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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cervino, Juan Bazerque, Juan Andrés Calvo-Fullana, Miguel Ribeiro, Alejandro University of Pennsylvania Dept. of Electrical and Systems Engineering Philadelphia United States University of Pittsburgh Dept. of Electrical and Computer Engineering Pittsburgh United States Universitat Pompeu Fabra Dept. of Information and Communication Technologies Barcelona Spain

ISBN: (纸本)9781728163277

Multi-task learning aims to acquire a set of functions, either regressors or classifiers, that perform well for diverse tasks. At its core, the idea behind multi-task learning is to exploit the intrinsic similarity across data sources to aid in the learning process for each individual domain. In this paper we draw intuition from the two extreme learning scenarios - a single function for all tasks, and a task-specific function that ignores the other tasks dependencies - to propose a bias-variance trade-off. To control the relationship between the variance (given by the number of i.i.d. samples), and the bias (coming from data from other task), we introduce a constrained learning formulation that enforces domain specific solutions to be close to a central function. This problem is solved in the dual domain, for which we propose a stochastic primal-dual algorithm. Experimental results for a multi-domain classification problem with real data show that the proposed procedure outperforms both the task specific, as well as the single classifiers. © 2023 IEEE.

关键词： Constrained Learning Multi-task Learning

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Analysis of Energy Potential of Micro Wind Turbines at Three Locations in the Central-Eastern Part of the Bulgarian Danube River Region 5

Analysis of Energy Potential of Micro Wind Turbines at Three...

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5th IEEE International Conference on Communications, information, Electronic and Energy systems. CIEES 2024

作者： Valov, Nikolay Gabrovska-Evstatieva, Katerina Stoyanov, Ivaylo Kaneva, Tsvetelina Kadirova, Seher Evstatiev, Boris Mihailov, Nicolay University of Ruse "Angel Kanchev" Dept. of Automation and Electronics Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Science Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Electrical Power Engineering Ruse Bulgaria University of Ruse "Angel Kanchev" Dept. of Computer Systems and Technologies Ruse Bulgaria

ISBN: (纸本)9798350352863

The share of renewable energy sources in the energy mix worldwide is gradually increasing. And even though they all have a highly variable nature, they can be forecasted statistically. The wind energy potential was investigated in this article in the Bulgarian Danube region, located between Ruse and Silistra. Two years of meteorological data, including the wind speed at 10 m height and the ambient temperature was used to estimate the average wind energy for each month of the year at three geographic locations - Ruse, Staro Selo (near Tutrakan), and Calarasi, Romania (near Silistra). The results showed that Staro Selo has the highest energy potential, reaching up to 0.75 kWh/day/m2 in February, followed by Calarasi with 0.38 kWh/day/m2. The city of Ruse showed the lowest wind potential with the highest average monthly value reaching only 0.0027 kWh/day/m2. The obtained results showed that the regions around Tutrakan and Silistra are appropriate for the application of micro wind turbines. © 2024 IEEE.

关键词： Windmill

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

Support Chatbot based on ChatGPT for Customer Relationship M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Mohamed, Islam Samih Abdelsalam, Mohamed Moawad, Ibrahim F. Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently. © 2024 IEEE.

关键词： Sales

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Scalable, Cost Effective IoT Based Medical Oxygen Monitoring System for Resource Constrained Hospital Environment 6

Scalable, Cost Effective IoT Based Medical Oxygen Monitoring...

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6th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Rodrigues, Joel Chandra Shekar, R.K. School of Engineering Dept. of Computer Science and Engg. Bengaluru India BITS Pilani Pilani Campus Dept. of Computer Science and Information Systems India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Oxygen therapy is one of the critical treatments employed in epidemics, pandemics, and natural calamities. Recent covid pandemic worldwide witnessed many deaths due to improper management, delayed delivery, and wastage of medical oxygen. Therefore, efficient utilization of available oxygen is very important. To monitor and manage oxygen, several hospitals employ IoT-based systems. Scalability is an essential feature in such monitoring systems.in order to cater to the needs of a sudden surge in the number of patients requiring oxygen. The most commonly employed technique to monitor and manage an oxygen cylinder uses a pressure sensor where scaling up is an issue. Therefore, in this paper, a scalable solution that efficiently measures and monitors the available oxygen in the cylinder is proposed. The approach measures oxygen level using a weight sensor module and raises alerts during critical conditions such as low oxygen level and blockage or leakage of oxygen. The proposed system is a cost-effective, plug-and-play system that aids rapid deployment thereby providing timely care to the patients. Also, it does not require any change in the existing infrastructure making it suitable for a resource-constrained environment. The proposed system supports a web-based dashboard and mobile app that can be remotely accessed. © 2022 IEEE.

关键词： Oxygen

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Physical and System Level Power Reduction Technique for NoC Architectures: Overview of State of the Art

Physical and System Level Power Reduction Technique for NoC ...

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2023 International Conference on Decision Aid sciences and Applications, DASA 2023

作者： Belkebir, Djalila Lati, Abdelhai Belkebir, Malak Faculty of New Information and Communication Technologies Ouargla30000 Algeria University of Oum El Bouaghi Research Laboratory on Computer Science's Complex Systems Dept. Mathematics and Computer Science Oum El Bouaghi Algeria

ISBN: (纸本)9798350342055

Power dissipation and energy consumption are key factors for the performance improvement of modern technology systems. In recent years, several strategies have been proposed to solve the power management issues on embedded systems. In this paper, we classify the state of the art of those techniques. Classification is done based on run-time, activation, and idle state of the different components of the network on chip (NoC). We illustrate the requirement of power management based on several important parameters. A highlight is put on the drawbacks and benefits. Also, classification is done based on different NoC abstraction levels. However, by this paper, we intend to guide the researchers and application developers in getting insights about the strategies of power management techniques that will lead to the design of more efficient high performances in embedded systems.of the future. © 2023 IEEE.

关键词： Network-on-chip

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iSFA: Intelligent SF Allocation Approach for LoRa-Based Mobile and Static End Devices

iSFA: Intelligent SF Allocation Approach for LoRa-Based Mobi...

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IEEE Conference on Wireless Communications and Networking

作者： Anakhi Hazarika Nikumani Choudhury Dept. of Electrical & Electronics Engineering India Dept. of Computer Science & Information Systems India

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

LoRaWAN (Long Range Wide Area Network) is a low-power, wide-area wireless communication protocol designed specifically for the Internet of Things (IoT) and machine-to-machine (M2M) applications that enable long-range, bidirectional communication between low-power devices. Lo-RaWAN employs Adaptive Data Rate (ADR) technology to dynamically adjust the data rate for each device based on its signal quality and distance from the gateway. ADR enables improved network performance, extends device battery life, and simplifies network management, making LoRaWAN suitable for various IoT deployments. However, the end devices' inefficient utilization of radio resources (e.g., spreading factor and transmission power) significantly degrades network performance, device battery life, and adaptability to changing network conditions. Machine Learning (ML) algorithms analyze and optimize the real-time network conditions to enhance network performance. This work aims to develop an ML-based approach that adaptively selects the most suitable Spreading Factor (SF) for end devices (ED). Two independent ML algorithms such as K-means and Reinforcement Learning (RL) have been applied to EDs and Gateways, respectively, to dynamically allocate SF for both static and mobile EDs. Through simulations, the performance of the proposed mechanism is analyzed in terms of packet success rate, convergence time, energy consumption, latency, and throughput.

关键词： Performance evaluation Wireless communication Wide area networks Machine learning algorithms Reinforcement learning Logic gates Throughput

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Optical Evanescent Field for Direct Observation of Sub-100nm Particle Motion on Glass Surface 1

Optical Evanescent Field for Direct Observation of Sub-100nm...

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1st International Conference on Robotics, engineering, science, and Technology, RESTCON 2024

作者： Khajornrungruang, Panart Permpatdechakul, Thitipat Suzuki, Keisuke Blattler, Aran Terayama, Yutaka Goto, Daiki Phaisalpanumas, Pipat Bun-Athuek, Natthaphon Phunopas, Amornphun Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Division Dept. of Intelligent and Control System Faculty of Computer Science and Systems Engineering Fukuoka Japan Kyushu Institute of Technology Iizuka-Shi Mechanical Information Science and Technology Division Dept. of Interdisciplinary Informatics Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Production and Robotics Engineering Bangkok Thailand Kyushu Institute of Technology Iizuka-Shi Mechanical Science and Technology Course Dept. of Intelligent and Control System Graduate School of Computer Science and Systems Engineering Fukuoka Japan King Mongkut's University of Technology North Bangkok Bangsue Faculty of Engineering Dept. of Industiral Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Bangsue College of Industrial Technology Dept. of Mechanical Engineering Technology Bangkok Thailand

ISBN: (纸本)9798350348842

To directly investigate the dynamic nanoscale phenomenon on the surface being processed in wet conditions such as precision polishing, and cleaning in semiconductor industrial, an optical method for visualization and observation of each sub-100 nm sized particle that is moving on an interface such as a silica glass surface by applying an evanescent field have been proposing. Subsequently, we developed an experimental apparatus equipped with an optical microscopy system for verifying the moving particle observation method in a laboratory scale. This article introduces some experimentally direct observation results of duplicated wet processes. © 2024 IEEE.

关键词： Visualization

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Arabic Sentiment Analysis of Food Delivery Services Reviews

Arabic Sentiment Analysis of Food Delivery Services Reviews

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Mustafa, Dheya Khabour, Safaa M. Shatnawi, Ahmed S. Taqieddin, Eyad The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Yarmouk University Faculty of Information Technology and Computer Sciences Dept. of Information Systems Irbid21163 Jordan Jordan University of Science & Technology Dept. of Software Engineering Irbid21110 Jordan School of Computing and Informatics Dept. of Cyber Security Al-Hussein Technical University Amman Jordan

ISBN: (纸本)9798350335590

Customer reviews on online platforms have grown to become an important source of insight into a company's performance. Food delivery services (FDS) companies aim to effectively use customers' feedback to identify areas for improvement of customer satisfaction. Although Arabic is becoming one of the most widely used languages on the Internet, only a few studies have focused on Arabic sentiment analysis to date. The present study conducts an extensive emotion mining and sentiment analysis on FDS-related reviews in Arabic, exploiting natural language processing, and machine learning techniques to extract information that reflects customers' subjective viewpoints, recognize their feelings, and determine polarity in the FDS domain. This work begins with collecting the FDS dataset (Talabat), and then extracting and creating a dialects lexicon for Arabic dialects, followed by walking the reader through detailed steps of cleaning and pre-processing a manually annotated dataset. Finally, we examined classification algorithms including Decision Tree (DT), and Support Vector Machine (SVM). We achieved a maximum accuracy of about 82% using the SVM classifier. © 2023 IEEE.

关键词： Sentiment analysis

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Towards Eco-Sustainability and Green Analytics Model to Measure the Performance of Big Data systems 9

Towards Eco-Sustainability and Green Analytics Model to Meas...

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9th International Conference on information Management, ICIM 2023

作者： Jusoh, Yusmadi Yah Abdullah, Rusli Nor, Rozi Nor Haizan Yahaya, Jamaiah Hj. Muhammad, Shireen Arunachalam, Aishwharya Raani Universiti Putra Malaysia Faculty of Computer Science and Information Technology Dept. of Software Engineering and Information Systems Serdang Malaysia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia Faculty of Communication Selangor Malaysia

ISBN: (纸本)9798350336382

Many BDS is doomed to failure because of the missing knowledge on measuring the performance of BDS. The failure to identify the performance measurement and correct will make the problems worsen. This will complicate the efforts of fixing such problems and more importantly, will affect the quality of knowledge, insights, and results expected by the users in the future. In achieving durable performance, the organization needsto understand the factors that contribute to the performance of the eco-sustainability and green analytics metrics for BDS. Although the existence of its importance and awareness was largely neglected. Therefore, the eco-sustainability and green analytic measurement of the BDS is examined in this research. Measuring the BDS performance has the benefit of identifying problems and launching corrective actions before these problems happen and become worsen. The research will investigate the eco-sustainability and green analytic measures which will be proposed and used to capture performance for each process of BDS. Then, based on such measures and metrics, as well as existing performance concepts, frameworks, and models, a measurement model for BDS will be created. The model will be validated through expert evaluations and confirmatory studies with users and practitioners of BDS users. The research has a greater significance in leveraging existing performance concepts in BDS settings and the research proposes users' participation in the sustainability and continuous performance evaluation of theirBDS. © 2023 IEEE.

关键词： Big data

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