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6th International Conference on Electrical, Control and Instrumentation engineering, ICECIE 2024

作者： Aranda, Jonalyn Mae E. Lazana, Cristian S. Master of Science in Engineering - Specialized in Electronics Engineering Polytechnic University of the Philippines Technological University of the Philippines Manila Philippines Master of Science in Electronics Engineering Polytechnic University of the Philippines De La Salle University Manila Philippines

ISBN: (纸本)9798350380040

Pyrolysis, a viable method for producing valuable products like tire pyrolytic oil, addressing the environmental problems associated with the improper disposal of used tires. However, additional refinement is necessary to extract lighter fractions of the oil, which have higher economic value. This study focuses on the design and development of a small-scale atmospheric distillation system specifically engineered to characterize pyrolytic oil from waste tires. The system is designed to separate the lighter fractions of tire pyrolytic oil and analyze the distilled oil in accordance with ASTM standards, while also conducting physical and spectral characterization using a real-time sensor network. The real-time sensor network, which controls the system for characterizing tire pyrolytic oil, maintains precise temperature control within the 30-60°C range. It uses gravitydriven fluid movement, eliminating the need for mechanical pumps. Key parameters like density, pH level, and water content are carefully monitored and displayed on both a control panel and mobile devices. With the use of advanced sensors and calibration methods, the system ensures accurate measurements, addressing limitations found in existing technologies. The data collected during the oil characterization were compared with results from a standard testing laboratory in the Philippines accredited by the Department of Energy (DOE) using ASTM standards. These comparisons highlight the system's effectiveness in improving the reliability and usability of tire pyrolytic oil for various industrial applications. This innovative approach provides a cost-effective and efficient solution for refining tire pyrolytic oil and characterizing its physical and spectral properties, underscoring its significant potential for industrial use. © 2024 IEEE.

关键词： ASTM standards

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Development of Real-Time Sensor Network for Characterization of Pyrolysis Oil from Waste Tires

Development of Real-Time Sensor Network for Characterization...

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International Conference on Electrical, Control and Instrumentation engineering (ICECIE)

作者： Jonalyn Mae E. Aranda Cristian S. Lazana Master of Science in Engineering - Specialized in Electronics Engineering Polytechnic University of the Philippines/ Technological University of the Philippines Manila Philippines Master of Science in Electronics Engineering Polytechnic University of the Philippines /De La Salle University Manila Philippines

ISBN: (数字)9798350380040

ISBN: (纸本)9798350380057

关键词： Temperature sensors Temperature measurement Oils Tires Real-time systems Temperature control Reliability Usability Standards Testing

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Optimization of Cooperative RelayingMolecular Communications for Nanomedical Applications

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Computer Modeling in engineering & sciences 2024年第2期138卷 1259-1275页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Heba M.El-Hoseny Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61111Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)OsloNorway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer science The Higher Future Institute for Specialized Technological StudiesEl ShoroukEgypt

Recently,nano-systems based on molecular communications via diffusion(MCvD)have been implemented in a variety of nanomedical applications,most notably in targeted drug delivery system(TDDS)***,because the MCvD is unreliable and there exists molecular noise and inter symbol interference(ISI),cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells,especially if the separation distance between the nano transmitter and nano receiver is *** this work,we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme,while accounting for blood flow effects in terms of drift *** fractions of the molecular drug that should be allocated to the nano transmitter and nano relay positioning are computed using a collaborative optimization problem solved by theModified Central Force Optimization(MCFO)*** the previous work,the probability of bit error is expressed in a closed-form *** is used as an objective function to determine the optimal velocity of the drug molecules and the detection threshold at the nano *** simulation results show that the probability of bit error can be dramatically reduced by optimizing the drift velocity,detection threshold,location of the nano-relay in the proposed nano system,and molecular drug budget.

关键词： Nanomedical system molecular communication cooperative relay optimization

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An Intriguing and Predictive Machine Learning Method for the Detection and Identification of Diabetes 12th

An Intriguing and Predictive Machine Learning Method for the...

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12th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2024

作者： Srinivasula Reddy, A. Raja, Mandapati Tejaswini, V. Ramesh Kumar, R. Sri Vidya, G. Ragini, K. Department of Electrical and Electronics Engineering CMR Engineering College Telangana Hyderabad India Department of Electronics and Communication Engineering CMR Engineering College Telangana Hyderabad India CMR Technical Campus Telangana Hyderabad India Department of Master of Business Administration CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601387

According to WHO estimates from 2016, diabetes has been named one of the chronic illnesses with the greatest rate of growth and is the seventh largest cause of mortality. For a clinically significant outcome, early identification of diabetes is always preferred due to the relatively long asymptomatic phase. Diabetes is a leading cause of stroke, heart attacks, kidney failure, blindness, and amputation of lower limbs. If it is discovered in its early stages, they could be prevented. The digital age has made an enormous quantity of clinical data available, which has allowed deep learning algorithms to provide accurate medical diagnosis and prognosis. To train the machine to predict it, we employ the diabetes dataset. The dataset was studied using the random forest, deep neural network, and logistic regression algorithms, together with a DNN that contained embedded data for the categorical features. With a F1 rating of 1.0 on test data, the DNN with insertions correctly predicts the majority of test set examples, or almost 100% of the cases. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Smart Aeroponic Farms with IoT-Enabled Efficient Automation and Monitoring 2

Smart Aeroponic Farms with IoT-Enabled Efficient Automation ...

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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Jeyabharath, R. Tamilvani, P. Karthikeyan, G. Vijayakumar, P. Rohini, J. Mohammadha Hussaini, M. Kavin Kumar, K. K.S.R. College of Engineering Department of Electrical and Electronics Engineering Tiruchengode India M.P.Nachimuthu M.Jaganathan Engineering College Department OfElectrical and Electronics Engineering Erode India Sona College of Technology Department of Electrical and Electronics Engineering Salem India Kongu Engineering College Master of Computer Applications Erode Perundurai India Erode Sengunthar Engineering College Department of Computer Science and Engineering Thuduppathi638057 India Government College of Engineering Department of Electrical and Electronics Engineering Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349221

The Internet of Things (IoT) has enabled the adoption of automated aeroponics equipment, which has transformed our understanding of horticulture. Aeroponics is a method of vegetative production that does not use soil. Plant cultivation takes place in an airborne or vapour environment. The use of Internet of Things (IoT)-enabled aeroponics devices allows farms to autonomously monitor and manage variables such as temperature, lighting, humidity, and fertiliser concentrations. This allows for real-time adjustments to be made to optimize plant growth and yield. The IoT-enabled automated aeroponics system utilizes sensors and actuators to collect data on environmental conditions and automate the delivery of nutrients and water to the plants. Additionally, the system can be programmed to send alerts and notifications to farmers in case of any irregularities or issues that require attention. This level of automation not only increases efficiency and productivity but also reduces the need for manual labor and minimizes resource wastage. The IoT-enabled automated aeroponics systems have the potential to revolutionize farming by providing a more sustainable and efficient method of crop production. The computer system uses environmental elements to evaluate the results of aeroponics studies. The decision was made to use an automated aeroponics methodology to develop maize plants. According to the findings of this study, applying this method could potentially reduce labour expenses while increasing the value of the product. © 2024 IEEE.

关键词： Internet of things

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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Maximum Energy Productivity for Concurrent Wireless Data and Power Shifting-Enabled IoT Network with Energy Coordination

Maximum Energy Productivity for Concurrent Wireless Data and...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Joseph, Sneha Sankar, R. Royappa, Angelina Anandakumar, D. Gururaj, D. Karthikeyan, K.V. Department of Electronics and Communication Engineering R.M.K. Engineering College Chennai India Department of Master of Computer Application S.A Engineering College Chennai India Department of Electronics and Communication Engineering Velammal Engineering College Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Electronics and Communication Engineering Rajalakshmi Engineering College Chennai India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350334357

The online phase may be successfully extended by simultaneous wireless data, internet of things (IoT) components, and sophisticated technologies. The development support base station is developed to accomplish the exchange of renewable electricity to manage the volatility of power generation by the hybrid access points. In this research, we jointly investigate the cooperative SWIPT-enabled IoT systems. While maximizing the program's energy consumption, we must also adhere to maximize the transmission limits, thermoelectric generator restrictions, and customer quality of service (QoS) requirements. We collaborate to find solutions to the challenges of power-sharing, period shifting, and ecological collaboration. The incessant algorithm is employed to address the load distribution and duration swapping problems, the matching algorithm is employed to fix the cooperation agreement issue, and since this trouble is a nonlinear optimization issue, it is challenging to address directly. Instead, we are using the interchanging differential technique. The outcomes of the simulations demonstrate that the suggested algorithm performs with a considerable advantage in terms of energy conservation compared to the comparative method. Also, it has been shown that using energy collaboration technologies can reduce the amount of power a system uses and make it run better. © 2023 IEEE.

关键词： Energy utilization

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Fingerprint definition for song recognition using Machine Learning Algorithm

Fingerprint definition for song recognition using Machine Le...

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2023 International Conference on Smart Systems for Applications in Electrical sciences, ICSSES 2023

作者： Arunakumari, B.N. Shashidhar, R. Sahana, B. Jagadamba, G. Manjunath, A.S. Roopa, M. Bms Institute of Technology and Management Dept. of Computer Science and Engineering Bengaluru India Jss Science and Technology University Dept. of Electronics & Communication Engineering Mysuru India Rv College of Engineering Dept. of Electronics & Communication Engineering Bengaluru India Siddaganga Institte of Technology Dept. of Information Science and Engineering Tumkuru India Jss Science and Technology University Dept. of Master of Computer Applications Mysuru India Dayananda Sagar College of Engineering Dept. of Electronics & Communication Bengaluru India

ISBN: (纸本)9798350347296

A condensed content-based signature that summarises an audio recording is known as an audio fingerprint. Audio The capability to recognize music regardless of design and without the use of meta-data or watermark embedding has made fingerprinting technologies popular. In. tegrity checking, watermark support, and content-based aural retrieval are additional applications for fingerprinting. The various methods of fingerprinting have been discussed using various justifications and terminologies, such as pattern matching, multimedia information retrieval, or cryptography. It is a distinct and condensed digest created using perceptually important elements of a video. Audio material can be monitored using fingerprinting technologies without requiring embedding of metadata or watermarks. However, audio fingerprinting has extra applications The algorithm is trained using thousands of mini audio speech commands. In order to improve the training efficiency, audio features are extracted during a pre-processing. The accuracy of the developed model is 83.7%. © 2023 IEEE.

关键词： Cryptography

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Deep Learning for Energy Demand Forecasting in Electric Vehicle Charging Stations 5

Deep Learning for Energy Demand Forecasting in Electric Vehi...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Hanumanthakari, Sudheer Anita, S. Christiana, M. Beulah Viji Srinivasan, Vellayan Kannan, Sathish Kumar, S.K. Nandha ICFAI Foundation for Higher Education Faculty of Science and Technology Department of Electronics and Communication Engineering Hyderabad501203 India R.M.K. Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Master of Business Administration Tamil Nadu Chennai600123 India S. A. Engineering College Department of Master of Business Administration Tamil Nadu Chennai600077 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of VLSI Microelectronics Tamil Nadu Chennai602105 India PSNA College of Engineering & Technology Department of Electrical and Electronics Engineering Tamil Nadu Dindigul624 622 India

ISBN: (纸本)9798350395235

Electric vehicle (EV) growth in the Indian market has resulted in an enormous rise in energy demand. Because EVs are becoming more popular, effective charging infrastructure and energy management technologies are essential. Intelligent energy management systems are necessary to reduce the risks of power outages and disruptions resulting from the increasing number of EVs on the road. This paper proposes a novel Deep Learning (DL) model called Fractional Gradient-Based Recurrent Neural Network (FG-RNN) for predicting energy demand at electric vehicle charging stations (EVCS) to solve these challenges. This study examines and preprocesses data from an adaptable charging network (ACN). The proposed FG-RNN model was tested against two DL models: Deep Neural Network (DNN) and Long Short-Term Memory (LSTM). The FG-RNN model is one of the better solutions because of its high R-squared (R2) value of 97.682%. Furthermore, the model has a remarkably low Mean Absolute Percentage Error (MAPE) of 2.51% and a Mean Squared Error (MSE) of 0.072. Based on these results, the FG-RNN model appears to be a promising option for intelligent energy management systems for predicting energy consumption for EVCS. © 2024 IEEE.

关键词： Electric vehicles

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Exploring Sequential Feature Selection in Deep Bi-LSTM Models for Speech Emotion Recognition

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Computers, Materials & Continua 2024年第2期78卷 2689-2719页

作者： Fatma Harby Mansor Alohali Adel Thaljaoui Amira Samy Talaat Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesCairo12622Egypt Department of Computer Science and Information College of Science at Zulfi Majmaah UniversityP.O.Box 66Al-Majmaah11952Saudi Arabia Preparatory Institute for Engineering Studies of Gafsa ZarrougGafsa2112Tunisia Computers and Systems Department Electronics Research InstituteCairo12622Egypt

Machine Learning(ML)algorithms play a pivotal role in Speech Emotion Recognition(SER),although they encounter a formidable obstacle in accurately discerning a speaker’s emotional *** examination of the emotional states of speakers holds significant importance in a range of real-time applications,including but not limited to virtual reality,human-robot interaction,emergency centers,and human behavior *** identifying emotions in the SER process relies on extracting relevant information from audio *** studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients(MFCCs)due to their ability to capture the periodic nature of audio signals *** these traits may improve their ability to perceive and interpret emotional depictions appropriately,MFCCS has some *** this study aims to tackle the aforementioned issue by systematically picking multiple audio cues,enhancing the classifier model’s efficacy in accurately discerning human *** utilized dataset is taken from the EMO-DB database,preprocessing input speech is done using a 2D Convolution Neural Network(CNN)involves applying convolutional operations to spectrograms as they afford a visual representation of the way the audio signal frequency content changes over *** next step is the spectrogram data normalization which is crucial for Neural Network(NN)training as it aids in faster *** the five auditory features MFCCs,Chroma,Mel-Spectrogram,Contrast,and Tonnetz are extracted from the spectrogram *** attitude of feature selection is to retain only dominant features by excluding the irrelevant *** this paper,the Sequential Forward Selection(SFS)and Sequential Backward Selection(SBS)techniques were employed for multiple audio cues features ***,the feature sets composed from the hybrid feature extraction methods are fed into the deep Bidirectional Long Short

关键词： Artificial intelligence application multi features sequential selection speech emotion recognition deep Bi-LSTM

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