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An Examination of the Function of Artificial-Intelligence in the Diagnosis of Autism-Spectrum-Disorder 2

An Examination of the Function of Artificial-Intelligence in...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Garg, Raghav Manral, Chandni Joshi, Tripuresh Bisht, Mamta Bharath, K.S. Kharayat, Pushpendra Singh Tula's Institute Deptt. of Computer Science & Engg. U.K Dehradun India Graphic Era Hill University College of Nursing Bhimtal Campus India Tula's Institute Deptt. of Electronics & Comm. Engg. U.K Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Chettinad School of Pharmaceutical Sc. Chettinad Academy of Research & Education Chettinad Hospital & Research Institute Kelambakkam India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India

ISBN: (纸本)9798350368413

Autism Spectrum Disorder (ASD) is a brain disease that makes it hard to do things over and over, connect with others, and talk. Early identification is very important, even if biology is the main reason. ML seems like a hopeful way to identify the condition more quickly and cheaply. This study uses different machine learning methods to find important ASD traits in order to make the testing process better and more automated. Due to the fast development of artificial intelligence, it is now possible to use smart methods to do early, large-scale, and pointless screening and detection of autism. In the future, researchers should work on creating an intelligent medical screening and detection system for autism patients, as well as screening tools and a patient recognition model that is smart and uses multiple types of data. © 2024 IEEE.

关键词： Diseases

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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IEEE Conference on Global comm.nications (GLOBECOM)

作者： Peijie Xian Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen Univ. Shenzhen China Shaanxi Key Lab. of Information Comm. Network and Security Xi’an Univ. of Posts & Telecomm. Xi’an China School of Information Technology Carleton Univ. Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation.

关键词： Cloud computing Adaptation models Heuristic algorithms Computational modeling Quality of service Reinforcement learning Dynamic scheduling Inference algorithms Resource management Long short term memory

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Runtime Replacement of Machine Learning Modules in FPGA-Based Systems 10

Runtime Replacement of Machine Learning Modules in FPGA-Base...

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10th Mediterranean Conference on Embedded Computing, MECO 2021

作者： Mahmoud, Dina G. Shokry, Beatrice Eirefaey, Abdallah Amer, Hassanein H. Adly, Ihab School of Computer and Communication Sciences EPFL Lausanne Switzerland American University in Cairo Elect. and Comm. Eng. Dept. Cairo Egypt The British University in Egypt Electrical Engineering Dept. Cairo Egypt

ISBN: (纸本)9780738133614

Field Programmable Gate Arrays (FPGAs) are often used for accelerating machine learning algorithms. These algorithms can be implemented by various architectures which may differ in performance, required area, power consumption and reliability. This paper proposes the use of the Dynamic Partial Reconfiguration (DPR) capability of FPGAs to download the appropriate architecture depending on changing operating conditions (e.g., harsh environments, low battery). Since multipliers are usually an important component of machine learning circuits, three different multiplier implementations are studied in this paper along with two fault-tolerant architectures for each of them. Several scenarios are then described to illustrate the advantage of changing multiplier architecture on the fly, when the need arises. The concept presented in this research can be extended to other machine learning building blocks. © 2021 IEEE.

关键词： Fault tolerance

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RF Energy Harvesting Effectiveness in Relay-based D2D comm.nication

RF Energy Harvesting Effectiveness in Relay-based D2D Commun...

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computer Science, Information Technology and engineering (ICCoSITE), International Conference on

作者： Mahmoud M. Salim Hussein A. Elsayed Mohamed S. Abdalzaher Mostafa M. Fouda Electronics and Comm. Engineering Department October 6 University Giza Egypt Cognitive Enhancement Meta-Communication ERC Research Center School of Electrical Engineering Korea University Seoul Korea Electronics and Comm. Engineering Department Ain Shams University Cairo Egypt Seismology Department National Research Institute of Astronomy and Geophysics Cairo Egypt Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA

The energy harvesting (EH) of radio frequency (RF) has taken attention to solve the wireless devices’ energy problem. On the other hand, relays play a crucial role in improving device-to-device (D2D) comm.nication performance with regard to data rate and energy efficiency (EE) as well as network coverage and extendability. Relay nodes (RNs) can take advantage of EH while benefiting the D2D networks. This paper provides an answer to the controversial question of whether it is worth using RF EH in relay-based D2D comm.nication. As such, it presents the main EH protocols for relay-based D2D wireless networks, namely power splitting (PS) and time switching (TS). Besides, it models a network of relay-based D2D comm.nication links sharing the spectrum of a number of conventional comm.nication links under the quality of service (QoS) conditions. The results show that the effectiveness of EH depends on factors like the density of the nodes in the investigated area.

关键词： Radio frequency Performance evaluation Protocols Wireless networks Switches Quality of service Device-to-device comm.nication

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Monitoring and Predictive Maintenance using Machine Learning for Industrial Machine 2

Monitoring and Predictive Maintenance using Machine Learning...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Gupta, Anand Kumar Karnatak, Srijana Jain, Suchi Bisht, Mamta Kharayat, Pushpendra Singh Ankatwar, Gajanan Tula's Institute Deptt. of Computer Science & Engg U.K Dehradun India Graphic Era Hill University Deptt. of PC/PDP Bhimtal Campus India Tula's Institute Deptt. of Electronics &comm. Engg U.K Dehradun India Uttaranchal University School of Applied and Life Sciences Dehradun India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India Datta Meghe Institute of Higher Education & Research Maharastra Wardha India

ISBN: (纸本)9798350368413

In recent years, the fourth industrial revolution has drawn interest from all across the world. Many concepts emerged from this new revolution, one of which being predictive maintenance. Predictive maintenance can be achieved for industrial machines through implementation of machine learning techniques to monitor real-time operational data. It can analyze sensor data for early prediction of equipment failures and to reduce downtime and optimizes scheduled maintenance. The purpose of this investigation is to look into developments in academic failure prediction. Predicting failures involves taking into account ideas such as design support systems and predictive maintenance decision support systems. The research gap is identified by analyzing the existing literature studies by exploring optimization strategies through integration of predictive maintenance in industrial environment in smart factories. © 2024 IEEE.

关键词： Predictive maintenance

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A Resource Allocation Scheme in Heterogeneous Multi-system Satellite Network with Beam-hopping

A Resource Allocation Scheme in Heterogeneous Multi-system S...

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Asia-Pacific Conference on comm.nications

作者： Yilin Zhai Yu Zhang Chengchao Liang School of Communication and Information Engineering Chongqing Univ. Posts & Telecom. Chongqing China Info. & Comm. Institute of CQUPT Chongqing China Depart. of Systems and Computer Eng. Carleton Univ. Ottawa ON Canada

The emerging architecture in the next generation of mobile networks leverages the coexistence of Low Earth Orbit (LEO) and Geostationary Orbit (GEO) satellites in a heterogeneous network. This setup not only offers seamless coverage but also enhances user rates. Nevertheless, the efficient allocation of onboard resources, particularly spectrum resources, poses a significant challenge due to their scarcity in such heterogeneous satellite coexistence networks. A practical solution is found in the use of beam hopping (BH) technology. This technology enables multi-beam satellites to serve users using fewer beams than traditional spot-beam systems. This paper proposes a resource allocation strategy for the heterogeneous LEO-GEO coexistence satellite network. We formulate this resource allocation strategy as a joint optimization problem. Due to the complexity of the system arising from the coupling of multiple variables, we break down the original problem into two manageable sub-problems. The first addresses user association, subcarrier, and power allocation and employs a standard convex optimization algorithm for a solution. The second tackles the illuminated beam selection issue, with a genetic algorithm (GA) providing a solution. The effectiveness of our proposed scheme is established through simulation experiments, demonstrating clear performance gains.

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A Deep Learning Approach for Detecting Virtual Link Anomalies in LEO Satellite Networks

A Deep Learning Approach for Detecting Virtual Link Anomalie...

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Asia-Pacific Conference on comm.nications

作者： Rui Pang Lizhi He Zhanjun Liu Chengchao Liang School of Communication and Information Engineering Chongqing Univ. Posts & Telecom. Chongqing China Depart. of Systems and Computer Eng. Carleton Univ. Ottawa ON Canada Info. & Comm. Institute of CQUPT Chongqing China

This paper proposes a deep learning (DL)-based time series (TS) anomaly detection method (DLTS) for the low earth orbit (LEO) satellite network slicing scenario, aiming to address the virtual link anomalies induced by software and hardware abnormalities. Initially, the time series anomalous variations of each resource utilization in satellite network slicing are categorized into three types based on the utilization of computing, storage, and network resources of virtual nodes. Thereafter, the anomaly detection problem is formulated as a classification problem, and the time series are transformed into images using the Gramian Angular Field (GAF) for model input. Lastly, we propose a design principle for a time-constrained deep neural network architecture to mitigate training time, and design a DL model architecture to classify the TS transformation images of resource utilization for each virtual node. This aligns with the objective of the satellite network slicing scenario. Additionally, a new evaluation metric is introduced. Experimental results underscore the shorter training time of the proposed model, and affirm its efficacy, demonstrated through accuracy, F1 score, and the newly proposed evaluation metric.

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Safeguarding Authenticity in Text with BERT-Powered Detection of AI-Generated Content

Safeguarding Authenticity in Text with BERT-Powered Detectio...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Utsho Chakraborty Jaydeep Gheewala Sheshang Degadwala Dhairya Vyas Mukesh Soni Computer Engineering Gujarat Technological University India Sarvajanik College of Engineering and Technology Surat Gujarat India Dept. of Comm. Engg. Sigma University Gujarat India The Maharaja Sayajirao University of Baroda Vadodara Gujarat India Dr. D. Y. Patil School of Science & Technology Tathawade Pune

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This research study explores the crucial domain of upholding textual authenticity by introducing a comprehensive method for identifying AI-generated content, employing BERT (Bidirectional Encoder Representations from Transformers). In a time when Artificial Intelligence (AI) significantly shapes written comm.nication, it becomes imperative to differentiate between text produced by humans and that generated by machines. The proposed approach utilizes the capabilities of BERT by delving into contextual embedding, revealing complex patterns that serve as indicators of AI origin. Through meticulous experimentation and evaluation, we substantiate the effectiveness of our method in precisely discerning AI-generated text. This contribution adds to the ongoing endeavors to safeguard the integrity of human-authored content in the ever-evolving digital landscape.

关键词： Technological innovation Shape Training data Text detection Bidirectional control Transformers Encoding

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Fuzzy Logic and AI Hybrid Prediction System to Evaluate Carbon Dioxide Fixation by Microalgae

Fuzzy Logic and AI Hybrid Prediction System to Evaluate Carb...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Gupta, Anand Kumar Bhandari, Narendra Singh Jain, Suchi Deogaonkar, Anant Goyal, Shweta Rayal, Ashish Tula's Institute Deptt. of Computer Science & Engg. U.K. Dehradun India School of Agriculture Graphic Era Hill University Dehradun India Tula's Institute Deptt. of Electronics & Comm. Engg. U.K. Dehradun India School of Management Ramdeobaba University Nagpur India Graphic Era Deemed to be University Deptt. of Electrical Engineering Dehradun India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun 248007 India

ISBN: (纸本)9798350379716

Microalgae (MA) are critical elements of the maritime ecosystem and play an essential role in ecological equilibrium. Furthermore, MAs are quite important financially. Nevertheless, MAs are too tiny, and just one droplet of seawater comprises a diverse range of MA types. Tracking and recognizing shifts in the types of MA is a hard endeavor. Considering the current challenges that the innovation faces, it is expected that the employment of MA fixed CO2 techniques in certain areas would result in a more optimal MA carbon sequestration procedure in the next years. Moreover, it provides a solid foundation and an advantageous structure for attaining environmentally friendly, low-carbon, and effective power usage, as well as completely executing sustainable growth and gradually moving toward the carbon maximum and carbon-neutral status. © 2024 IEEE.

关键词： Carbon dioxide Fuzzy logic microalgae

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DeepCare: Deep Learning-Based Smart Healthcare Framework using 5G Assisted Network Slicing

DeepCare: Deep Learning-Based Smart Healthcare Framework usi...

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2022 IEEE International Conference on Advanced Networks and Telecomm.nications Systems, ANTS 2022

作者： Basu, Deborsi Krishnakumar, Vikram Ghosh, Uttam Datta, Raja Indian Institute of Technology G. S. Sanyal School of Telecommunications Kharagpur India SASTRA Deemed Univ. Dept. of Electronics and Communication Engineering Tamil Nadu Thanjavur India School of Applied Computational Sciences MMC Dept. of Data Science and Computer Science United States Indian Institute of Technology Dept. of Electronics and Electrical Comm. Engg. Kharagpur India

ISBN: (纸本)9781665473408

5G and beyond comm.nication networks require satisfying very low latency standards, high reliability, high-speed user connectivity, more security, improved capacity and better service demands. Augmenting such a wide range of KPIs (Key Performance Indicators) needs a smart, intelligent and programmable solution for TSPs (Telecomm.nication Service Providers). Resource availability and quality sustainability are challenging parameters in a heterogeneous 5G environment. Programmable Dynamic Network Slicing (PDNS) is a key technology enabling parameter that can allow multiple tenants to bring their versatile applications simultaneously over shared physical infrastructure. Latest emerging technologies like virtualized Software-Defined Networks (vSDN) and Artificial Intelligence (AI) play a pivotal supporting role in solving the above-mentioned constraints. Using the PDNS framework, we have proposed a novel slice backup algorithm leveraging Deep Learning (DL) neural network to orchestrate network latency and load efficiently. Our model has been trained using the available KPIs and incoming traffic is analyzed. The proposed solution performs stable load balancing between shared slices even if certain extreme conditions (slice unavailability) through intelligent resource allocation. The framework withstands service outage and always select the most suitable slice as a backup. Our results show latency-aware resource distribution for better network stability. © 2022 IEEE.

关键词： Deep learning

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