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2022 IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2022

作者： Samantaray, Swati Patra, Sudhansu Shekhar Mohanty, Amlan Barik, Lalbihari Barik, Akash Brahma, Aditya Narayan Kiit Deemed to Be University School of Humanities Bhubaneswar India Kiit Deemed to Be University School of Computer Applications Bhubaneswar India Iiit Department of Computer Science Bhubaneswar India King Abdulaziz University Faculty of Computing & Information Technology in Rabigh Department of Information Systems Saudi Arabia New Horizon College of Engineering Dept. of Computer Science Bengaluru India

ISBN: (数字)9781665497817

ISBN: (纸本)9781665497817

Folk dances are a primordial expression of ecstasy and bliss and serve as a mode of communication. They also facilitate in keeping the people connected to their traditions as well as ancestry. Furthermore, these dances help in preserving cultural unity and their tales can often reveal a lot about the periods these dances have developed through. Mahari, Gotipua and Odissi are such folk dance forms of the eastern state of Odisha. These folk dances are on the verge of extinction owing to the reducing number of practitioners, audience, absence of documentation, and digitalization. The absence of documentation has presented a barrier to the legitimacy of folk dance as an academic field of study. This research paper intends to focus on the classification of the folk dances of Odisha such as Mahari, Gotipua and Odissi which will help them to digitize and preserve. We propose deep learning-based methods for the classification and then it can be preserved. To validate the poses and hand motions, we offer a convolutional neural network model (CNN). © 2022 IEEE.

关键词： Neural network models

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Energy efficiency in Edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification

arXiv

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arXiv 2023年

作者： Corral, José María Rodríguez Civit-Masot, Javier Luna-Perejón, Francisco Díaz-Cano, Ignacio Morgado-Estévez, Arturo Domínguez-Morales, Manuel 10 Spain Architecture and Computer Technology Dept. E.T.S. Ingeniería Informática University of Seville Avda. Reina Mercedes s/n Sevilla Spain

In this work, we evaluate the energy usage of fully embedded medical diagnosis aids based on both segmentation and classification of medical images implemented on Edge TPU and embedded GPU processors. We use glaucoma diagnosis based on color fundus images as an example to show the possibility of performing segmentation and classification in real time on embedded boards and to highlight the different energy requirements of the studied implementations. Several other works develop the use of segmentation and feature extraction techniques to detect glaucoma, among many other pathologies, with deep neural networks. Memory limitations and low processing capabilities of embedded accelerated systems (EAS) limit their use for deep network-based system training. However, including specific acceleration hardware, such as NVIDIA’s Maxwell GPU or Google’s Edge TPU, enables them to perform inferences using complex pre-trained networks in very reasonable times. In this study, we evaluate the timing and energy performance of two EAS equipped with Machine Learning (ML) accelerators executing an example diagnostic tool developed in a previous work. For optic disc (OD) and cup (OC) segmentation, the obtained prediction times per image are under 29 and 43 ms using Edge TPUs and Maxwell GPUs, respectively. Prediction times for the classification subsystem are lower than 10 and 14 ms for Edge TPUs and Maxwell GPUs, respectively. Regarding energy usage, in approximate terms, for OD segmentation Edge TPUs and Maxwell GPUs use 38 and 190 mJ per image, respectively. For fundus classification, Edge TPUs and Maxwell GPUs use 45 and 70 mJ, respectively. © 2023, CC BY-NC-ND.

关键词： Graphics processing unit

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AI in Agriculture: Yield Optimization and Food Security

AI in Agriculture: Yield Optimization and Food Security

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Lavanya Chavan Rishikesh Kumbhalkar Poorva Agrawal Nitin Rakesh R. Parvathi Mohammad Shahid Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology (VIT) Vellore India Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Artificial Intelligence (AI) is remarkably automating the agricultural methods. In this process, AI is helping to optimize yield, and improving food security in a world with growing population and environmental concern(s). AI technology integration in agriculture is the focus of this paper which includes predictive analytics, machine learning and IoT. These applications led to precision farming and resource management which lead to efficient use of resources. The discussion on the benefits of AI in increasing productivity and sustainability is based on the summarization of the latest literature and empirical studies. Yet, these technologies bring along their own issues such as initial capital investment to implement the systems in emergence economies as well as the need for the rapid upgrading of the infrastructure and human capital. This research demonstrates that AI has the potential to change agriculture radically, while at the same time acknowledging the pending problems that hinder full-fledged implementation.

关键词： Training Food security Agriculture Resource management Artificial intelligence Sustainable development Socioeconomics Optimization Farming Investment

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On the Effective Representation of Quantum Data for Classical Machine Learning Problems 26

On the Effective Representation of Quantum Data for Classica...

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26th Pan-Hellenic Conference on Informatics, PCI 2022

作者： Savvas, Ilias Chernov, Andrey Kartashov, Oleg Beliavsky, Grigory Butakova, Maria The University of Thessaly School of Technology Dept. of Digital Systems Larissa Greece The Smart Materials Research Institute Southern Federal University Rostov-on-Don Russia The Vorovich Institute of Mathematics Mechanics and Computer Sciences South Federal University Rostov-on-Don Russia

ISBN: (纸本)9781450398541

There is no doubt that quantum computing has opened up new horizons and perspectives in many fields, including scientific research. With the advancement of technology and quantum computers, we can now conduct new kinds of scientific experiments: observing quantum properties of individual systems, atoms, electrons and photons as well as influencing and controlling quantum systems. Quantum mechanics describes the properties of quantum systems by their quantum states. Unlike in classical mechanics, quantum states themselves cannot be directly observed in experiments, so many-body dynamics mathematical simulations are useful. Even though many-body dynamics problems can be solved for limited cases, but when we consider real-world pieces of matter this leads to computation-intensive calculations that are beyond classic digital computers. The memory and time needed to describe the quantum state of a many-body system scales exponentially with the size of the system. This has led physics communities to focus their attention on the algorithms underlying modern machine learning with the goal of making progress in quantum matter research. As a consequence, the successful application of machine learning requires effective and informative data representation. This paper discusses some novel aspects of applying classical machine learning algorithms to quantum data derived from measurements associated with quantum systems in many-body simulation problems. In this paper, we investigate and experimentally demonstrate one of the effective methods of the quantum data representation suited for the classical machine learning applications based on quantum classical shadows. © 2022 ACM.

关键词： Digital computers

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Approximating CNN Computation for Plant Disease Detection

Approximating CNN Computation for Plant Disease Detection

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Anakhi Hazarika Pranav Sistla Vineet Venkatesh Nikumani Choudhury Dept. of Electronics &#x0026 Communication Engineering Indian Institute of Information Technology Guwahati India Dept. of Computer Science &#x0026 Information Systems Birla Institute of Technology &#x0026 Science Pilani Hyderabad India

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

Enabling smart technologies in agriculture has led to the improvement of crop productivity. In India, agriculture is a primary occupation, and 70% of the population is dependent on it. Plant diseases cause significant losses in an agriculture-oriented economy. Timely monitoring of plant health and detecting plant disease is a laborious process. Automated monitoring and detection techniques hold great promise for identifying plant condition and providing useful information to facilitate effective agricultural management measures. Deep learning (DL) algorithms improve the detection accuracy in many computer vision applications of smart and precision agriculture. This paper presents a plant disease detection and classification method using YOLOv3 (You Only Look Once) model to design an Internet-of-Things (IoT) device. An approximate computing technique has been adopted that minimizes the computational complexity of DL algorithms to deploy on any embedded devices efficiently. The proposed model achieves an average of 96.92% of classification accuracy while detecting plant disease for three different classes.

关键词： Computational modeling Plants (biology) Approximate computing Sociology Approximation algorithms Software Classification algorithms

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Spatio-Temporal Agnostic Deep Learning Modeling of Forest Fire Prediction Using Weather Data

Spatio-Temporal Agnostic Deep Learning Modeling of Forest Fi...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Abdul Mutakabbir Chung-Horng Lung Samuel A. Ajila Marzia Zaman Kshirasagar Naik Richard Purcell Srinivas Sampalli Dept of Systems and Computer Eng. Carleton University Ottawa ON Canada Research and Development Cistel Technology Ottawa ON Canada Dept. Electrical and Computer Eng. University of Waterloo Waterloo ON Canada Faculty of Computer Science Dalhousie University Halifax NS Canada

This research provides a spatio-temporal agnostic framework based on subsampling to generate generic deep learning models using publicly available weather data and to predict the probability of forest fire and severity. The aim is to show that this framework can be used to subsample and generate a balanced dataset for generic deep learning models to improve predictions for forest fires. The framework works for binary classification and regression deep learning models. It also works with limited variations between fire and non-fire data. Using this framework, 45 of the binary classification models built produced an F1Score greater than 0.95 while 35 of 54 regression models produced an R 2 Score greater than 0.91.

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jingyao Li Haitao Yuan Yaofei Ma Jing Bi Jinhong Yang Jia Zhang School of Automation Science and Electrical Engineering Beihang University Beijing China College of Computer Beijing University of Technology Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios.

关键词： Energy consumption Cloud computing Time-frequency analysis Multi-access edge computing Simulation Collaboration Servers Optimization Sorting Genetic algorithms

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AI-Driven Forecasting Mechanism for Cardiovascular Diseases: A Hybrid Approach using MLP and K-NN Models

AI-Driven Forecasting Mechanism for Cardiovascular Diseases:...

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Self Sustainable Artificial Intelligence systems (ICSSAS), International Conference on

作者： Koushik Reddy Chaganti Pokuri Venkataradha Krishnamurty Addala Hemantha Kumar G. Sudha Gowd Chinnala Balakrishna P. Naresh Dept. of Computer and Information Systems University of the Cumberlands KY USA CSE Department Audisankara College of Engineering and Technology Gudur Andhra Pradesh CSE Department Srinivasa Ramanujan Institute Of Technology Anantapuram CSE Department Guru Nanak Institute of Technology(A) Hyderabad Dept of CSE Dayananda Sagar University Bangalore

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Cardiovascular disease (CVD) results in malfunctioning of the heart and also the related blood vessels and regularly primes to death or corporal paralysis. Initial and automated discovery of CVD is crucial for saving many of the common humanoid lives. While several Investigations have been carried out to achieve this goal, and there is still room for development in terms of performance and reliability. This new work provides one more step in this area, since it utilizes two reliable ML algorithms, “multi-layer perceptron (MLP)” and “K-nearest neighbour (K-NN)“to detect CVD using publicly available data from the University of Engineering California Irvine source. The models’ concerts are enhanced optimally and by eradicating the possible outliers and common existing attributes with values that are treated as null. They performed general Experimental and effective findings show that the MLP model outperforms the KNN model in terms of reporting accuracy ($82.47 \%$) and area below the curve ($86.41 \%$). As a result, the proposed MLP model is recommended for automated CVD identification. Furthermore, this approach may be used to identify new illnesses, and the suggested model’s routine can be tested against other standard datasets.

关键词： Accuracy Prevention and mitigation Medical services Predictive models Nearest neighbor methods Reliability engineering Paralysis Cardiovascular diseases Standards Thyroid

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Plug & Play Device for Hybrid Smart Classroom: A Prototype Development

Plug & Play Device for Hybrid Smart Classroom: A Prototype D...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Arumalla Mohit Krishna Pranav Sistla Nikumani Choudhury Hirak Ranjan Das Dept. of Computer Science and Information Systems Birla Institute of Technology &#x0026 Science Pilani Hyderabad India Dept. of Mechanical Engineering Royal Global University India

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

The demand for a smart classroom has been compounded by Covid-19, which allows students to have a meaningful learning experience while staying home. Students who join a classroom in online mode don't have the opportunity to experience a classroom setting because of the hybrid mode of teaching (both online and offline classes). As a result, they have problems such as not being able to see the board clearly, not being able to follow the lecturer because he or she is out of frame, and thus having difficulty learning. Furthermore, this results in lower interaction between the online students and the professor. To teach effectively, the professor is unable to use the entire length of the board as it would not be visible to students joining in online mode. As students and instructors, we identified the issue and developed a plug-and-play device which is portable to address the aforesaid problem during this testing and difficult period of time of the pandemic. The paper outlines the practical implementation of a plug-and-play device that meets the aforementioned requirements. The model also considers power usage, as it can dynamically control energy-consuming resources such as lighting and air conditioning in response to the environment and the presence of students.

关键词： COVID-19 Pandemics Conferences Education Prototypes Lighting Software

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Towards Adaptive AI Governance: Comparative Insights from the U.S., EU, and Asia

arXiv

引用

arXiv 2025年

作者： Kulothungan, Vikram Gupta, Deepti Capitol Technology University 11301 Springfield Rd LaurelMD20708 United States Dept. of Computer Information Systems Texas A&M University - Central Texas 1001 Leadership Pl TX76549 United States

Artificial intelligence (AI) trends vary significantly across global regions, shaping the trajectory of innovation, regulation, and societal impact. This variation influences how different regions approach AI development, balancing technological progress with ethical and regulatory considerations. This study conducts a comparative analysis of AI trends in the United States (US), the European Union (EU), and Asia, focusing on three key dimensions: generative AI, ethical oversight, and industrial applications. The US prioritizes market-driven innovation with minimal regulatory constraints, the EU enforces a precautionary risk-based framework emphasizing ethical safeguards, and Asia employs state-guided AI strategies that balance rapid deployment with regulatory oversight. Although these approaches reflect different economic models and policy priorities, their divergence poses challenges to international collaboration, regulatory harmonization, and the development of global AI standards. To address these challenges, this paper synthesizes regional strengths to propose an adaptive AI governance framework that integrates risk-tiered oversight, innovation accelerators, and strategic alignment mechanisms. By bridging governance gaps, this study offers actionable insights for fostering responsible AI development while ensuring a balance between technological progress, ethical imperatives, and regulatory coherence. © 2025, CC BY.

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