Open Access Published since September 1, 1991

Journal of Intelligent Systems

ISSN: 2191-026X

Editor-in-chief: Hasan Fleyeh

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Special Issues:

"Trustworthy Artificial Intelligence for Big Data-Driven Research Applications based on Internet of Everythings"
Guest Editors:
Prof. Dr Mazin Abed Mohammed, College of Computer Science and Information Technology, University of Anbar, Iraq
Dr Korhan Cengiz, Republic of Turkey Trakya University, Turkey
Submission Deadline: 30.12.2022
"Current IoT Trends, Issues, and Future Potential Using AI & Machine Learning Techniques"
Guest Editor:
Dr Mohd Dilshad Ansari, CMR College of Engineering & Technology, Hyderabad, India
Submission Deadline: 30.12.2022
"Intelligent Robotics for Smart Cities"
Guest Editors:
Dr. Muhammad Attique Khan [Managing Guest Editor], HITEC University, Taxila, Pakistan
Dr. Gaurav Dhiman [Co-Guest Editor], University Centre for Research and Development, Chandigarh University, Gharuan, Mohali, India
Dr. Sathishkumar V E [Co-Guest Editor], Hanyang University, Seoul, Republic of Korea
Submission Deadline: 26.02.2023
International Conference on Computing Communication & Informatics (ICCCI) 2022

Objective
The Journal of Intelligent Systems provides readers with a compilation of stimulating and up-to-date articles within the field of intelligent systems. The focus of the journal is on high quality research that addresses paradigms, development, applications and implications in the field of intelligent systems. The Journal of Intelligent Systems is a peer reviewed journal. Special issues can be arranged by contacting the Editor.

Topics
The list of topics spans all the areas of modern intelligent systems such as:

Artificial Intelligence
Deep Learning
Machine Learning
Pattern Recognition
Artificial Neural Networks
Reinforcement Learning
Supervised, Semi-Supervised and Unsupervised Learning
Computer Vision
AI Powered Robotic Systems
Natural Language Processing
AI Powered Internet of Things
Image and Video Processing and Analysis
Data Mining
Bayesian Learning
Intelligent Agents and Multi-Agent Systems

Article formats
Research articles, Review papers, Short research communications, Essays, Book reviews

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Papers accepted for publication Journal of Intelligent Systems are subject to Article Processing Charges (APCs). The current publication fee is 1000 EUR.

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Volume 32 Issue 1

January 2023

Research Articles

Open Access January 12, 2023

Salp swarm and gray wolf optimizer for improving the efficiency of power supply network in radial distribution systems

Ihsan Salman, Khalid Mohammed Saffer, Hayder H. Safi, Salama A. Mostafa, Bashar Ahmad Khalaf

Article number: 20220221

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Abstract

The efficiency of distribution networks is hugely affected by active and reactive power flows in distribution electric power systems. Currently, distributed generators (DGs) of energy are extensively applied to minimize power loss and improve voltage deviancies on power distribution systems. The best position and volume of DGs produce better power outcomes. This work prepares a new hybrid SSA–GWO metaheuristic optimization algorithm that combines the salp swarm algorithm (SSA) and the gray wolf optimizer (GWO) algorithm. The SSA–GWO algorithm ensures generating the best size and site of one and multi-DGs on the radial distribution network to decrease real power losses (RPL) (kW) on lines and resolve voltage deviancies. Our novel algorithm is executed on IEEE 123-bus radial distribution test systems. The results confirm the success of the suggested hybrid SSA–GWO algorithm compared with implementing the SSA and GWO individually. Through the proposed SSA–GWO algorithm, the study decreases the RPL and improves the voltage profile on distribution networks with multiple DGs units.

Open Access January 12, 2023

Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks

Firas Mohammed Aswad, Ali Mohammed Saleh Ahmed, Nafea Ali Majeed Alhammadi, Bashar Ahmad Khalaf, Salama A. Mostafa

Article number: 20220155

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Abstract

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F -measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%.

Open Access January 12, 2023

On numerical characterizations of the topological reduction of incomplete information systems based on evidence theory

Changqing Li, Yanlan Zhang

Article number: 20220214

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Abstract

Knowledge reduction of information systems is one of the most important parts of rough set theory in real-world applications. Based on the connections between the rough set theory and the theory of topology, a kind of topological reduction of incomplete information systems is discussed. In this study, the topological reduction of incomplete information systems is characterized by belief and plausibility functions from evidence theory. First, we present that a topological space induced by a pair of approximation operators in an incomplete information system is pseudo-discrete, which deduces a partition. Then, the topological reduction is characterized by the belief and plausibility function values of the sets in the partition. A topological reduction algorithm for computing the topological reducts in incomplete information systems is also proposed based on evidence theory, and its efficiency is examined by an example. Moreover, relationships among the concepts of topological reduct, classical reduct, belief reduct, and plausibility reduct of an incomplete information system are presented.

Open Access January 19, 2023

A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor

K. V. Archana, G. Komarasamy

Article number: 20220206

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Abstract

In the case of magnetic resonance imaging (MRI) imaging, image processing is crucial. In the medical industry, MRI images are commonly used to analyze and diagnose tumor growth in the body. A number of successful brain tumor identification and classification procedures have been developed by various experts. Existing approaches face a number of obstacles, including detection time, accuracy, and tumor size. Early detection of brain tumors improves options for treatment and patient survival rates. Manually segmenting brain tumors from a significant number of MRI data for brain tumor diagnosis is a tough and time-consuming task. Automatic image segmentation of brain tumors is required. The objective of this study is to evaluate the degree of accuracy and simplify the medical picture segmentation procedure used to identify the type of brain tumor from MRI results. Additionally, this work suggests a novel method for identifying brain malignancies utilizing the Bagging Ensemble with K-Nearest Neighbor (BKNN) in order to raise the KNN’s accuracy and quality rate. For image segmentation, a U-Net architecture is utilized first, followed by a bagging-based k-NN prediction algorithm for classification. The goal of employing U-Net is to improve the accuracy and uniformity of parameter distribution in the layers. Each decision tree is fitted on a little different training dataset during classification, and the bagged decision trees are effective since each tree has minor differences and generates slightly different skilled predictions. The overall classification accuracy was up to 97.7 percent, confirming the efficiency of the suggested strategy for distinguishing normal and pathological tissues from brain MR images; this is greater than the methods that are already in use.

Open Access January 26, 2023

Detecting biased user-product ratings for online products using opinion mining

Akanksha Bansal Chopra, Veer Sain Dixit

Article number: 20229030

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Abstract

Collaborative filtering recommender system (CFRS) plays a vital role in today’s e-commerce industry. CFRSs collect ratings from the users and predict recommendations for the targeted product. Conventionally, CFRS uses the user-product ratings to make recommendations. Often these user-product ratings are biased. The higher ratings are called push ratings (PRs) and the lower ratings are called nuke ratings (NRs). PRs and NRs are injected by factitious users with an intention either to aggravate or degrade the recommendations of a product. Hence, it is necessary to investigate PRs or NRs and discard them. In this work, opinion mining approach is applied on textual reviews that are given by users for a product to detect the PRs and NRs. The work also examines the effect of PRs and NRs on the performance of CFRS by evaluating various measures such as precision, recall, F -measure and accuracy.

Open Access February 16, 2023

Evaluation and analysis of teaching quality of university teachers using machine learning algorithms

Ying Zhong

Article number: 20220204

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Abstract

In order to better improve the teaching quality of university teachers, an effective method should be adopted for evaluation and analysis. This work studied the machine learning algorithms and selected the support vector machine (SVM) algorithm to evaluate teaching quality. First, the principles of selecting evaluation indexes were briefly introduced, and 16 evaluation indexes were selected from different aspects. Then, the SVM algorithm was used for evaluation. A genetic algorithm (GA)-SVM algorithm was designed and experimentally analyzed. It was found that the training time and testing time of the GA-SVM algorithm were 23.21 and 7.25 ms, both of which were shorter than the SVM algorithm. In the evaluation of teaching quality, the evaluation value of the GA-SVM algorithm was closer to the actual value, indicating that the evaluation result was more accurate. The average accuracy of the GA-SVM algorithm was 11.64% higher than that of the SVM algorithm (98.36 vs 86.72%). The experimental results verify that the GA-SVM algorithm can have a good application in evaluating and analyzing teaching quality in universities with its advantages in efficiency and accuracy.

Open Access February 22, 2023

Efficient mutual authentication using Kerberos for resource constraint smart meter in advanced metering infrastructure

Md Mehedi Hasan, Noor Afiza Mohd Ariffin, Nor Fazlida Mohd Sani

Article number: 20210095

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Abstract

The continuous development of information communication technology facilitates the conventional grid in transforming into an automated modern system. Internet-of-Things solutions are used along with the evolving services of end-users to the electricity service provider for smart grid applications. In terms of various devices and machine integration, adequate authentication is the key to an accurate source and destination in advanced metering infrastructure (AMI). Various protocols are deployed to lead the identification between two parties, which require high computation time and communicational bit operations for system development. Therefore, Kerberos-based authentication protocols were designed in this study with the assistance of elliptic curve cryptography to manage the mutual authentication between two parties and reduce the time and bit operations. The protocols were evaluated in a widely adopted tool, AVISPA, which builds an understanding of the proposed protocol and ensures mutual authentication without unauthorized knowledge. In addition, upon comparing security and performance assessments to the current schemes, it was found that the protocol in this study required less time and bits to transmit information. Consequently, it effectively provides multiple security features making it suitable for resource constraint smart meters in AMI.

Open Access February 23, 2023

Recognition of English speech – using a deep learning algorithm

Shuyan Wang

Article number: 20220236

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Abstract

The accurate recognition of speech is beneficial to the fields of machine translation and intelligent human–computer interaction. After briefly introducing speech recognition algorithms, this study proposed to recognize speech with a recurrent neural network (RNN) and adopted the connectionist temporal classification (CTC) algorithm to align input speech sequences and output text sequences forcibly. Simulation experiments compared the RNN-CTC algorithm with the Gaussian mixture model–hidden Markov model and convolutional neural network-CTC algorithms. The results demonstrated that the more training samples the speech recognition algorithm had, the higher the recognition accuracy of the trained algorithm was, but the training time consumption increased gradually; the more samples a trained speech recognition algorithm had to test, the lower the recognition accuracy and the longer the testing time. The proposed RNN-CTC speech recognition algorithm always had the highest accuracy and the lowest training and testing time among the three algorithms when the number of training and testing samples was the same.

Open Access February 24, 2023

A new method for writer identification based on historical documents

Abdeljalil Gattal, Chawki Djeddi, Faycel Abbas, Imran Siddiqi, Brahim Bouderah

Article number: 20220244

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Abstract

Identifying the writer of a handwritten document has remained an interesting pattern classification problem for document examiners, forensic experts, and paleographers. While mature identification systems have been developed for handwriting in contemporary documents, the problem remains challenging from the viewpoint of historical manuscripts. Design and development of expert systems that can identify the writer of a questioned manuscript or retrieve samples belonging to a given writer can greatly help the paleographers in their practices. In this context, the current study exploits the textural information in handwriting to characterize writer from historical documents. More specifically, we employ oBIF(oriented Basic Image Features) and hinge features and introduce a novel moment-based matching method to compare the feature vectors extracted from writing samples. Classification is based on minimization of a similarity criterion using the proposed moment distance. A comprehensive series of experiments using the International Conference on Document Analysis and Recognition 2017 historical writer identification dataset reported promising results and validated the ideas put forward in this study.

Open Access February 25, 2023

Intelligent gloves: An IT intervention for deaf-mute people

Amal Babour, Hind Bitar, Ohoud Alzamzami, Dimah Alahmadi, Amal Barsheed, Amal Alghamdi, Hanadi Almshjary

Article number: 20220076

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Abstract

Deaf-mute people have much potential to contribute to society. However, communication between deaf-mutes and non-deaf-mutes is a problem that isolates deaf-mutes from society and prevents them from interacting with others. In this study, an information technology intervention, intelligent gloves (IG), a prototype of a two-way communication glove, was developed to facilitate communication between deaf-mutes and non-deaf-mutes. IG consists of a pair of gloves, flex sensors, an Arduino nano, a screen with a built-in microphone, a speaker, and an SD card module. To facilitate communication from the deaf-mutes to the non-deaf-mutes, the flex sensors sense the hand gestures and connected wires, and then transmit the hand movement signals to the Arduino nano where they are translated into words and sentences. The output is displayed on a small screen attached to the gloves, and it is also issued as voice from the speakers attached to the gloves. For communication from the non-deaf-mutes to the deaf-mute, the built-in microphone in the screen senses the voice, which is then transmitted to the Arduino nano to translate it to sentences and sign language, which are displayed on the screen using a 3D avatar. A unit testing of IG has shown that it performed as expected without errors. In addition, IG was tested on ten participants, and it has been shown to be both usable and accepted by the target users.

Open Access March 21, 2023

Reinforcement learning with Gaussian process regression using variational free energy

Kiseki Kameda, Fuyuhiko Tanaka

Article number: 20220205

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Abstract

The essential part of existing reinforcement learning algorithms that use Gaussian process regression involves a complicated online Gaussian process regression algorithm. Our study proposes online and mini-batch Gaussian process regression algorithms that are easier to implement and faster to estimate for reinforcement learning. In our algorithm, the Gaussian process regression updates the value function through only the computation of two equations, which we then use to construct reinforcement learning algorithms. Our numerical experiments show that the proposed algorithm works as well as those from previous studies.

Open Access March 25, 2023

Anti-leakage method of network sensitive information data based on homomorphic encryption

Junlong Shi, Xiaofeng Zhao

Article number: 20220281

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Abstract

With the development of artificial intelligence, people begin to pay attention to the protection of sensitive information and data. Therefore, a homomorphic encryption framework based on effective integer vector is proposed and applied to deep learning to protect the privacy of users in binary convolutional neural network model. The conclusion shows that the model can achieve high accuracy. The training is 93.75% in MNIST dataset and 89.24% in original dataset. Because of the confidentiality of data, the training accuracy of the training set is only 86.77%. After increasing the training period, the accuracy began to converge to about 300 cycles, and finally reached about 86.39%. In addition, after taking the absolute value of the elements in the encryption matrix, the training accuracy of the model is 88.79%, and the test accuracy is 85.12%. The improved model is also compared with the traditional model. This model can reduce the storage consumption in the model calculation process, effectively improve the calculation speed, and have little impact on the accuracy. Specifically, the speed of the improved model is 58 times that of the traditional CNN model, and the storage consumption is 1/32 of that of the traditional CNN model. Therefore, homomorphic encryption can be applied to information encryption under the background of big data, and the privacy of the neural network can be realized.

Open Access March 29, 2023

An intelligent algorithm for fast machine translation of long English sentences

Hengheng He

Article number: 20220257

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Abstract

Translation of long sentences in English is a complex problem in machine translation. This work briefly introduced the basic framework of intelligent machine translation algorithm and improved the long short-term memory (LSTM)-based intelligent machine translation algorithm by introducing the long sentence segmentation module and reordering module. Simulation experiments were conducted using the public corpus and the local corpus containing self-collected linguistic data. The improved algorithm was compared with machine translation algorithms based on a recurrent neural network and LSTM. The results suggested that the LSTM-based machine translation algorithm added with the long sentence segmentation module and reordering module effectively segmented long sentences and translated long English sentences more accurately, and the translation was more grammatically correct.

Open Access March 31, 2023

A lattice-transformer-graph deep learning model for Chinese named entity recognition

Min Lin, Yanyan Xu, Chenghao Cai, Dengfeng Ke, Kaile Su

Article number: 20222014

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Abstract

Named entity recognition (NER) is the localization and classification of entities with specific meanings in text data, usually used for applications such as relation extraction, question answering, etc. Chinese is a language with Chinese characters as the basic unit, but a Chinese named entity is normally a word containing several characters, so both the relationships between words and those between characters play an important role in Chinese NER. At present, a large number of studies have demonstrated that reasonable word information can effectively improve deep learning models for Chinese NER. Besides, graph convolution can help deep learning models perform better for sequence labeling. Therefore, in this article, we combine word information and graph convolution and propose our Lattice-Transformer-Graph (LTG) deep learning model for Chinese NER. The proposed model pays more attention to additional word information through position-attention, and therefore can learn relationships between characters by using lattice-transformer. Moreover, the adapted graph convolutional layer enables the model to learn both richer character relationships and word relationships and hence helps to recognize Chinese named entities better. Our experiments show that compared with 12 other state-of-the-art models, LTG achieves the best results on the public datasets of Microsoft Research Asia, Resume, and WeiboNER, with the F 1 score of 95.89%, 96.81%, and 72.32%, respectively.

Open Access April 7, 2023

Robot indoor navigation point cloud map generation algorithm based on visual sensing

Qin Zhang, Xiushan Liu

Article number: 20220258

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Abstract

At present, low-cost Red Green Blue Depth (RGB-D) sensors are mainly used in indoor robot environment perception, but the depth information obtained by RGB-D cameras has problems such as poor accuracy and high noise, and the generated 3D color point cloud map has low accuracy. In order to solve these problems, this article proposes a vision sensor-based point cloud map generation algorithm for robot indoor navigation. The aim is to obtain a more accurate point cloud map through visual SLAM and Kalman filtering visual-inertial navigation attitude fusion algorithm. The results show that in the positioning speed test data of the fusion algorithm in this study, the average time-consuming of camera tracking is 23.4 ms, which can meet the processing speed requirement of 42 frames per second. The yaw angle error of the fusion algorithm is the smallest, and the ATE test values of the algorithm are smaller than those of the Inertial measurement unit and Simultaneous-Localization-and-Mapping algorithms. This research algorithm can make the mapping process more stable and robust. It can use visual sensors to make more accurate route planning, and this algorithm improves the indoor positioning accuracy of the robot. In addition, the research algorithm can also obtain a dense point cloud map in real time, which provides a more comprehensive idea for the research of robot indoor navigation point cloud map generation.

Open Access April 10, 2023

Towards a better similarity algorithm for host-based intrusion detection system

Lounis Ouarda, Bourenane Malika, Bouderah Brahim

Article number: 20220259

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Abstract

An intrusion detection system plays an essential role in system security by discovering and preventing malicious activities. Over the past few years, several research projects on host-based intrusion detection systems (HIDSs) have been carried out utilizing the Australian Defense Force Academy Linux Dataset (ADFA-LD). These HIDS have also been subjected to various algorithm analyses to enhance their detection capability for high accuracy and low false alarms. However, less attention is paid to the actual implementation of real-time HIDS. Our principal objective in this study is to create a performant real-time HIDS. We propose a new model, “Better Similarity Algorithm for Host-based Intrusion Detection System” (BSA-HIDS), using the same dataset ADFA-LD. The proposed model uses three classifications to represent the attack folder according to certain criteria, the entire system call sequence is used. Furthermore, this work uses textual distance and compares five algorithms like Levenshtein, Jaro–Winkler, Jaccard, Hamming, and Dice coefficient, to classify the system call trace as attack or non-attack based on the notions of interclass decoupling and intra-class coupling. The model can detect zero-day attacks because of the threshold definition. The experimental results show a good detection performance in real-time for Levenshtein/Jaro–Winkler algorithms, 99–94% in detection rate, 2–5% in false alarm rate, and 3,300–720 s in running time, respectively.

Open Access April 26, 2023

A multiorder feature tracking and explanation strategy for explainable deep learning

Lin Zheng, Yixuan Lin

Article number: 20220212

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Abstract

A good AI algorithm can make accurate predictions and provide reasonable explanations for the field in which it is applied. However, the application of deep models makes the black box problem, i.e., the lack of interpretability of a model, more prominent. In particular, when there are multiple features in an application domain and complex interactions between these features, it is difficult for a deep model to intuitively explain its prediction results. Moreover, in practical applications, multiorder feature interactions are ubiquitous. To break the interpretation limitations of deep models, we argue that a multiorder linearly separable deep model can be divided into different orders to explain its prediction results. Inspired by the interpretability advantage of tree models, we design a feature representation mechanism that can consistently represent the features of both trees and deep models. Based on the consistent representation, we propose a multiorder feature-tracking strategy to provide a prediction-oriented multiorder explanation for a linearly separable deep model. In experiments, we have empirically verified the effectiveness of our approach in two binary classification application scenarios: education and marketing. Experimental results show that our model can intuitively represent complex relationships between features through diversified multiorder explanations.

Open Access April 28, 2023

Application study of ant colony algorithm for network data transmission path scheduling optimization

Peng Xiao

Article number: 20220277

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Abstract

With the rapid development of the information age, the traditional data center network management can no longer meet the rapid expansion of network data traffic needs. Therefore, the research uses the biological ant colony foraging behavior to find the optimal path of network traffic scheduling, and introduces pheromone and heuristic functions to improve the convergence and stability of the algorithm. In order to find the light load path more accurately, the strategy redefines the heuristic function according to the number of large streams on the link and the real-time load. At the same time, in order to reduce the delay, the strategy defines the optimal path determination rule according to the path delay and real-time load. The experiments show that under the link load balancing strategy based on ant colony algorithm, the link utilization ratio is 4.6% higher than that of ECMP, while the traffic delay is reduced, and the delay deviation fluctuates within ±2 ms. The proposed network data transmission scheduling strategy can better solve the problems in traffic scheduling, and effectively improve network throughput and traffic transmission quality.

Open Access May 19, 2023

Data analysis with performance and privacy enhanced classification

Rupali Tajanpure, Akkalakshmi Muddana

Article number: 20220215

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Abstract

Privacy is the main concern in cyberspace because, every single click of a user on Internet is recognized and analyzed for different purposes like credit card purchase records, healthcare records, business, personalized shopping store experience to the user, deciding marketing strategy, and the list goes on. Here, the user’s personal information is considered a risk process. Though data mining applications focus on statistically useful patterns and not on the personal data of individuals, there is a threat of unrestricted access to individual records. Also, it is necessary to maintain the secrecy of data while retaining the accuracy of data classification and quality as well. For real-time applications, the data analytics carried out should be time efficient. Here, the proposed Convolution-based Privacy Preserving Algorithm (C-PPA) transforms the input into lower dimensions while preserving privacy which leads to better mining accuracy. The proposed algorithm is evaluated over different privacy-preserving metrics like accuracy, precision, recall, and F 1-measure. Simulations carried out show that the average increment in the accuracy of C-PPA is 14.15 for Convolutional Neural Network (CNN) classifier when compared with results without C-PPA. Overlap-add C-PPA is proposed for parallel processing which is based on overlap-add convolution. It shows an average accuracy increment of 12.49 for CNN. The analytics show that the algorithm benefits regarding privacy preservation, data utility, and performance. Since the algorithm works on lowering the dimensions of data, the communication cost over the Internet is also reduced.

Review Article

Open Access April 10, 2023

Dimensions of artificial intelligence techniques, blockchain, and cyber security in the Internet of medical things: Opportunities, challenges, and future directions

Aya Hamid Ameen, Mazin Abed Mohammed, Ahmed Noori Rashid

Article number: 20220267

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Abstract

The Internet of medical things (IoMT) is a modern technology that is increasingly being used to provide good healthcare services. As IoMT devices are vulnerable to cyberattacks, healthcare centers and patients face privacy and security challenges. A safe IoMT environment has been used by combining blockchain (BC) technology with artificial intelligence (AI). However, the services of the systems are costly and suffer from security and privacy problems. This study aims to summarize previous research in the IoMT and discusses the roles of AI, BC, and cybersecurity in the IoMT, as well as the problems, opportunities, and directions of research in this field based on a comprehensive literature review. This review describes the integration schemes of AI, BC, and cybersecurity technologies, which can support the development of new systems based on a decentralized approach, especially in healthcare applications. This study also identifies the strengths and weaknesses of these technologies, as well as the datasets they use.

Special Issue: Cognitive Cyber-Physical System with Artificial Intelligence for Healthcare 4.0.

Open Access January 30, 2023

HWCD: A hybrid approach for image compression using wavelet, encryption using confusion, and decryption using diffusion scheme

Heggere Rangaswamaiah Latha, Alagarswamy Ramaprasath

Article number: 20229056

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Abstract

Image data play important role in various real-time online and offline applications. Biomedical field has adopted the imaging system to detect, diagnose, and prevent several types of diseases and abnormalities. The biomedical imaging data contain huge information which requires huge storage space. Moreover, currently telemedicine and IoT based remote health monitoring systems are widely developed where data is transmitted from one place to another. Transmission of this type of huge data consumes more bandwidth. Along with this, during this transmission, the attackers can attack the communication channel and obtain the important and secret information. Hence, biomedical image compression and encryption are considered the solution to deal with these issues. Several techniques have been presented but achieving desired performance for combined module is a challenging task. Hence, in this work, a novel combined approach for image compression and encryption is developed. First, image compression scheme using wavelet transform is presented and later a cryptography scheme is presented using confusion and diffusion schemes. The outcome of the proposed approach is compared with various existing techniques. The experimental analysis shows that the proposed approach achieves better performance in terms of autocorrelation, histogram, information entropy, PSNR, MSE, and SSIM.

Special Issue: Human-Centred Artificial Intelligence for Web 4.0

Open Access January 31, 2023

Environmental landscape design and planning system based on computer vision and deep learning

Xiubo Chen

Article number: 20220092

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Abstract

Environmental landscaping is known to build, plan, and manage landscapes that consider the ecology of a site and produce gardens that benefit both people and the rest of the ecosystem. Landscaping and the environment are combined in landscape design planning to provide holistic answers to complex issues. Seeding native species and eradicating alien species are just a few ways humans influence the region’s ecosystem. Landscape architecture is the design of landscapes, urban areas, or gardens and their modification. It comprises the construction of urban and rural landscapes via coordinating the creation and management of open spaces and economics, finding a job, and working within a confined project budget. There was a lot of discussion about global warming and water shortages. There is a lot of hope to be found even in the face of seemingly insurmountable obstacles. AI is becoming more significant in many urban landscape planning and design elements with the advent of web 4.0 and Human-Centred computing. It created a virtual reality-based landscape to create deep neural networks (DNNs) to make deep learning (DL) more user-friendly and efficient. Users may only manipulate physical items in this environment to manually construct neural networks. These setups are automatically converted into a model, and the real-time testing set is reported and aware of the DNN models that users are producing. This research presents a novel strategy for combining DL-DNN with landscape architecture, providing a long-term solution to the problem of environmental pollution. Carbon dioxide levels are constantly checked when green plants are in and around the house. Plants, on either hand, remove toxins from the air, making it easier to maintain a healthy environment. Human-centered Artificial Intelligence-based web 4.0 may be used to assess and evaluate the data model. The study findings can be sent back into the design process for further modification and optimization.

Special Issue: Trustworthy Artificial Intelligence for Big Data-Driven Research Applications based on Internet of Everythings

Open Access February 15, 2023

Deep learning for content-based image retrieval in FHE algorithms

Sura Mahmood Abdullah, Mustafa Musa Jaber

Article number: 20220222

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Abstract

Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.

Open Access February 16, 2023

Improving binary crow search algorithm for feature selection

Zakaria A. Hamed Alnaish, Zakariya Yahya Algamal

Article number: 20220228

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Abstract

The feature selection (FS) process has an essential effect in solving many problems such as prediction, regression, and classification to get the optimal solution. For solving classification problems, selecting the most relevant features of a dataset leads to better classification accuracy with low training time. In this work, a hybrid binary crow search algorithm (BCSA) based quasi-oppositional (QO) method is proposed as an FS method based on wrapper mode to solve a classification problem. The QO method was employed in tuning the value of flight length in the BCSA which is controlling the ability of the crows to find the optimal solution. To evaluate the performance of the proposed method, four benchmark datasets have been used which are human intestinal absorption, HDAC8 inhibitory activity (IC50), P-glycoproteins, and antimicrobial. Accordingly, the experimental results are discussed and compared against other standard algorithms based on the accuracy rate, the average number of selected features, and running time. The results have proven the robustness of the proposed method relied on the high obtained value of accuracy (84.93–95.92%), G -mean (0.853–0.971%), and average selected features (4.36–11.8) with a relatively low computational time. Moreover, to investigate the effectiveness of the proposed method, Friedman test was used which declared that the performance supremacy of the proposed BCSA-QO with four datasets was very evident against BCSA and CSA by selecting the minimum relevant features and producing the highest accuracy classification rate. The obtained results verify the usefulness of the proposed method (BCSA-QO) in the FS with classification in terms of high classification accuracy, a small number of selected features, and low computational time.

Open Access February 16, 2023

Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm

Sarah Ghanim Mahmood Al-kababchee, Zakariya Yahya Algamal, Omar Saber Qasim

Article number: 20220230

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Abstract

Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K -means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to use techniques that properly explore the search spaces. In this research, an enhancement of K -means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support.

Open Access March 31, 2023

A study on predicting crime rates through machine learning and data mining using text

Ruaa Mohammed Saeed, Husam Ali Abdulmohsin

Article number: 20220223

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Abstract

Crime is a threat to any nation’s security administration and jurisdiction. Therefore, crime analysis becomes increasingly important because it assigns the time and place based on the collected spatial and temporal data. However, old techniques, such as paperwork, investigative judges, and statistical analysis, are not efficient enough to predict the accurate time and location where the crime had taken place. But when machine learning and data mining methods were deployed in crime analysis, crime analysis and predication accuracy increased dramatically. In this study, various types of criminal analysis and prediction using several machine learning and data mining techniques, based on the percentage of an accuracy measure of the previous work, are surveyed and introduced, with the aim of producing a concise review of using these algorithms in crime prediction. It is expected that this review study will be helpful for presenting such techniques to crime researchers in addition to supporting future research to develop these techniques for crime analysis by presenting some crime definition, prediction systems challenges and classifications with a comparative study. It was proved though literature, that supervised learning approaches were used in more studies for crime prediction than other approaches, and Logistic Regression is the most powerful method in predicting crime.

Special Issue: Artificial Intelligence based Techniques and Applications for Intelligent IoT Systems

Open Access April 20, 2023

A multi-crop disease identification approach based on residual attention learning

Kirti, Navin Rajpal

Article number: 20220248

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Abstract

In this work, a technique is proposed to identify the diseases that occur in plants. The system is based on a combination of residual network and attention learning. The work focuses on disease identification from the images of four different plant types by analyzing leaf images of the plants. A total of four datasets are used for the work. The system incorporates attention-aware features computed by the Residual Attention Network (Res-ATTEN). The base of the network is ResNet-18 architecture. Integrating attention learning in the residual network helps improve the system's overall accuracy. Various residual attention units are combined to create a single architecture. Unlike the traditional attention network architectures, which focus only on a single type of attention, the system uses a mixed type of attention learning, i.e., a combination of spatial and channel attention. Our technique achieves state-of-the-art performance with the highest accuracy of 99%. The results show that the proposed system has performed well for both purposes and notably outperformed the traditional systems.

Open Access April 25, 2023

A review of small object and movement detection based loss function and optimized technique

Ravi Prakash Chaturvedi, Udayan Ghose

Article number: 20220324

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Abstract

The objective of this study is to supply an overview of research work based on video-based networks and tiny object identification. The identification of tiny items and video objects, as well as research on current technologies, are discussed first. The detection, loss function, and optimization techniques are classified and described in the form of a comparison table. These comparison tables are designed to help you identify differences in research utility, accuracy, and calculations. Finally, it highlights some future trends in video and small object detection (people, cars, animals, etc.), loss functions, and optimization techniques for solving new problems.

Open Access April 26, 2023

Automatic adaptive weighted fusion of features-based approach for plant disease identification

Kirti, Navin Rajpal, Virendra P. Vishwakarma

Article number: 20220247

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Abstract

With the rapid expansion in plant disease detection, there has been a progressive increase in the demand for more accurate systems. In this work, we propose a new method combining color information, edge information, and textural information to identify diseases in 14 different plants. A novel 3-branch architecture is proposed containing the color information branch, an edge information branch, and a textural information branch extracting the textural information with the help of the central difference convolution network (CDCN). ResNet-18 was chosen as the base architecture of the deep neural network (DNN). Unlike the traditional DNNs, the weights adjust automatically during the training phase and provide the best of all the ratios. The experiments were performed to determine individual and combinational features’ contribution to the classification process. Experimental results of the PlantVillage database with 38 classes show that the proposed method has higher accuracy, i.e., 99.23%, than the existing feature fusion methods for plant disease identification.

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Cite Score	3.3
Journal Citation Indicator	0.38
SCImago Journal Rank	0.398
Source Normalized Impact per Paper	0.732

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Editorial

Editor-in-Chief
Prof. Hasan Fleyeh
Department of Computer Engineering
School of Technology and Business Studies
Dalarna University
Rödavägen 3, 78188 Borlänge
Sweden
JISYS_Editorial@degruyter.com

Editorial Board
Animesh Adhikari, S P Chowgule College, Goa, India
Rageed Al-Hashemy, Mustansiriyah University, Baghdad, Iraq
Mohd Dilshad Ansari, CMR College of Engineering & Technology, India
Manjunath Aradhya, Dayananda Sagar College of Engineering, Bangalore, India
Valentina Emilia Balas, University of Arad, Romania
Daniel Berrar, Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Japan
Çağatay Çatal, Qatar University, Department of Computer Science and Engineering, Qatar
Sigrid Ewert, School of Computer Science, University of the Witwatersrand, Wits, South Africa
Anna Formica, Istituto di Analisi dei Sistemi ed Informatica - Antonio Ruberti (IASI), National Research Council in Rome, Italy
Begoña García Zapirain, University of Deusto, Spain
Eyke Hullermeier, Philipps-Universität Marburg, Germany
M.A. Jayaram, Siddaganga Institute of Technology, Tumkur, India
Liangxiao Jiang, China University of Geosciences, China
Seifedine Kadry, Noroff University College, Norway
Cengiz Kahraman, Istanbul Technical University, Turkey
Asad Khan, Faculty of Information Technology, Monash University, Australia
Eunjin Kim, John D. Odegard School of Aerospace Sciences, University of North Dakota, Grand Forks, USA
Frank Klawonn, Ostfalia University of Applied Sciences, Braunschweig/Wolfenbuettel, Germany
Philippe Le Parc, Université de Bretagne Occidentale, Brest, France
Pawan Lingras, Saint Mary's University, Halifax, Nova Scotia, Canada
Vijay Kumar Mago, Lakehead University, Canada
Farid Meziane, University of Derby, College of Science and Engineering, UK
Mariofanna Milanova, Department of Computer Science, University of Arkansas, Little Rock, USA
Mazin Abed Mohammed, College of Computer Science and Information Technology, University of Anbar, Anbar, Iraq
Serena Nicolazzo, University of Milan, Italy
Elpiniki Papageorgiou, Energy Systems Dept., University of Thessaly, Greece
Shaolin Qu, Bloomberg L.P., New York, NY, USA
Maytham Hassan Safar, Computer Engineering Department, Kuwait University, Kuwait
Ashutosh Sharma, Institute of Computer Technology and Information Security, Southern Federal University, Russia
Amit Kumar Singh, National Institute of Technology Patna, India
Praveen Ranjan Srivastava, Birla Institute of Technology and Science, Pilani, Rajasthan, India
Jeffery T. Walker, University of Arkansas, Little Rock, USA
JingTao Yao, University of Regina, Canada

(Deutsch)

Type: Journal
Language: English
Publisher: De Gruyter
First published: September 1, 1991
Publication Frequency: 1 Issue per Year
Audience: researchers in the field of intelligent systems