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Seizure Detection by Analyzing EEG Signals Using Deep Learning networks 1

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2nd International Conference on Advances in Data-driven Computing and Intelligent systems, ADCIS 2023

作者： Agarwal, Amber Trivedi, Rishikesh Goyal, Somya R. Ahmed, Istiaque Manipal University Jaipur Rajasthan Jaipur303007 India Osaka Metropolitan University Osaka Japan

ISBN: (数字)9789819995240

ISBN: (纸本)9789819995233

Epileptic seizures is a well-known and the most chronic neurological disorder and it is observed highly in infants and elderly people. Symptoms of epileptic seizures are very dangerous;they lead to injuries due to falling because of jerking movements of the arms and legs that can’t be controlled. Electroencephalography (EEG) signals are prominent tools to analyze brain activities and detect seizures. Learning machines are promising in detecting seizures by analyzing EEG signals. This study proposes a customized deep learning network for seizure detection, i.e., DLN-SD utilizing EEG signals. The publicly available CHB-MIT Scalp EEG dataset is considered. It is empirically compared with pre-trained models, namely AlexNet and GoogleNet and observed that the proposed model (DLN-SD) brings 96.0% precision, 98.0% recall, and 98.0% accuracy and performs best among all three models. It is concluded that the DLN-SD shows an improvement of 15% over AlexNet and 9% over GoogleNet to detect seizures from the EEG dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Cloud-Based Swarm Robotics for Modern Agriculture 1

引用

International Conference on Data Analytics and Management, ICDAM 2023

作者： Sakya, Gayatri Shivam Rani, Naina Tripathi, Tanu Vats, Satvik Department of Electronics and Communication Engineering JSS Academy of Technical Education Uttar Pradesh Noida India Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

Collaborative systems with multiple robots strategies are frequently cited as promising solutions to increase efficiency and system resilience in the agricultural area, where robotics is anticipated to play a significant role. Swarm robotics, one of the multi-robot techniques, emphasizes factors like adaptability, scalability, and resilience in completing complicated tasks and is seen to be particularly useful for precision farming and large-scale agricultural applications. This paper discusses the agri-bot as a cutting-edge technique for monitoring the quality of crops and watering the crops with the least amount of work and labor, making it a useful machine. Also IoRT (Internet of Robotic Things) is used among four robots for swarm robotics. The proposed design and its implementation are discussed in the paper for state-of-the-art swarm-based agriculture. The robots designed with sensors and control mechanism will monitor the field and using IoT will transfer the data to cloud. The synchronizing robot will acts as gateway for other robots. It will receive commands from server and all the robots will act accordingly to complete the task. So this work implements the swarm robotics technology and hence will have future scope in many other critical applications like surveillance, medicine, and food delivery sentinel swarm robotics for defense applications as well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Crops

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Development of Reliable TOPSIS Method Using Intuitionistic Z-Numbers 1

引用

12th World Conference on Intelligent systems for Industrial Automation, WCIS 2022

作者： Alam, Nik Muhammad Farhan Hakim Nik Badrul Khalif, Ku Muhammad Naim Ku Jaini, Nor Izzati Centre for Mathematical Sciences Universiti Malaysia Pahang Gambang Pahang Kuantan Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Pahang Pahang Bandar Tun Abdul Razak Jengka Malaysia Centre of Excellence for Artificial Intelligence and Data Science Universiti Malaysia Pahang Gambang Pahang Kuantan Malaysia

ISBN: (数字)9783031515217

ISBN: (纸本)9783031515200

Technique for order of preference by similarity to ideal solution (TOPSIS) is a multi-criteria decision-making (MCDM) method which is developed based on the distance measure from the positive and negative ideal solutions. This paper extends the TOPSIS for handling data in form of intuitionistic Z-numbers (IZN). IZN consists of restriction and reliability components which are characterized by the intuitionistic fuzzy numbers. The distance measure between IZN is proposed using the convex compound of the distances for the restriction and reliability parts. The supplier selection problem in an automobile manufacturing company is adopted to illustrate the proposed model. Sensitivity analysis is performed for the validation of the proposed model and its result shows that the proposed model gives a consistent ranking of alternatives. The strength of the proposed model is the preservation of decision information in form of IZN which does not possess the conversion into regular fuzzy number to avoid the loss of information. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitivity analysis

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Smartainity: A Comprehensive Framework for Urban Performance Assessment in African Smart Cities with Key Performance Indicators 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Moumen, Nessrine Radoine, Hassan Nahiduzzaman, Kh. Md. Oulidi, Hassane Jarar School of Architecture Planning and Design Mohammed VI Polytechnic University Benguerir Morocco School of Engineering University of British Columbia Okanagan Kelowna Canada Hassania School of Civil Engineering Casablanca Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

Smart cities are emerging as a potential solution to the challenges of urbanization, especially in the African context. However, there is a lack of a comprehensive and systematic framework for assessing the performance of smart cities in terms of sustainability and smartness. This paper proposes Smartainity, a novel methodology for urban performance assessment based on Key Performance Indicators (KPIs) that cover five dimensions: technology and innovation, economy, energy, society and living, and environment. The framework is designed to be flexible, scalable, and adaptable to different contexts and scenarios. The paper also presents the application of Smartainity to the case study of Casablanca Smart City 2023, a pilot scheme in Morocco that aims to transform the city into a smart and sustainable urban hub. The paper discusses the strengths and limitations of the methodology, as well as the future directions for its improvement and expansion. The paper contributes to the literature on smart cities by providing a new perspective on urban performance assessment that integrates sustainability and smartness criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Sustainable development

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EEG-Based Drivers Drowsiness Prediction Using Personalized Features Extraction and Classification Methods Under Python 1

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International Conference on Intelligent systems and Smart Technologies, I2ST 2023

作者： Elidrissi, Mouad Elmouzoun Essoukaki, Elmaati Taleb, Lhoucine Ben Mouhsen, Azeddine En-naoui, Ilias Harmouchi, Mohammed Laboratory of Radiation-Matter and Instrumentation Faculty of Science and Techniques Hassan First University of Settat Settat Morocco Higher Institute of Health Sciences Hassan First University of Settat Settat Morocco

ISBN: (数字)9783031476723

ISBN: (纸本)9783031476716

During the last century, most of researchers tend to develop alternative solutions to variant fields’ problems using artificial intelligence and machine learning techniques, leading to an important AI revolution. Applying these techniques in healthcare was a big win, namely the importance of predicting the occurrence of seizures at a prompt time or detecting pathologies as earlier as possible. Moreover, the early detection of drowsiness state for drivers became a serious subject of interest, due to the important turnout for driving in addition to the increasing of cars accidents. So, increasing road safety was an obligation. Our paper therefore, presents several methods of detecting drivers’ drowsiness using personalized features extraction and machine learning algorithms applied to a single channel of electroencephalogram signals under Python. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Python

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Combining DOE and EDAS Methods for Multi-criteria Decision Making 1

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32nd International Conference on Organization and Technology of Maintenance, OTO 2023

作者： Trung, Do Duc Truong, Nguyen Xuan Dung, Hoang Tien Ašonja, Aleksandar Faculty of Mechanical Engineering Hanoi University of Industry Hanoi100000 Viet Nam Faculty of Economics and Engineering Management in Novi Sad University Business Academy in Novi Sad Novi Sad Serbia

ISBN: (数字)9783031514944

ISBN: (纸本)9783031514937

Most existing Multi-Criteria Decision Making (MCDM) methods can only be used to rank readily available choices. After the first ranking has been completed, some of the options are discarded and others may arise, then it will be necessary to start the ranking over again. This study proposes a new method that can quickly rank alternatives in this situation. The proposed method is a combination of the Design of the Experimental method (DOE) and Evaluation based on the Distance from Average Solution (EDAS) method, hence it will be referred to as the DOE-EDAS method. The new aspect of this proposed method is to build a relationship between the scores of the options and the criteria. This relationship is called the regression equation. Using this regression equation, it is possible to quickly calculate the scores for alternatives (after an alternative is discarded or an alternative is added) and rank them. The method has been applied to multi-criteria decision-making in some specific cases. The results demonstrate the effectiveness of this method. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Stock Price Prediction Using LSTM, CNN and ANN 1

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Kakarla, Krishna Prabeesh Kiran, Dammalapati Chetan Sai Kanchana, M. SRM Institute of Science and Technology Tamil Nadu Chennai603210 India

ISBN: (数字)9789819984510

ISBN: (纸本)9789819984503

Forecasting the stock market is difficult because the stock price time series is so intricate. We applied long short-term memory (LSTM) algorithm, convolutional neural network (CNN) and artificial neural network (ANN) because of the following reason: Recent work provides preliminary proof that machine learning methods could locate relationships in stock price sequences that are not linear. The stock market's non-stationary and high volatility makes it difficult to foresee the trajectory time series data in the economy. We expanded the scope of our model training outside the Indian stock market. More specifically on stock prices of MAANG and Divis labs, all three models were performing really well with RMSE of LSTM being 0.42 and R2 score being 0.55, and this being the best result among all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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A Modified African Vultures Optimization Algorithm for Enhanced Feature Selection 1

引用

International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Cheng, Wy-Liang Pan, Li Sharma, Abhishek Lim, Tiong Hoo Ang, Chun Kit Chong, Kim Soon Tiang, Sew Sun Lim, Wei Hong Faculty of Engineering Technology and Built Environment UCSI University Kuala Lumpur56000 Malaysia Department of Computer Science and Engineering Graphic Era Deemed to Be University Dehradun248002 India Faculty of Engineering Universiti Teknologi Brunei Bandar Seri Begawan1410 Brunei

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

Feature selection is a reliable technique for reducing redundant, noisy, or inaccurate features in raw input datasets without compromising classifier accuracy. Integrating metaheuristic search algorithms (MSAs) into feature selection enables the discovery of pertinent features, simplifying dataset representation. However, traditional MSAs, such as the African Vultures Optimization Algorithm (AVOA) encounter limitations with their initialization scheme, leading to a higher likelihood of local optima trapping when handling datasets with numerous features. To address this issue, a modified MSA called Multi-Chaotic African Vultures Optimization (MCAVOA) is proposed for more effective feature selection. Specifically, multiple chaotic maps are employed to generate the initial population of MCAVOA, enhancing its robustness against premature convergence in complex optimization problems like feature selection. The performances of MCAVOA are evaluated using twelve datasets from the UCI Machine Learning Repository and compared with the original AVOA, as well as four other MSAs. Extensive simulation studies reveal that MCAVOA achieves the highest mean accuracy and selects the fewest features in majority of employed datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Feature Selection

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Unveiling Worldwide Prospects and Challenges in Implementing Telematics Technologies in Electric Vehicles 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Singh, Ranbir Agrawal, Anubhav Ankur School of Engineering and Technology BML Munjal University Gurgaon Gurgaon India

ISBN: (数字)9789819981359

ISBN: (纸本)9789819981342

The fourth industrial revolution has changed the paradigm of the technology around the globe. Electric vehicle technology has emerged as the prominent solution toward the sustainable transportation enabling the reduction in carbon footprints worldwide. Autonomous vehicles, connected cars, vehicle-to-vehicle communication, vehicle-to-infrastructure communication, and vehicle-to-grid communication are among the most recent technological developments in the field of automobile engineering. This research article aims to critically review the literature available in Web of Science database and present various bibliometric indicators such as total number of publications, growth rate, average citations per paper, and most popular words. The application, scope, outcomes, and future research perspectives of telematics in electric vehicles are also studied. It is unveiled that telematics is emerging as an efficient tool in reducing accidents and traffic congestions in addition to managing the vehicular performance. Telematics has its applications in road safety, fleet management, and predictive maintenance leading to the developments of smart cities. In future, vehicular telematics will be an integral part of electric vehicles in predicting the vehicle health, driver behavior, optimizing the driver style, and promoting personalized training programs. Challenges like standardization, protocols, interoperability, and cybersecurity need an urgent attention in telematics. Authors provide valuable insights for policymakers, industry stakeholders, and researchers to pave the path for effective telematics implementation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Telematics

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A Framework Based SVM for Bankruptcy Prediction 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Adelelhamid, Tamouh Ayoub, Mniai Khalid, Jebari IABML FSTT Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

Bankruptcy prediction is a field within finance and business that aims to assess the likelihood of industrial companies facing financial distress. The impact of bankruptcy can lead to colossal losses for the company, employees, consumers and the entire country. Various methods and models are used to analyze financial data and other relevant information to make predictions. Indeed, this study aims to utilize a framework that makes it possible to analyze unbalanced. After this first treatment, the proposed framework invests the performance of support vector machines as a machine learning algorithm for prediction tasks. The performance of the proposed framework is compared with other techniques such as Random Forest (RF), Decision Tree (DT) and K-nearest Neighbors (KNN). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Support vector machines

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