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10th IEEE International Conference on Intelligent Computing and Information systems, ICICIS 2021

作者： Samir, Ahmed M. Rady, Sherine Gharib, Tarek F. Kafrelsheikh University Faculty of Computer and Information Sciences Software Engineering Department Kafrelsheikh Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9781665440769

The problem of detecting top influential users in social networks is known as the influence maximization problem. Top social network influencers play a significant role in different fields like virtual marketing, politics, and public health. There are various techniques and methods implemented to identify the top influential users for maximizing the influence within social networks. There exist, however, a real challenge to effectively reach those top influential users with both high accuracy and less computational time. This paper proposes an efficient approach for the problem of influence maximization based on communities' identification. A proposed approach LCS includes three stages: firstly, the application of the Louvain community detection algorithm to detect the different communities of the social network graph. Secondly, executing the CRANK algorithm to rank and sort the generated communities-based CRANK main four metrics. Finally, the selection of the top seed nodes from the sorted communities using degree centrality. Experimental results conducted on three social network datasets, under an Independent Cascade model for verification, show the effectiveness of the proposed approach compared to the centrality-based heuristics. Additionally, comparable results are achieved against greedy algorithms with far less time complexity. © 2021 IEEE.

关键词： Economic and social effects

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UVM Based Verification Framework for Deep Learning Hardware Accelerator: Case Study

UVM Based Verification Framework for Deep Learning Hardware ...

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Electrical, computer and Energy Technologies (ICECET), International Conference on

作者： Randa Aboudeif Tasneem A. Awaad Mohamed AbdElsalam Yehea Ismail Electronics and Communications Engineering Department The American University in Cairo Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Siemens Digital Industries Software Ain Shams University Cairo Egypt Siemens Digital Industries Software Cairo Egypt Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Hardware Verification of Deep Learning Accelerators (DLAs) has become critically important for testing the reliability and trustworthiness of Learning Enabled Autonomous systems (LEAS). In this paper, we introduce a scalable, reusable, and efficient hardware verification framework for DLAs using the Universal verification methodology (UVM). The verification environment is focused on testing the inference functionality of the perception module in LEAS for different DLA designs. Moreover, the verification environment is portable across simulation and hardware-assisted verification platforms for emulation and FPGA prototyping. To assess our proposed UVM design methodology, we have applied it to the Nvidia Deep Learning Accelerator (NVDLA), an open-source core for DLAs as a case study.

关键词： Deep learning Runtime Design methodology Emulation Stability analysis Convolutional neural networks Reliability Hardware acceleration Field programmable gate arrays Testing

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Parkinson’s Detection Using RNN-Graph-LSTM with Optimization Based on Speech Signals

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computers, Materials & Continua 2022年第7期72卷 871-886页

作者： Ahmed S.Almasoud Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abubakar Elsafi Mesfer Al Duhayyim Ishfaq Yaseen Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityMahayil62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science College of Science and Artsat MahayilKing Khalid UniversityMahayil62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityMahayil62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj62529Saudi Arabia

Early detection of Parkinson’s Disease(PD)using the PD patients’voice changes would avoid the intervention before the identification of physical *** machine learning algorithms were developed to detect PD ***,these ML methods are lack in generalization and reduced classification performance due to subject *** overcome these issues,this proposed work apply graph long short term memory(GLSTM)model to classify the dynamic features of the PD patient speech *** proposed classification model has been further improved by implementing the recurrent neural network(RNN)in batch normalization layer of GLSTM and optimized with adaptive moment estimation(ADAM)on network hidden *** consider the importance of feature engineering,this proposed system use Linear Discriminant analysis(LDA)for dimensionality reduction and SparseAuto-Encoder(SAE)for extracting the dynamic speech *** on the computation of energy content transited from unvoiced to voice(onset)and voice to voiceless(offset),dynamic features are *** PD datasets is evaluated under 10 fold cross validation without sample *** proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy,sensitivity,and specificity andMatthew correlation *** evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.

关键词： Dimensionality reduction LDA feature extraction auto encoder classification recurrent neural network LSTM and optimization

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Opinion Formation and Social Impact in Networks with Natural and Artificial Intelligence systems

Opinion Formation and Social Impact in Networks with Natural...

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Dmitriy Tsarev Anna Avdyushina Alexander Alodjants Institute of Advanced Data Transfer Systems ITMO University St. Petersburg Russia Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia

We suggest a new quantum-like approach to study distributed intelligence systems (DIS) consisting of natural (owners) and artificial (avatars) intelligence agents organized in a scale-free network. We demonstrate the complex features of social impact in DIS that imply the hierarchical formation of opinion leaders in the network and their topological protection. We elucidated the effect of significant narrowing of the spread of opinions within the network when most owners chose their position as limiting (extreme).

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Novel Approach for Enhancing Mental Well-Being Through Machi...

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Advancements in Computing (ICAC), International Conference on

作者： O.V.D.E Roshani H.M.K.J.J Ayuwardhana P.H.M.S Rodrigo R.U Hewageegana Harinda Fernando Dilshan I. de Silva Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.

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RTL implementation of MCMC-based constraints solver 16

RTL implementation of MCMC-based constraints solver

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2021 International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design, SMACD 2021 and 16th Conference on PhD Research in Microelectronics and Electronics, PRIME 2021

作者： Ahmed, Moemen Ahmed, Youssef Nagy, Younan Adbel-Rahman, Manar Salah, Khaled El-Kharashi, M. Watheq Khan, Ayub Department of Computer and Systems Faculty of Engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Fremont United States

ISBN: (纸本)9783800755899

Functional hardware verification is one of the most challenging areas in the hardware design cycle. With the increase in the complexity and size of the design, the time needed for verification becomes the largest part of the total design time. The most recent technique in practical verification is constrained random simulation. This method needs a solver to produce random input stimuli that satisfies a pre-defined set of input constraints. The efficiency of the overall verification process depends critically on the speed of the constraints solver and the distribution of the generated solutions. In this paper, we propose hardware acceleration for RTL constraints solver integrated with SystemVerilog. The solver is based on Markov Chains Monte Carlo methods. © VDE VERLAG GMBH.

关键词： Monte Carlo methods

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Smart Farmer Education System for Disease Identification, Severity Assessment and Infestation Management (Brinjal) 17

Smart Farmer Education System for Disease Identification, Se...

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17th IEEE International Conference on Industrial and Information systems, ICIIS 2023

作者： Bandara, Keshara Vidhanaarachchi, Samitha Nanayakkara, A.A.E. Kulasinghe, Kavindu Rajapakshe, Sanka Wickramasinghe, Asela Yapa, Kanishka Sri Lanka Institute of Information Technology Department of Information Technology New Kandy Road Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka University of Ruhuna Faculty of Agriculture Department of Soil Science Mapalana Kamburupitiya Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350323634

The agriculture industry is one of the most significant sources of foreign exchange and employment in the Sri Lankan market. Therefore, small crops play a crucial role in ensuring the food security of the population as they are integral components of Sri Lankan cuisine. However, industry experts have identified inefficient disease and pest management as a hindrance to production. This issue requires immediate attention and proper action, which can be effectively addressed through the utilization of ICT technologies. Consequently, this research paper proposes a smart system that aims to assist farmers and agriculture professionals by providing them with farmer education, disease and infestation management tools, as well as progression level calculations. The implementation of this system aims to uplift the agriculture industry in Sri Lanka. The developed 'AI-based mobile application system for Brinjal Diseases,' which is based on analyzing the eating patterns of leafhopper damage, presents an innovative approach to identify the most significant threats to Brinjal, specifically focusing on leafhopper damage. This proposed system effectively informs various stakeholders, including the Area Agricultural Research Officer, Seed Certification and Plant Protection Center (SCPPC), Agricultural Service Center, Office of the Register of Pesticides (ROP), Plant Protection Service (PPS), as well as farmers, regarding the dispersion and prevention of brinjal diseases and pest infections. © 2023 IEEE.

关键词： Crowdsourcing

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Materials Behaviour Analysis of 3D Printed Brass-PLA Filament 7

Materials Behaviour Analysis of 3D Printed Brass-PLA Filamen...

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7th International Conference on Mechanical engineering Research 2023, ICMER 2023

作者： Rajan, Dinesh Samykano, M. Suraparaju, S.K. Moorthy, K. Kadirgama, K. Ramasamy, D. Pandey, A.K. Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Centre for Research in Advanced Fluid and Process University Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Pahang Kuantan Gambang26300 Malaysia Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia

The current study aims to bridge a crucial gap in existing research, potentially paving the way for a groundbreaking transformation in the development and application of PLA/Brass composites within diverse industries such as aerospace, automotive, consumer goods, and medical devices. The primary objective of this research is to assess the mechanical properties of a composite material made up of Polylactic Acid (PLA) and Brass, produced using Fused Deposition Modelling (FDM) 3D printing technology. Brass, renowned for its exceptional mechanical properties, has been integrated into PLA to form this composite material. The study employs various analytical techniques, including Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDX), to scrutinize the chemical and physical characteristics of the PLA/Brass composite. This research revolves around exploring the impact of different printing parameters on the mechanical behavior of the printed specimens. The investigation delves into aspects such as tensile strength, compression resistance, bending properties, and impact resistance. To achieve this, test specimens with varying compositions have been produced using a Raise3D N2 Plus FDM 3D printer, with careful manipulation of printing parameters such as layer height and printing speed. The compositional variations range from 15% wt. to 80% wt., with layer height values spanning 0.25 mm, 0.30 mm, and 0.35 mm, and printing speeds ranging from 20 mm/s to 40 mm/s. The outcomes of this research have revealed the distinct influences of specific printing parameters on various mechanical properties. For example, in the context of tensile testing, it was observed that the combination of a layer height of 0.25 mm and a printing speed of 30 mm/s resulted in the highest elastic modulus. Similarly, the study provides crucial insights into optimizing PLA/Brass composite material properties through controlled addit

关键词： Scanning electron microscopy

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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