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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

CAAI Transactions on Intelligence Technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Interpretable Arabic Hate Speech Detection using Large Language Model

Interpretable Arabic Hate Speech Detection using Large Langu...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Majed Alwateer Ibrahim Gad Mahmoud Elmarhomy Ghada Elmarhomy Hasan Hashim Malik Almaliki El-Sayed Atlam Department of Computer Science College of Computer Science and Engineering Taibah University Saudi Arabia Computer Science Department Faculty of Science Tanta University Tanta Egypt Computer Science and Information College of Applied Taibah University Saudi Arabia Department of Information Systems College of Computer Science and Engineering Taibah University Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Social media platforms serve as significant spaces for users to have conversations, discussions and express their opinions. However, anonymity provided to users on these platforms allows the spread of hate speech and other offensive material. Due to the wide-ranging nature of these platforms, there is a critical need to automatically detect and report occurrences of hate speech. There are various detection methods, but many of them operate as black boxes, lacking interpretability and explainability by design. To address the lack of interpretability, this study explores the development of an interpretable framework to detect hate speech in Arabic using large language models (LLMs). The proposed approach combines advanced natural language processing techniques with interpretable machine learning methods to enhance understanding of model decisions. The experimental results demonstrate that the model achieves high accuracy while maintaining interpretability, enabling users to understand the reasoning behind the detections. The proposed method achieves an accuracy of 0.846%, with a precision of 0.843% and a recall of 0.846%, outperforming existing Arabic hate speech detection models. These results show the effectiveness of combining LLM with interpretability for this critical task, providing a reliable and transparent solution for automated moderation of harmful content.

关键词： Technological innovation Accuracy Social networking (online) Smart cities Large language models Hate speech Text categorization Writing Reliability Stakeholders

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Optimizing Big Data Implementation to Create Business Value and Architecture Proposed in the Banking Industry: A Systematic Review

Optimizing Big Data Implementation to Create Business Value ...

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2023 International Conference on computer science, information Technology and Engineering, ICCoSITE 2023

作者： Kristiana, Idha Meyliana, Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The exponential growth of data is compelling organizations to employ data in decision-making. As one of the businesses with an ecosystem that contributes to data growth, banks have challenges in generating insight. A high level of data security is frequently linked to a high level of data access difficulties. This poses a challenge to the implementation of data-driven business, where taking control of our data is one of the best ways to ensure that we not only own data but also have the ability to process and use data to extract business value. Through literature review, a number of challenges to optimizing the implementation of big data analytics in the banking industry were discovered. Data governance refers to the methods and procedures that assist banks in managing and securing data. A big data architecture is presented to address the highlighted issues, particularly with a multi-tiered approach to big data structures. With the adoption of this architecture, it will be simpler to generate business-value-generating insights for the banking industry using big data with accessibility and protection of data. © 2023 IEEE.

关键词： Big data

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Assessing the Effects of Blackhole Attacks on MANET Reliability and Security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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A New Classification Method for Drone-Based Crops in Smart Farming

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2022年第9期16卷 164-174页

作者： Al-Rami, Bandar Alheeti, Khattab M. Ali Aldosari, Waleed M. Alshahrani, Saeed Matar Al-Abrez, Shahad Mahdi Dean of Faculty of Computer Studies Arab open University Riyadh Saudi Arabia College of Computer Sciences and Information Technology Computer Networking Systems Department University of Anbar Ramadi Iraq Department of Computer Engineering Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Faculty of Computer Science and Information Technology Saudi Electronic University Riyadh Saudi Arabia

During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for in-creasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and re-mote sensing usage for many different applications such as crop classification, crop health monitoring and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolution-al Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93% © 2022. International Journal of Interactive Mobile *** Rights Reserved.

关键词： Drones

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Security Risks and Best Practices for Blockchain and Smart Contracts: A Systematic Literature Review 8

Security Risks and Best Practices for Blockchain and Smart C...

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8th International Conference on information Management and Technology, ICIMTech 2023

作者： Yulianto, Semi Hendric Spits Warnars, Harco Leslie Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Bina Nusantara University Binus Graduate Program - Doctor of Computer Science Computer Science Department Jakarta11480 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia

ISBN: (纸本)9798350326093

The rise of blockchain technology and smart contracts has brought widespread attention due to their capacity to transform multiple industrial sectors through decentralized, transparent, secure transactions. However, despite their promise to revolutionize various fields worldwide, lingering concerns regarding security risks impede their adoption rate. Addressing these concerns is crucial now more than ever;therefore, we conducted a comprehensive literature review within our study's scope that focused on published papers between 2014-2023 centered around security risks concerning blockchain and smart contracts. Our systematic approach using the PRISMA checklist analyzed nine categorized research model-based primary studies while recognizing vulnerabilities in smart contract development and providing best practices to mitigate such issues. These findings benefit both researchers and practitioners as they showcase how acknowledging these vulnerabilities can further develop into exploring more significant aspects of blockchain technology's security issues and smart contract development processes. Our study contributes significantly by expanding knowledge in this field while providing novel insights valuable for individuals involved in designing or implementing blockchain technologies. © 2023 IEEE.

关键词： Smart contract

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Efficient Dynamic Routing in Elastic Optical Networks Based on Traffic Prediction and Bandwidth Reservation 23

Efficient Dynamic Routing in Elastic Optical Networks Based ...

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23rd International Conference on Transparent Optical Networks, ICTON 2023

作者： Goścień, Róza Wroclaw University of Science and Technology Faculty of Information and Communication Technologies Department of Systems and Computer Network Wyb. Wyspianskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9798350303032

In this paper, we study the problem of efficient dynamic routing in elastic optical networks. We propose a novel resource allocation strategy that benefits from the bandwidth reservation in advance based on the analysis of the traffic prediction. We also propose a mechanism for the strategy's partial implementation to provide its high flexibility and the possibility to control both the efficiency and processing time. Next, we perform numerical experiments on representative European network topology. Their aim is the selection of the strategy's beneficial configuration as well as its performance evaluation. The results prove its high efficiency - the approach reduces the bandwidth blocking probability up to 0.85%, corresponding to 16.925 Tbps of more served traffic (compared to the allocation without traffic-aware bandwidth reservation). © 2023 IEEE.

关键词： Network topology

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Interpretable Arabic Hate Speech Detection using Large Language Model 2

Interpretable Arabic Hate Speech Detection using Large Langu...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Alwateer, Majed Gad, Ibrahim Elmarhomy, Mahmoud Elmarhomy, Ghada Hashim, Hasan Almaliki, Malik Atlam, El-Sayed College of Computer Science and Engineering Taibah University Department of Computer Science Saudi Arabia Tanta University Faculty of Science Computer Science Department Tanta31527 Egypt College of Applied Taibah University Computer Science and Information Saudi Arabia College of Computer Science and Engineering Taibah University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331506995

关键词： Arabic Hate Speech Detection Large Language Mode XAI

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Mobile Robots through Task-Based Human Instructions using Incremental Curriculum Learning

Mobile Robots through Task-Based Human Instructions using In...

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IEEE Conference on Cybernetics and Intelligent systems

作者： Muhammad A. Muttaqien Ayanori Yorozu Akihisa Ohya Faculty of Systems and Information Engineering Computer Science University of Tsukuba Tsukuba Japan

ISBN: (数字)9798350364194

ISBN: (纸本)9798350364200

This paper explores the integration of incremental curriculum learning (ICL) with deep reinforcement learning (DRL) techniques to facilitate mobile robot navigation through task-based human instruction. By adopting a curriculum that mirrors the progressive complexity encountered in human learning, our approach systematically enhances robots’ ability to interpret and execute complex instructions. We explore the principles of DRL and its synergy with ICL, demonstrating how this combination not only improves training efficiency but also equips mobile robots with the generalization capability required for navigating through dynamic indoor environments. Empirical results indicate that robots trained with our ICL-enhanced DRL framework outperform those trained without curriculum learning, highlighting the benefits of structured learning progressions in robotic training.

关键词： Training Visualization Mechatronics Navigation Random access memory Deep reinforcement learning Indoor environment

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Towards the Automatic Generation of Pedagogical Conversational Agents from Lecture Slides 1

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3rd EAI International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2021

作者： Wölfel, Matthias Faculty of Computer Science and Business Information Systems Karlsruhe University of Applied Sciences Karlsruhe Germany

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

Although corresponding technological and didactical models have been known for decades, the digitization of teaching has hardly advanced beyond simple non-interactive formats (e.g. downloadable slides are provided within a learning management system). The COVID-19 crisis is changing this situation dramatically, creating a high demand for highly interactive formats and fostering exchange between conversation partners about the course content. systems are required that are able to communicate with students verbally, to answer their questions, and to check the students’ knowledge. While technological advances have made such systems possible in principle, the game stopper is the large amount of manual work and knowledge that must be put into designing such a system and feeding it the right content. In this publication, we present a first system to overcome the aforementioned drawback by automatically generating a corresponding dialog system from slide-based presentations, such as PowerPoint, OpenOffice, or Keynote, which can be dynamically adapted to the respective students and their needs. Our first experiments confirm the proof of concept and reveal that such a system can be very handy for both respective groups, learners and lecturers, alike. The limitations of the developed system, however, also reminds us that many challenges need to be addressed to improve the feasibility and quality of such systems, in particular in the understanding of semantic knowledge. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Students

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