Journal of Intelligent & Fuzzy Systems - Volume Pre-press, issue Pre-press

Authors: Ayub, Mohammed | El-Alfy, El-Sayed M.

Article Type: Research Article

Abstract: Energy is a critical resource for daily activities and lifestyles with direct impacts on the economy, health and environment. Therefore, monitoring its efficient use is essential to reduce energy waste and lessen related concerns such as global warming and climate change. One of the prominent and evolving solutions is Non-Intrusive Load Monitoring (NILM) smart meters, which enables consumers to track their per-appliance energy consumption more effectively. Some recent approaches have proposed deep learning as a powerful tool for energy disaggregation. However, it is difficult to employ these models in resource-constrained end devices for effective energy monitoring. In this paper, we …explore and evaluate a lightweight improved model for multi-target non-intrusive load monitoring based on MobileNet architectures. With extensive experiments using the ENERTALK dataset, the results show that MobileNetV3-large is the most appealing for energy disaggregation as it requires about 55% less storage for trained model and about 6% less training time than MobileNetV2 with almost the same performance. On average, version 3 large has a 17.63% reduction in SAE and requires 54.21% and 8.93% less space and less training time than version 2, respectively. Moreover, the average performance is boosted using an ensemble multi-target MobileNet model across all houses, leading to significant reduction of MAE, SAE, and RMSE errors of about 6%, 48%, and 4%, respectively. In comparison to other work, the proposed MMNet-NILM shows superior performance for the majority of appliances in terms of all considered evaluation metrics. Show more

Keywords: Multi-target MobileNet, ENERTALK, Lightweight NILM, energy disaggregation, ensemble MobileNet

DOI: 10.3233/JIFS-219426

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-22, 2024

Authors: Sindge, Renuka Sambhaji | Dutta, Maitreyee | Saini, Jagriti

Article Type: Research Article

Abstract: Video Super Resolution (VSR) applications extensively utilize deep learning-based methods. Several VSR methods primarily focus on improving the fine-patterns within reconstructed video frames. It frequently overlooks the crucial aspect of keeping conformation details, particularly sharpness. Therefore, reconstructed video frames often fail to meet expectations. In this paper, we propose a Conformation Detail-Preserving Network (CDPN) named as SuperVidConform. It focuses on restoring local region features and maintaining the sharper details of video frames. The primary focus of this work is to generate the high-resolution (HR) frame from its corresponding low-resolution (LR). It consists of two parts: (i) The proposed model decomposes …confirmation details from the ground-truth HR frames to provide additional information for the super-resolution process, and (ii) These video frames pass to the temporal modelling SR network to learn local region features by residual learning that connects the network intra-frame redundancies within video sequences. The proposed approach is designed and validated using VID4, SPMC, and UDM10 datasets. The experimental results show the proposed model presents an improvement of 0.43 dB (VID4), 0.78 dB (SPMC), and 0.84 dB (UDM10) in terms of PSNR. Further, the CDPN model set new standards for the performance of self-generated surveillance datasets. Show more

Keywords: Super-resolution, image super-resolution, video super-resolution, recurrent network, residual learning

DOI: 10.3233/JIFS-219393

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Ezeji, Ijeoma Noella | Adigun, Matthew | Oki, Olukayode

Article Type: Research Article

Abstract: The rise of decision processes in various sectors has led to the adoption of decision support systems (DSSs) to support human decision-makers but the lack of transparency and interpretability of these systems has led to concerns about their reliability, accountability and fairness. Explainable Decision Support Systems (XDSS) have emerged as a promising solution to address these issues by providing explanatory meaning and interpretation to users about their decisions. These XDSSs play an important role in increasing transparency and confidence in automated decision-making. However, the increasing complexity of data processing and decision models presents computational challenges that need to be investigated. …This review, therefore, focuses on exploring the computational complexity challenges associated with implementing explainable AI models in decision support systems. The motivations behind explainable AI were discussed, explanation methods and their computational complexities were analyzed, and trade-offs between complexity and interpretability were highlighted. This review provides insights into the current state-of-the-art computational complexity within explainable decision support systems and future research directions. Show more

Keywords: Explainable decision support systems, computational complexity, optimization, explainable artificial intelligence, review

DOI: 10.3233/JIFS-219407

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024

Authors: Vusirikkayala, Gowthami | Madhu Viswanatham, V.

Article Type: Research Article

Abstract: Detecting communities within a network is a critical component of network analysis. The process involves identifying clusters of nodes that exhibit greater similarity to each other compared to other nodes in the network. In the context of Complex networks (CN), community detection becomes even more important as these clusters provide relevant information of interest. Traditional mathematical and clustering methods have limitations in terms of data visualization and high-dimensional information extraction. To address these challenges, graph neural network learning methods have gained popularity in community detection, as they are capable of handling complex structures and multi-dimensional data. Developing a framework for …community detection in complex networks using graph neural network learning is a challenging and ongoing research objective. Therefore, it is essential for researchers to conduct a thorough review of community detection techniques that utilize cutting-edge graph neural network learning methods [102 ], in order to analyze and construct effective detection models. This paper provides a brief overview of graph neural network learning methods based on community detection methods and summarizes datasets, evaluation metrics, applications, and challenges of community detection in complex networks. Show more

Keywords: Community detection (CD), complex networks (CN), graph neural network (GNN), deep learning (DL), communities, clusters

DOI: 10.3233/JIFS-235913

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-24, 2024

Authors: Abu-Sharkh, Osama M.F. | Surkhi, Ibrahim | Zabin, Hamzah | Alhasan, Maher

Article Type: Research Article

Abstract: As the entire world is becoming increasingly a global village, the need for reliable, smooth, and easy-to-use applications that facilitate the communication process between people speaking different languages worldwide becomes essential, especially in the tourism industry. While numerous online and mobile applications attempt to bridge the linguistic gap using text-to-text, text-to-voice, or voice-to-text-to-voice translators, they often fall short due to constraints such as the need for a single shared device, manual setup of speaker’s gender and preferred language, and an inability to communicate from a distance. These applications struggle to mimic the practical nature of real-time multilingual conversations where immediate …and clear communication is paramount. This paper introduces an intelligent peer-to-peer polyglot voice-to-voice mobile application to facilitate the communication of people speaking different languages worldwide transparently mimicking a live conversation whether the involved parties are close to each other or at a nearby distance. People can interact with others transparently using their preferred language, irrespective of others’ languages, while the application automatically recognizes the language, gender of the speaker, and spoken words with very high accuracy. Five languages were implemented in the developed application as a proof-of-concept, and it is designed to smoothly and simply adapt more in future updates. Show more

Keywords: Multilingual, intelligent, text-to-voice, translation, voice-to-text

DOI: 10.3233/JIFS-219388

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-13, 2024

Authors: Martín-del-Campo-Rodríguez, C. | Batyrshin, Ildar | Sidorov, Grigori

Article Type: Research Article

Abstract: Word embeddings have been successfully used in diverse tasks of Natural Language Processing, including sentiment analysis and emotion classification, even though these embeddings do not contain any emotional or sentimental information. This article proposes a method to refine pre-trained embeddings with emotional and sentimental content. To this end, a Multi-output Neural Network is proposed to learn emotions and sentiments simultaneously. The resulting embeddings are tested in emotion classification and sentiment analysis tasks, showing an improvement compared with the pre-trained vectors and other proposes in the state-of-the-art for fine-grained emotion classification.

Keywords: Word embedding, multi-output neural network, VAD, polarity, emotion classification

DOI: 10.3233/JIFS-219354

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-8, 2024

Authors: Mathi, Senthilkumar | Jothi, Uma | Saravanan, G. | Ramalingam, Venkadeshan | Sreejith, K.

Article Type: Research Article

Abstract: Mobile devices have risen due to internet growth in recent years. The next generation of internet protocol is evolving for mobile devices to generate their addresses and get continuous services across networks to support the enormous number of addresses in network-based mobility. The mobile device updates its current location to its home network and the correspondent users through a binding update scheme in the visited network. Numerous studies have investigated binding update schemes to verify the reachability of the mobile device at its home network. However, most schemes endure security threats due to the incompetence of authenticating user identity and …concealing the temporary location of mobile devices. To address these issues, this paper proposes a secure and efficient binding update scheme (One-CLU) by incorporating a one-key-based cryptographically generated address (CGA) to validate and conceal the address ownership of mobile devices with minimal computations. The security correctness of the proposed One-CLU scheme is verified using AVISPA – a model checker. Finally, the simulation and the numerical results showthat the proposed scheme significantly reduces communication payloads and costs for the binding update, binding refresh, and packet delivery. Show more

Keywords: Mobile communication, routing, privacy, cryptography, communication security

DOI: 10.3233/JIFS-219422

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Al-Azani, Sadam | Almeshari, Ridha | El-Alfy, El-Sayed

Article Type: Research Article

Abstract: Speaker demographic recognition and segmentation analytics play a key role in offering personalized experiences across different automated industries and businesses. This paper aims at developing a multi-label demographic recognition system for Arabic speakers from audio and associated textual modalities. The system can detect age groups, genders, and dialects, but it can be easily extended to incorporate more demographic traits. The proposed method is based on deep learning for feature learning and recognition. Representations of audio modality are learned through 3D spectrogram and AlexNet CNN-based architecture. An AraBERT transformer is employed for learning representations of the textual modality. Additionally, a method …is provided for fusing audio and textual representations. The effectiveness of the proposed method is evaluated using the Saudi Audio Dataset for Arabic (SADA), which is a recently published database containing audio recordings of TV shows in different Arabic dialects. The experimental findings show that when using models with standalone modalities for multi-label demographic classification, textual modality using AraBERT performed better than the audio modality represented using 3D spectrogram along with AlexNet CNN-based architecture. Furthermore, when combining both modalities, audio and textual, significant improvement has been attained for all demographic traits. Show more

Keywords: Demographic, 3D spectrogram, AraBERT, multi-label classification, Arabic LLMs, multimodal deep learning

DOI: 10.3233/JIFS-219389

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024

Authors: Midula, P. | Shine, Linu | George, Neetha

Article Type: Research Article

Abstract: Fabrication of semiconductor wafers is a complex process and chances of defect wafers are high. Because of defective wafers the circuit patterns will not be created correctly and it is necessary to identify them. Manual identification of defects are time consuming and expensive. Deep learning methods are widely used for defect detection. In this paper we propose a simple Convolutional Neural Network (CNN) model for classification of nine defects in wafers. A custom CNN consisting of 9 layers is used for the classification of defects as Center, Donut, Edge-Loc, Edge-Ring, Loc, Random, Scratch, Near-full, and None. Performance of the model …is evaluated using WM-811K dataset. Results shows that the model classifies the defects with high confidence score and an accuracy of 99.1% is achieved using this method. Further, the convolution operation in the CNN is realized using Coordinate Rotation Digital Computer (CORDIC) algorithm. The model is implemented in Field Programmable Gate Arrays (FPGA) and proved less complex method and consume less computational power than conventional methods. Show more

Keywords: CNN, CORDIC, FPGA, wafer maps

DOI: 10.3233/JIFS-219430

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-9, 2024

Authors: Kaur, Amandeep | Rama Krishna, C. | Patil, Nilesh Vishwasrao

Article Type: Research Article

Abstract:  Software-Defined Networking (SDN) is a modern networking architecture that segregates control logic from data plane and supports a loosely coupled architecture. It provides flexibility in this advanced networking paradigm for any changes. Further, it controls the complete network in a centralized using controller(s). However, it comes with several security issues: Exhausting bandwidth and flow tables, Distributed Denial of Service (DDoS) attacks, etc. DDoS is a powerful attack for Internet-based applications and services, traditional and SDN paradigms. In the case of the SDN environment, attackers frequently target the central controller(s). This paper proposes a Kafka Streams-based real-time DDoS attacks classification approach …for the SDN environment, named KS-SDN-DDoS. The KS-SDN-DDoS has been designed using highly scalable H2O ML techniques on the two-node Apache Hadoop Cluster (AHC). It consists of two modules: (i) Network Traffic Capture (NTCapture) and (ii) Attack Detection and Traffic Classification (ADTClassification). The NTCapture is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-1). It captures incoming network traffic, extracts and formulates attributes, and publishes significant network traffic attributes on the Kafka topic. The ADTClassification is deployed on the two nodes Apache Kafka Streams Cluster (AKSC-2). It consumes network flows from the Kafka topic, classifies it based on the ten attributes, and publishes it to the decision Kafka topic. Further, it saves attributes with outcome to the Hadoop Distributed File System (HDFS). The KS-SDN-DDoS approach is designed and validated using the recent “DDoS Attack SDN dataset”. The result shows that the proposed system gives better classification accuracy (100%). Show more

Keywords: Control plane, real-time, dynamic network, Apache Hadoop, data plane, Kafka streams

DOI: 10.3233/JIFS-219405

Citation: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-12, 2024