments in Bipedal Walk;natural Language processing for Waste Management Using Public Opinions in Smart Cities;prediction of Diabetes During Pregnancy Through Fog Environment;empirical Wavelet Transform Grounded Poignant Ground Target Recognition and Classification by Seismic signal processing;A Powered-Up Classification of Disabling Distributed Network Cloud-Based Attacks Using MLPNN-BP and MLPNN-LM;stroke Prediction Framework Based on Missing Value Information and Outlier Detection by Using machine learning Techniques in E-Healthcare;magnetic Resonance Imaging Digitization for Brain Abnormality Recognition.

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

3rd International Conference on Machine Intelligence and Sma...

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3rd international conference on machine Intelligence and Smart Systems, MISS 2023

ISBN: (纸本)9783031691140

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ConvoWaste: An Automatic Waste Segregation machine Using Deep learning

ConvoWaste: An Automatic Waste Segregation Machine Using Dee...

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international conference on Robotics,Electrical and signal processing Techniques (ICREST)

作者： Md. Shahariar Nafiz Shuvra Smaran Das Md. Kishor Morol Abdullah Al Juabir Dip Nandi Department of Electrical and Electronic Engineering American International University-Bangladesh (AIUB) Department of Computer Science American International University-Bangladesh (AIUB)

Nowadays, proper urban waste management is one the biggest concerns for maintaining a green and clean environment. An automatic waste segregation system can be a viable solution to improve the sustainability of the country and to boost up the circular economy. This paper proposes a machine to segregate the waste into the different parts with the help of smart object detection algorithm using ConvoWaste in the field of Deep Convolutional Neural Network (DCNN), and image processing technique. In this paper, the deep learning and image processing techniques are applied to classify the waste precisely and the detected waste is placed inside the corresponding bins with the help of a servo motor-based system. This machine has the provision to notify the responsible authority regarding the waste level of the bins and the time to trash out the bins filled with garbage by using the ultrasonic sensors placed in each bin and the dual-band GSM-based communication technology. The entire system is controlled remotely through an android app in order to dump the separated waste in a desired place by its automation properties. The use of this system can aid the process of recycling resources that were initially destined to become waste, utilizing natural resources and turning these resources back into the usable products. Thus, the system helps to fulfill the criteria of circular economy through the resource optimization and extraction. Finally, the system is made to provide the services at a low cost with higher accuracy level in terms of the technological advancement in the field of Artificial Intelligence (AI). We have got 98% accuracy for our ConvoWaste deep learning model.

关键词： Deep learning Waste management Training Image processing signal processing algorithms signal processing Turning

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An Artificial Voice Box that Makes Use of Unconventional Methods of machine learning 1

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3rd international conference on Information Technology, InCITe 2023

作者： Chadha, Raman Singla, Sanjay Singh, Nongmeikapam Thoiba Department of Computer Science Engineering UIE Chandigarh University Punjab India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

Patients who are subjected to intensive medical treatments in order to identify voice problems may suffer a considerable amount during the diagnostic process. As a result, automated speech recognition and methods for disorder diagnosis have garnered a lot of attention in recent years, and both of these approaches have been effective. Voice recordings were collected from the Saarbrucken Voice Database to help with the completion of this inquiry. To de-noise and remove any potential noise, the signals are first preprocessed using a hybrid Wiener filter discrete wavelet transform (HWFDWT). Melanie Frequency Cestrum Cat Swarm Optimization is used to extract features while accounting for coefficients. Finally, the features are arranged by running the data through the modified optimized back propagation network (MOBPN) for the categorization of disordered voices. In all aspects of precision, recall, F1-score, and timespan, the classification strategy exceeds the present Support Vector machine (SVM) and Back Propagation Neural Network (BPNN) methodologies. Due to the cognitive speech system, people who lack the ability to speech can now convey their feelings and ideas to others in a form that can now communicate their thoughts and feelings to others in a way that is simple to grasp. It is a device that can record the electrical pulses the brain emits and then transform those records into synthetic speech. Provide a thorough explanation of the idea or solution you intend to develop. After recording the brain's electrical activity, it will be sent to a synthesizer for further processing. When it has completed decoding the signal, the synthesizer will transform it into a voice. After the voice has been decrypted, it is sent into an artificial voice box for further processing. Brain signals are employed to create an artificial voice. This voice is processed before being transmitted through the box. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte

关键词： Electroencephalography

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Implementation of Hybrid machine learning Algorithms in Classification of Real and Fake Profiles 3rd

Implementation of Hybrid Machine Learning Algorithms in Clas...

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3rd international conference on signal and Data processing, ICSDP 2023

作者： Mathew, Jeffrey Subhashini, N. Muthulakshmi, S. School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819795772

Social media platforms have been increasingly popular in recent times, with millions of people logging on daily to connect with one another. But there has also been an increase in the quantity of fake accounts in recent times, particularly during the pandemic when everyone was online, leaving them exposed to various attacks from bots, malicious users, and other fake accounts. They can be used for a wide range of serious undesirable activities, such as propagating false information, engaging in fraud, cyberbullying, upsetting democratic procedures, and endorsing extreme viewpoints. Differentiating between these types of accounts and the real ones has become critical. This research work seeks to define characteristics that distinguish authentic accounts from fraudulent ones and also to implement hybrid algorithms for improved performance and better distinguishing the genuineness of the profiles. This research uses symmetric uncertainty feature selection approach to identify attributes that distinguish a real account from a fake account. The performance of several legacy algorithms, including Random Forest, SVM, Decision Tree, Neural Network, and Logistic Regression, is compared after implementation. The most effective combination of these algorithms is then discovered by combining them using Voting Classifier. It has been found that the best hybrid model for effectively classifying fake and real profiles was a combination of Random Forest and Neural Network. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fake profiles Hybrid algorithms Symmetric uncertainty Voting classifier

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Convolutional neural network-based signal discrimination for P300 brain-computer interface

Convolutional neural network-based signal discrimination for...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yongzhi Liao Liumei Zhang Yaoxin Wang School of Computer Science Xi’an Shiyou University Xi’an China

The P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. This paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. The test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects.

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System Identification-informed Transparent and Explainable machine learning with Application to Power Consumption Forecasting

System Identification-informed Transparent and Explainable M...

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Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), international conference on

作者： Hua-Liang Wei Dept. Automatic Control and Systems Engineering Energy Institute Centre of Machine Intelligence Centre for Signal Processing and Cpmplex Systems The University of Sheffield Sheffield UK

System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable machine learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.

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EEG mental arithmetic task levels classification using machine learning and deep learning algorithms

EEG mental arithmetic task levels classification using machi...

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international Symposium on Artificial Intelligence and signal processing (AISP)

作者： Krashana Nirde Manasa Gunda Ramchandra Manthalkar Suhas Gajre Electronics and Telecommunication Engg.SGGSIE&T SRTMU Vishnupuri Nanded Nanded India

EEG signals are often-used modalities for monitoring mental arithmetic performance. In order to learn robust characteristics and predict the level of classification from EEG, we provide a deep neural networks and machine learning architecture. In this research, we considered online public dataset related to mental arithmetic task. EEG dataset recording are stored last 3 minutes of resting period (180 sec before task) and first 1 minute of the mental arithmetic task period (60 sec-while doing task). The task involves series of subtraction performed by 36 participants. The participants were divided among Good (1) and Bad (0) based on task level. We used machine learning and deep learning methods to classify three different task levels. (i.e., no task, normal task, and difficult task). In algorithm 1 we extracted five features like skewness, kurtosis, mean, standard deviation and variance from the EEG signals and applied as input to the classifiers. Here three machine learning classifiers used for three task levels classification like SVM, KNN and Decision Tree. KNN obtained high classification with 91% whereas SVM obtained S9% and Decision Tree obtained 65%. Algorithm 2 uses ANN and LSTM, two deep learning algorithms, to learn directly from raw EEG data. For better performance we used optimization techniques. ANN used rmsprop and obtained the classification accuracy of 96.80% whereas LSTM uses Adam and SGD and obtained the classification accuracy of 94%. Therefore, when raw data applied to deep learning algorithms can perform well when grouping of handmade features given to a machine learning algorithm.

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Research on Intelligent Auxiliary Review of Power Grid 3D Design Based on BP Neural Network

Research on Intelligent Auxiliary Review of Power Grid 3D De...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Dongmei Fan Wendou Yan Yi Liang Zhi Cheng Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd. Guangzhou China

BP neural network, or back propagation neural network, is a machine learning algorithm widely used in pattern recognition, function approximation, signal processing and other fields. In recent years, with the expansion of power grid scale and the increase of complexity, BP neural network plays an increasingly important role in the three-dimensional processing of power grid. This paper mainly discusses the application of BP neural network in intelligent assistance, evaluation and optimization of power grid.

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An Iterative Posterior Regularized NMF-Based Adaptive Wiener Filter for Speech Enhancement 3rd

An Iterative Posterior Regularized NMF-Based Adaptive Wiener...

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3rd international conference on machine learning, Image processing, Network Security and Data Sciences, MIND 2021

作者： Yechuri, Sivaramakrishna Vanambathina, Sunny Dayal School of Electronics Engineering VIT-AP Amaravati India

ISBN: (纸本)9789811958670

In this paper, a novel single-channel speech enhancement algorithm based on an iterative posterior regularized nonnegative matrix factorization (NMF)-based adaptive Wiener filter is proposed. The statistical-based approaches’ performance is poor under the non-stationary background noise conditions. Prior information is needed for template-based methods, but performance-wise template-based methods are robust and better in presence of non-stationary compared to statistical model-based approaches. To overcome the limitations of both, we propose a method that combines the iterative posterior regularized NMF and an adaptive Wiener filtering method. In the proposed method, prior distributions are used for transformed domain spectral magnitudes of speech and noise components to model an iterative posterior NMF. The magnitude of the spectral component of speech and noise is considered as an inverse gamma, Gaussian distribution. The samples of speech and noise are well fit in those distributions to provide a better adaptive estimation of the necessary statistics of those distributions to get a natural regularization of the NMF criterion. An adaptive Wiener filter technique derives the adaptive factor (α) based on the signal-to-noise ratio (SNR) level for the Wiener filter gain. The adaptive factor minimizes the estimation errors of the Wiener filter and improves the speech quality by adjusting the base vector weights based on estimated noise and speech levels. The proposed method outperforms other benchmark algorithms in terms of the source to distortion ratio (SDR), short-time objective intelligibility (STOI), and perceptual evaluation of speech quality (PESQ). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： signal to noise ratio

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