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2024 International Conference on Intelligent Algorithms for Computational intelligence Systems, IACIS 2024

作者： Adnan, Myasar Mundher Vijay Kumar, Bura Madhura, G.K. Mohammed, G. Das, Abhijeet The Islamic University Engineering College of Technical Engineering Department of Computers Techniques Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Ahl Al Bayt University Karbala Iraq Department of Civil Engineering Bhubaneswar India

ISBN: (纸本)9798350360660

Generating spatial and temporal features for real time prediction and classification in precision agriculture is challenging due to inaccurate estimation of features from the remote sensing images. Using Geographical Information System (GIS) and systematic data regarding spatial and temporal data for monitoring and management of agricultural activities. The proposed research integrated GIS and used satellite image processing with Ensemble machine learning (EML) algorithms. Initially, the remote sensing data is pre-processed with normalization technique to remove noise. The pre-processed data is given as input to the Improved Speeded Up Robust Features (ISURF) for fast computation and identification of relevant features. The EML algorithms such as Random Forest (RF), Support Vector machine (SVM), and artificial Neural Network (ANN) are used as a prediction and classification models considering the ISURF features as input. Additionally, the Spatial Attention Mechanism (SAM) is integrated with the EML algorithm to particularly focus on the spatial features of the remote sensing data. The experimental results showed that the proposed SAM-EML achieved precise prediction and classification with 98.30% accuracy, and 97.19% precision when compared to the existing methods Random Forest Regression (RFR) and RF-SVM. © 2024 IEEE.

关键词： Adversarial machine learning

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African Vultures Based Feature Selection with Multi-modal Deep learning for Automatic Seizure Prediction 2

African Vultures Based Feature Selection with Multi-modal De...

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Sandhya, M. Khan, Zabiha Mohan, B.R. Nayana, C.P. Rajashekhar, S.A. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9798350382952

The superiority of life for people with epilepsy can be greatly improved with the assistance of accurate seizure prediction and early warning. An automatic prediction model is required to procedure the EEG signals and account for the leads optimization problematic, as opposed to the majority of hand-designed prediction approaches. In this research, we put forth a fully automated model for seizure prediction using Channel and Spatial attention (CASA). The first step in the feature extraction practice is to pre-process the raw EEG signals. Large amounts of computation can be saved by adding more features to the system, but finding the right ones can be tricky. The African vulture optimization algorithm's (AVOA) strong capacity to break out of local optima is what makes this procedure possible. CASA saved the raw EEG data's temporal and geographical details. Automatic optimization of EEG full-lead data was completed with channel attention (CA), leading to an increase in the accuracy of predictions. The aforementioned adaptive learning of feature parameters was accomplished via spatial attention (SA). When all else fails, a fully associated layer is used to make the seizure forecast. The suggested algorithm is tested on the Freiburg EEG database, and the results reveal that the AVOA-based system performs admirably when it comes to predicting seizures. © 2024 IEEE.

关键词： Electroencephalography

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Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis Solar Tracking System 4

Enhancing Solar Energy Harvesting Efficiency: A Dual-Axis So...

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4th International Conference on Innovative Practices in technology and Management, ICIPTM 2024

作者： Ravikumar, S. Eugene Berna, I. Vijay, K. Pradeep, R. Babu, R. R&d Institute of Science and Technology Vel Tech Rangarajan Dr.Sagunthala Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of College of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307757

The photovoltaic effect has permanently transformed the landscape of renewable sources by making it possible to convert solar energy into useable power. This concept is based on solar photovoltaic (PV) cells, but current PV technology limits us to capturing just a small percentage of the sun's energy at any given time. However, productivity can be greatly increased by using solar monitoring equipment. Creating and validating a low-priced active dual-axis solar tracking system is a breeze if you follow the steps outlined in this article. The best orientation for solar panels is perpendicular to the sun because their production is directly related to the amount of light they receive. Conventional solar panels have trouble keeping up with the ever-changing ideal angle for sunlight absorption and energy gathering. By tracing the sun's movement across the sky, solar trackers can triple the effectiveness of solar panels. This technological advancement is expected to lead to an improvement in energy generation and overall efficiency. This state-of-the-art solar tracker is powered by LEDs and an Arduino Uno. These systems work together to follow the sun's motion and maintain the solar panels facing the appropriate way. To ensure that the solar panels receive the most amount of sunshine possible, the system will automatically adjust their angle. Data processing and analysis in LabVIEW demonstrated the improved efficiency of the novel architecture. Researchers discovered a remarkable 13.44% boost in energy production compared to conventional fixed solar PV systems. © 2024 IEEE.

关键词： Solar panels

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An Enhanced Product Quality Evaluation using Hybrid LSTM-FCNN Model and GWO Algorithm for Social Media Sentiment Analysis 8

An Enhanced Product Quality Evaluation using Hybrid LSTM-FCN...

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8th International Conference on Electronics, Communication and Aerospace technology, ICECA 2024

作者： Raj Kumar, V.S. Kumaresan, T. Kanna, P. Rajesh Jagadeesan, S. Showmiya, P. Nithin, P. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India Nandha Engineering College Department of Computer Science and Engineering Erode India Nehru Institute of Technology Department of Computer Science - Cyber Security Coimbatore India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India

ISBN: (纸本)9798350367904

Sentiment analysis is still in its developing era, and sometimes, struggles are faced in evaluating user sentiment due to the use of traditional models to analyze the relationship structures in social media interactions. To tackle the issue, we proposed an innovative approach Hybrid LSTM neural network and a fully connected neural network (FCNN) integrated with the Grey Wolf Optimization (GWO) algorithm. The objective of this proposed method is to improve sentiment analysis by employing the LSTM to capture the sequence of data and improve the ability of FCNNs to improve feature extraction and the analysis process. The proposed system process initiated with the data preprocessing of input Twitter sentiment dataset transformed into a suitable input to the Hybrid LSTM-FCNN neural network. Then, temporal dependency of the data captured by LSTM and FCNN is used to extract the features. The GWO method mimics the grey wolf hunting behaviour, for optimizing the proposed Hybrid LSTM-FCNN model's parameters to enhance the classification accuracy. The system models start with the initialization of the model, parameters, and training the models using the dataset to reduce the cross-entropy loss, estimating the testing dataset from unseen data and fine-tuning using GWO as necessary. Results demonstrate significant improvements in sentiment categorization compared to traditional methods. The Hybrid LSTM-FCNN model, enhanced with GWO, effectively captures nuanced sentiment patterns, leading to more accurate classification. Additionally, GWO's computational efficiency contributes to scalability. In conclusion, our proposed approach offers a promising solution for enhancing product quality evaluation through advanced neural network architectures and optimization algorithms applied to social media sentiment analysis. © 2024 IEEE.

关键词： Tweets

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Wave Height Prediction Suitable for Maritime Transportation Based on Green Ocean of Things

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2023年第2期4卷 328-337页

作者： Lou, Ranran Lv, Zhihan Guizani, Mohsen Qingdao University College of Computer Science and Technology Qingdao266071 China Uppsala University Faculty of Arts Department of Game Design Uppsala75002 Sweden Mohamed Bin Zayed University of Artificial Intelligence Department of Machine Learning Abu Dhabi51133 United Arab Emirates

Nowadays, the application fields of the Internet of Things (IoT) involve all aspects. This article combines ocean research with the IoT, in order to investigate the wave height prediction to assist ships to improve the economy and safety of maritime transportation and proposes an ocean IoT Green Ocean of Things (GOoT) with a green and low-carbon concept. In the wave height prediction, we apply a hybrid model (EMD-TCN) combining the temporal convolutional network (TCN) and the empirical mode decomposition (EMD) to the buoy observation data. We then compare it with TCN, long short-term memory (LSTM), and hybrid model EMD-LSTM. By testing the data of eight selected NDBC buoys distributed in different sea areas, the effectiveness of the EMD-TCN hybrid model in wave height prediction is verified. The hysteresis problem in previous wave height prediction research is eliminated, while improving the accuracy of the wave height prediction. In the 24 h, 36 h, and 48 h wave height prediction, the minimum mean absolute errors are 0.1265, 0.1689, and 0.1963, respectively;the maximum coefficient of determination are 0.9388, 0.9019, and 0.8712, respectively. In addition, in the short-term prediction, the EMD-TCN hybrid model also performs well, and has strong versatility. © 2020 IEEE.

关键词： Empirical mode decomposition

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Estimating fish weight growth in aquaponic farming through machine learning techniques 3

Estimating fish weight growth in aquaponic farming through m...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Kumar, Purushottam Tiwari, Pranav Srinivasulu Reddy, U. Machine Learning & Data Analytics Lab CoE in Artificial Intelligence National Institute of Technology Trichy India Indian Institute of Information Technology Tiruchirappalli Computer Science and Engineering Trichy India National Institute of Technology Machine Learning & Data Analytics Lab Department of Computer Applications Trichy India

ISBN: (纸本)9798350338607

Due to the ever-growing population, rapid urbanization, unusual environmental change, and dwindling water supply, the food production from conventional farming techniques won't be able to keep up with increasing food demand for long, leading to severe food scarcity. Aquaponics is emerging as a viable alternative to food cultivation techniques but lacks efficiency, hindering its adoption on a large-scale production basis. The adoption of artificial intelligence (AI) techniques in smart aquaponic farming can further contribute to increasing the yield in aquaponics. In the study, both linear and non-linear regression algorithms such as linear regression, lasso regression, ridge regression, KNN regression, SVR, and decision tree regression were trained on aquaponic fish pond dataset, evaluated on the basis of multiple metrics like MAE, MSE, RMSE, and RMSLE, and a competitive analysis was done to determine the best algorithm for predicting the harvest weight of the fish in aquaponic ponds. The results showed that KNN regression and decision tree regression algorithms have lowest error and highest accuracy of 99.15% and 99.1% respectively, for predicting the harvest weight of the fish. The results obtained could be scaled up to commercial levels and used in regulating nutrient concentration and aquatic environmental conditions © 2023 IEEE.

关键词： Fish

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Mobile-Based Fitness Monitoring System: A Smartphone-Centric Tracker without External Accessories 15

Mobile-Based Fitness Monitoring System: A Smartphone-Centric...

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

作者： Sujatha, E. Devi, P. Jayasri Archana Ilampiray, P. Kalaivani Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering R.M.K. Engineering College India Department of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani India

ISBN: (纸本)9798331300579

This paper delves into the transformative impact of modern technology on human health, exploring the shift towards utilizing mobile applications as integral tools for health monitoring, control, and management. As society becomes increasingly interconnected and reliant on technology, concerns about the potential health implications arise. This paper investigates the nuanced relationship between technology and health, specifically focusing on the development of mobile applications as a paradigm shift from traditional external gadgets. © Grenze Scientific Society, 2024.

关键词： Electronic health record

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Flexible Ring Slot Antenna for Optimized 5G Performance in N77 and N78 Frequency Bands for Wearable Applications

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Progress in Electromagnetics Research C 2024年 150卷 47-55页

作者： Soni, Gaurav Kumar Yadav, Dinesh Kumar, Ashok Sharma, Chanchal Yadav, Manish Varun Department of Electronics and Communication Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Electronics and Communication Engineering Government Mahila Engineering College Rajasthan Ajmer India Department of Computer Science Engineering in Artificial Intelligence and Machine Learning Saraswati College of Engineering Maharashtra Navi Mumbai India Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

The growth of 5G communications has created a demand for advanced wearable and flexible antennas due to supporting the high speeds, low latency, and capability of mechanical deformation conditions such as bending and conformability. In this paper, the design and analysis of a defected ground structure (DGS)-based ring slot antenna for N77 (3.3–4.2 GHz) and N78 (3.3–3.8 GHz) frequency bands is demonstrated. The antenna is made of an RT/Duroid 5880 substrate and has a loss tangent (tan δ) of 0.0009 and dielectric constant (Εr) of 2.2. A DGS-based ring slot microstrip antenna is simulated, tested, and experimentally characterized on different body locations (leg, chest, and hand) along with bending, and their results are presented accordingly. The magnitude of S11 (|S11|) of the proposed antenna is −26.81 dB at resonant frequency of 3.45 GHz, with the impedance bandwidth of 22 MHz (3.486 GHz to 3.508 GHz), peak gain of 6.27 dBi, and radiation efficiency of 85.02%. The simulated specific absorption rate (SAR) for 1 g and 10 g human body tissues is 0.263 W/Kg and 0.076 W/Kg, respectively. The total volume of the antenna is 0.58λ0 × 0.58λ0 × 0.00595λ0 (at 3.5 GHz). The proposed antenna is suitable for 5G wearable devices. © 2024, Electromagnetics Academy. All rights reserved.

关键词： Microstrip antennas

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Demystifying AI: A Robust and Comprehensive Approach to Explainable AI

Demystifying AI: A Robust and Comprehensive Approach to Expl...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Vasanth, S. Keerthana, S. Saravanan, G. Rmk College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India St.Joesph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Sri Sai Ram Institute of Technology Department of Ece West Tambaram Chennai India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508432

The adoption of artificial intelligence (AI) and machine learning (ML) in various computing platforms and areas, necessitates the development of strong Explainable AI (XAI) techniques. Most current AI models are opaque about their decision-making process thereby impeding trust, debugging, and improvement. The goal of this research is to develop comprehensive robust XAI methods capable of explaining the reasoning and decision-making processes in Autonomic, Edge, Server-less, Quantum computing platforms and IoT, Business Automation, Service Innovation domains where these AI models are *** study comprehensively addresses the opacity in AI models through solutions for balanced test-train splits, model evaluation, feature importance, metric imbalances, ROC curve and precision-recall curve analysis, accuracy and statistical metrics, benefits of manual review. This research aims at increasing transparency and trustworthiness within AI systems through developing as well as applying such XAI methods that can detect and mitigate biases while enhancing ethical debugging;responsible development for AI enabled computing purposes. © 2024 IEEE.

关键词： Adversarial machine learning

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artificial intelligence powered chatbots for the speech disabled and hearing impaired using Feedforward Neural Network 1

Artificial Intelligence powered chatbots for the speech disa...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Mukherjee, Subhayan Chatterjee, Anusuya Chandrakar, Khushbu Saxena, Sandeep Asansol Engineering College Artificial Intelligence and Machine Learning Asansol India Asansol Engineering College Electronics and Communication Engineering Asansol India Asansol Engineering College Computer Science and Engineering Asansol India Greater Noida Institute of Technology Computer Science and Engineering Delhi India

ISBN: (纸本)9798350356816

Speech has been the catalyst for natural human communication, and any disability in this field presents a significant barrier to normal communication. Sign language is the primary method of communication, but its education is not available in all parts of the country. This research paper titled 'artificial intelligence Powered Chatbots for the Speech Disabled and Hearing Impaired using Feedforward Neural Network' provides a novel integration of artificial intelligence to guide and empower the daily routines of people with speech disabilities. By introducing a specialized chatbot tailored to the unique needs of speech-disabled individuals, this paper addresses the pressing need for advanced assistive technologies in the daily lives of speech-disabled individuals. This paper underscores the transformative impact of AI in fostering inclusivity and empowerment in the lives of people with disabilities. © 2024 IEEE.

关键词： Assistive technology

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