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3rd International Conference on computing and Communication Networks, ICCCN 2023

作者： Gayathri, R. Sheela Sobana Rani, K. Aravindhan, K. Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology Coimbatore India Karpagam College of Engineering Coimbatore India School of Computing and Information and Technology Reva University Bangalore India

ISBN: (纸本)9789819726707

Speech categorisation is a broad study that has attracted a lot of interest recently. Understanding other people's emotions and responding appropriately is the largest distinction between robots and people. Speech signals are categorised in this article using both conventional machine learning methods and deep learning algorithms. Speech emotion is categorised using the interactive emotional binary motion capture (IEMOCAP) and Ryerson affective voice and audio-visual database of songs (RAVDESS) databases. The expanded dataset is mixed with the voice signals once they have been transformed into spectrograms. This model incorporates CNN + LSTM, attention, and a novel type of spectrogram frequency distribution based on the spectrogram’s properties. Mel-frequency cepstral coefficients guarantee improved speech feature performance in tasks requiring emotion categorisation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spectrographs

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A Sentimental Analysis Approach for Personalized Drug Recommendations Using Machine Learning 1

A Sentimental Analysis Approach for Personalized Drug Recomm...

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1st IEEE International Conference on computing and Data Science, ICCDS 2024

作者： Vijayaraj, A. Murugan, V.P. Jebakumar, R. Gaurav, N.V. Santhosh, V. Shai Kumar, R. R.M.K. Engineering College Department of Information Technology Chennai India Panimalar Engineering College Department of Mathematics Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350365337

Since the virus that causes corona disease was discovered, it has gotten more difficult to find medical professionals with the appropriate licenses, including doctors, nurses, diagnostic equipment, and medications. Due to the intense grieving, a substantial number of people in the medical industry pass away. Because of the scarcity, a lot of individuals began self-medicating without first consulting a doctor, which made the health problem worse. There are numerous well-established applications for machine learning, and recent years have witnessed a growth in the pace and breadth of computerization-related research and development. The goal of this research is to develop a method for recommending medications that might greatly reduce the obligations of specialists. In this work, we built a drug recommendation system that makes estimations about the emotional of patient reviews using a variety of different vectorization techniques, such as using a Bow, The TF-ID Word 2 Vector, or Manually Feature Analysis. The predicted emotions were graded using accuracy, remembrance, flscore, exactness, and the area beneath the slope (AUC). The results show that, when weighed against the other models, the classification technique Linear SVC + The TF -I vector process has the greatest accuracy. © 2024 IEEE.

关键词： Recommender systems

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Aircraft parameter estimation using a stacked long short-term memory network and Levenberg-Marquardt method

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Chinese Journal of Aeronautics 2024年第2期37卷 123-136页

作者： Zhe HUI Yinan KONG Weigang YAO Gang CHEN School of Aerospace Science and Technology Xidian UniversityXi’an 710071China Computation Aerodynamics Institution China Aerodynamics Research and Development CenterMianyang 621000China Faculty of Computing Engineering and MediaDe Montfort UniversityLeicester LE19BHUnited Kingdom School of Aerospace Engineering Xi’an Jiaotong UniversityXi’an 710049China

To effectively estimate the unknown aerodynamic parameters from the aircraft’s flight data,this paper proposes a novel aerodynamic parameter estimation method incorporating a stacked Long Short-Term Memory(LSTM)network model and the Levenberg-Marquardt(LM)*** stacked LSTM network model was designed to realize the aircraft dynamics modeling by utilizing a frame of nonlinear functional mapping based entirely on the measured input-output data of the aircraft system without requiring explicit postulation of the *** LM method combines the already-trained LSTM network model to optimize the unknown aerodynamic *** proposed method is applied by using the real flight data,generated by ATTAS aircraft and a bio-inspired morphing Unmanned Aerial Vehicle(UAV).The investigation reveals that for the two different flight data,the designed stacked LSTM network structure can maintain the efficacy of the network prediction capability only by appropriately adjusting the dropout rates of its hidden layers without changing other network parameters(i.e.,the initial weights,initial biases,number of hidden cells,time-steps,learning rate,and number of training iterations).Besides,the proposed method’s effectiveness and potential are demonstrated by comparing the estimated results of the ATTAS aircraft or the bio-inspired morphing UAV with the corresponding reference values or wind-tunnel results.

关键词： Parameter estimation LSTM network model LM method Aerodynamic parameters Flight data Aircraft dynamics modeling Network prediction capability Network parameters

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An Improved Convolutional Networks Model for Carbon Emission Prediction in Power Systems

An Improved Convolutional Networks Model for Carbon Emission...

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2024 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2024

作者： Cai, Dongsheng Acen, Caroline Ukwuoma, Chiagoziem C. Zhang, Nan Liu, Wei Zhang, Zhenyuan Huang, Qi College of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu China School of Computing and Cyber Security Chengdu University of Technology Chengdu China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Information Engineering Southwest University of Science and Technology Mianyang China

ISBN: (纸本)9798350352290

In response to the imperative need for carbon reduction, tracking the carbon footprint of power systems has emerged as a critical concern. Traditional methods have struggled to accurately capture the nuances of carbon emissions, necessitating a fresh approach. To address this gap, the study introduces a pioneering deep learning-based model. We contend that the rapid advancement of deep learning offers a solution by harnessing neural network models, particularly the temporal convolutional networks (TCN) and gated recurrent units (GRU). These models are adept at handling the intricate patterns and dependencies inherent in carbon emission data, which exhibit both short-term fluctuations and long-term trends. Sequel to that, we propose a novel TCN-GRU-ATT neural network model designed explicitly for carbon footprint data. This innovative model combines TCN, GRU, and attention mechanisms to comprehensively capture both short-term variations and long-term dependencies within carbon emission patterns. Using power generation data from a gas-fired unit within the power grid as a foundation, the model calculates carbon emissions across various time intervals. Results demonstrate the model's exceptional performance, with a low Mean Absolute Percentage Error (MAPE) of 1.7123%, a Root Mean Square Error (RMSE) of 8.8436 kg/CO2, and a high Coefficient of Determination R2 of 0.9802. © 2024 IEEE.

关键词： Low emission

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Long-Term Load Forecasting Based on Hybrid CEEMDAN-SSA-BiGRU-Attention Model 6

Long-Term Load Forecasting Based on Hybrid CEEMDAN-SSA-BiGRU...

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6th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2024

作者： Lin, Jiajian Yeo, Lit Yen Sheng, Yuting Ektesabi, Mehran Motamed Tavalaei, Jalal Afrouzi, Hadi Nabipour Swinburne University of Technology Faculty of Engineering Computing and Science Kuching Malaysia The Eleventh Metallurcial Construction Group CO. Ltd Department of Engineering Management Liuzhou China School of Science Computing and Engineering Technologies Swinburne Univerdity of Technology HawthornVIC Australia

ISBN: (纸本)9798350389692

Electric load is characterized by uncertainty, randomness, and volatility, so accurate prediction will be challenging. This paper thus comes up with a hybrid prediction model called CEEMDAN-SSA-BiGRU Attention to predict the load. First, it preprocesses the load data from a selected power grid in Chicago, USA, from 2012 to 2015. Then, the load data is divided into a series of IMF sequences combined with the original data, shaping a new feature set using CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise). SSA (Sparrow search algorithm) optimized the hyperparameter of the BiGRU Attention network, and the network processed the new feature set to make predictions. The results from the experiments show that the judgment coefficient (R-square, R2) of the model, the root mean square error (RMSE), and the mean absolute error (MAE) are 90.2%, 51618, and 39470, respectively. The three indexes are much better than other models and have a higher forecast precision degree and stable ability, which shows the model's superiority in long-term load forecasting.. © 2024 IEEE.

关键词： Prediction models

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Adaptive Genghis Khan Shark based convolutional denoising autoencoder for quality enhancement of satellite images

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International Journal of Information technology (Singapore) 2025年 1-14页

作者： Rajalakshmi, M. Subramanian, R. S. Sankara Tamilselvan, S. Bhuvanesh, A. Department of Computing Technologies School of Computing SRM Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Sri Eshwar College of Engineering Tamil Nadu Coimbatore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli India

Satellite image processing often enhances feature perception and visibility for exact feature identification. Additionally, the change improves remote sensing data quality and clarity. Due to their long distances, these photos often have weather-related noise and distortion that must be fixed. This paper addresses these challenges with a Deep Learning (DL) satellite image denoising technique. An Adaptive Genghis Khan Shark Algorithm (AGKSA) and a Convolutional Denoised Autoencoder are used to improve satellite images in this study. The CDAE settings are optimized using the AGKSA while keeping important image attributes. AGKSA is used in this hybrid model to optimize CDAE performance under various noise conditions. Experimental results showed that the proposed model outperforms traditional image denoising approaches in satellite imagery feature extraction and visual quality. In very precise image processing applications like environmental monitoring and land use classification, the CDAE-AGKSA works well. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Adaptive Genghis Khan algorithm Convolutional denoised autoencoder Satellite image enhancement Satellite image processing

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An Imperceptible Adversarial Attack Against 3-D Object Detectors in Autonomous Driving

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IEEE Internet of Things Journal 2025年第12期12卷 21404-21414页

作者： Wang, Yizhou Wu, Libing Jin, Jiong Wang, Enshu Zhang, Zhuangzhuang Zhao, Yu Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Swinburne University of Technology School of Engineering MelbourneVIC3122 Australia Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Hong Kong Department of Computer Science Hong Kong

As LiDAR-based 3-D object detection gains attention, existing research on point cloud adversarial attacks has exposed vulnerabilities in 3-D neural network models, which can further impact the reliability of perception systems in autonomous driving. However, current adversarial attacks primarily focus on the point cloud classification tasks, while detection tasks are more challenging to attack because they involve the localization of multiple targets and the sparse distribution of objects. Existing methods often implement attacks by adding global perturbations to the point clouds, which results in poor attack performance and lack of imperceptibility. In this article, we investigate the robustness of 3-D object detectors against adversarial examples. We propose a novel imperceptible attack method to generate 3-D adversarial point clouds. First, we introduce the saliency map for the point clouds, assigning the unique value to each point to measure its importance to the model’s discrimination results. These salient points are then aggregated and adaptively matched to different attack areas corresponding to different targets. Next, we design an optimization-based attack algorithm to generate adversarial point clouds, which are supervised by a dual-loss function consisting of the detection loss to ensure attack effectiveness and the distance loss to limit gap with the original point cloud. Finally, we conducted experiments on two real-world datasets, the KITTI and Waymo Open datasets, to evaluate the proposed attack method. Extensive experiments demonstrate that our attack method achieves average attack success rates of 83.07% and 77.11% on two datasets against seven 3-D object detectors with minimal perturbations. Additionally, the generated adversarial point clouds exhibit strong transferability across multiple mainstream detectors. © 2014 IEEE.

关键词： Object detection

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ARIMA Modeling for Reliable Potable Water Identification and Quality Prediction 5

ARIMA Modeling for Reliable Potable Water Identification and...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Selvaraj, P. Shalma, H. Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur603203 India Sphoorthy Engineering College Department of Artificial Intelligence and Machine Learning Telengana Hyderabad India

ISBN: (纸本)9798331504403

This work aims to employ machine learning methods and visualization techniques for evaluating water quality by measuring its potability, a numerical indicator of water quality. The work utilizes water quality parameters to determine the stability condition using pH, Temperature, Dissolved Oxygen, Conductivity, Nitrate, Fecal Coliform, and Total Coliform to evaluate the overall water quality. ARIMA is utilized to identify and categorize water potability based on its features. Using Tableau's interactive dashboarding, we created an interactive dashboard to visually simplify water potability metrics in India. The study utilized both real datasets from various locations around the country using government data sourced directly from the Central Pollution Control Board (CPCB). Based on the results of the experiments, the ARIMA was found to be superior to other classifiers in accurately predicting water potability. © 2024 IEEE.

关键词： Water pollution control

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Selection of qualified reviewers and avoiding conflicts of interest within the issue of compromised peer review in paper retractions using an ontology-based decision support system

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International Journal of Metadata, Semantics and Ontologies 2023年第4期16卷 298-314页

作者： Al-Hidabi, Mymoona Dawood Abdulmalek Lee, Yunli Yusoff, Zaharin Teh, Phoey Lee Chia, Wai Chong Uwasomba, Chukwudi School of Engineering and Technology Sunway University Jalan Universiti Selangor No. 5 Bandar Sunway Darul Ehsan47500 Malaysia School of Engineering and Technology Sunway University Jalan Universiti Selangor No. 5 Bandar Sunway Darul Ehsan47500 Malaysia IRDI International Medical University Kuala Lumpur Malaysia Department of Cyber and Computing Faculty of Art Computing and Engineering Wrexham University Wales Wrexham United Kingdom School of Computing and Communications Open University Buckinghamshire Milton Keynes United Kingdom

Paper retractions are rising due to the absence of reliable tools to detect and identify fraudulent articles before publication. This paper proposes an ontology-based decision support system to prevent compromised peer review by carefully selecting qualified reviewers and avoiding potential conflicts of interest. There are three main contributions: (i) formulating the criteria for the selection of qualified reviewers as well as for recognising potential conflicts of interest;(ii) designing the ontology-based decision support system;(iii) designing and performing the methodology for validation. A pilot test with 30 computer science experts is conducted to determine qualified reviewers and conflict criteria, including the design of ontologies structure. Subsequently, three selected computer science experts and journal editors are requested to evaluate a set of test data as the ground truth. Overall results show that the proposed solution achieves 91% accuracy in qualified reviewer selection and 94% accuracy in conflict-of-interest detection. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Knowledge representation

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Exploring the Effects of Glasses on mmWave: Attenuation, Thermal Impact, and Implications for 5G and Beyond Mobile Technologies 39

Exploring the Effects of Glasses on mmWave: Attenuation, The...

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39th International Conference on Information Networking, ICOIN 2025

作者： Joy, Md Jannatul Rakib Foroughimehr, Negin Yavari, Ali Swinburne University of Technology 6G Research and Innovation Laboratory School of Science Computing and Engineering Technologies Melbourne Australia

ISBN: (纸本)9798331506940

The growing demand for higher bandwidth has pushed mobile carrier frequencies into the millimeter-wave (mmWave) spectrum such as high-band 26 GHz, to meet the requirements of 5G networks and advanced communication technologies. While mmWave frequencies provide the necessary bandwidth for high-speed data transmission, they pose significant challenges, especially in urban environments where signals interact with common building materials like glass. This study focuses on the effects of 26 GHz mm Wave propagation through various glass types commonly found in modern urban architecture. Our experiment measured electric field (E-field) strength loss, attenuation, and temperature variations as the waves passed through different glass samples. Results show that highly reflective and coated glass types, such as Low-E and silver reflective glass, caused significant E-field strength reduction reaching up to 9 3%. The experiment also revealed a noticeable temperature increase in some glass samples, further influencing signal degradation. These findings underline the importance of consideration of the glass materials as they can cause signal loss, impacting reliability of the 5G and beyond connectivity in urban environments. © 2025 IEEE.

关键词： Light propagation

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