-
New Evolutionary Algorithms for Determining Consensus of Ordered Partition Collectives Cybern. Syst. (IF 1.7) Pub Date : 2024-01-05 Dai Tho Dang, Ngoc Thanh Nguyen
The ordered partition structure is helpful when an expert has to classify elements of a set into given classes. Finding consensus for an ordered partition collective is very important in making dec...
-
Depth-Bounded Fuzzy Bisimulation for Fuzzy Modal Logic Cybern. Syst. (IF 1.7) Pub Date : 2024-01-03 Linh Anh Nguyen, Ivana Micić, Ngoc-Thanh Nguyen, Stefan Stanimirović
We introduce depth-bounded fuzzy bisimulation between fuzzy Kripke models. Roughly speaking, a depth-bounded fuzzy bisimulation is a decreasing sequence of fuzzy binary relations whose infimum is a...
-
A Nature-Inspired Method to Mine Top-k Multi-Level High-Utility Itemsets Cybern. Syst. (IF 1.7) Pub Date : 2023-12-28 N. T. Tung, Trinh D. D Nguyen, Loan T. T. Nguyen, Quan Thanh Tho, An Mai
High-Utility Itemset Mining (HUIM) is designed to discover sets of itemsets that can bring high profits from the database. However, HUIM encounters several challenges in picking a suitable minimum ...
-
FeDN2: Fuzzy-Enhanced Deep Neural Networks for Improvement of Sentence-Level Sentiment Analysis Cybern. Syst. (IF 1.7) Pub Date : 2023-12-28 Huyen Trang Phan, Dinh Tai Pham, Ngoc Thanh Nguyen
Sentence-level sentiment analysis is a natural language processing model growing rapidly and strongly due to its role in artificial intelligence systems. There are many approaches to developing and...
-
Improved Skin Disease Classification with Mask R-CNN and Augmented Dataset Cybern. Syst. (IF 1.7) Pub Date : 2023-12-23 Kushal Pokhrel, Cesar Sanin, Md. Kowsar Hossain Sakib, Md Rafiqul Islam, Edward Szczerbicki
Skin diseases are a significant global health concern, impacting millions worldwide. Severe diseases like psoriasis and dermatitis can coexist with more benign skin issues like acne and eczema. Pri...
-
How to Explain Sentiment Polarity – A Survey of Explainable Sentiment Analysis Approaches Cybern. Syst. (IF 1.7) Pub Date : 2023-12-25 Bernadetta Maleszka
Sentiment analysis area has become more and more popular due to information and opinion overload (especially in social networks). With growth of efficient and high accuracy methods of artificial in...
-
Performance Evaluation and Comparative Analysis of Machine Learning Models on the UNSW-NB15 Dataset: A Contemporary Approach to Cyber Threat Detection Cybern. Syst. (IF 1.7) Pub Date : 2023-12-25 Afrah Fathima, Amir Khan, Md Faizan Uddin, Mohammad Maqbool Waris, Sultan Ahmad, Cesar Sanin, Edward Szczerbicki
This research work utilizes the University of New South Wales Network Based 2015 (UNSW-NB15) dataset to investigate the dynamic nature of cyber threats, departing from the obsolete Knowledge Discov...
-
Applied Machine Learning, Data Science, and Generative AI with Exploratory and Descriptive Case Studies in Varied Domains Cybern. Syst. (IF 1.7) Pub Date : 2023-12-23 Edward Szczerbicki, Ngoc Thanh Nguyen
Published in Cybernetics and Systems: An International Journal (Ahead of Print, 2023)
-
Adaptive2Former: Enhancing Chromosome Instance Segmentation with Adaptive Query Decoder Cybern. Syst. (IF 1.7) Pub Date : 2023-12-21 Linfeng Yu, Xinxu Zhang, Zhenpeng Zhong, Yi Lai, Haoxi Zhang, Edward Szczerbicki
Chromosome instance segmentation plays a crucial role in chromosomal karyotype analysis. However, the overlapping of chromosome instances and their individual morphological differences make accurat...
-
The Impact of Generative AI and ChatGPT on Creating Digital Advertising Campaigns Cybern. Syst. (IF 1.7) Pub Date : 2023-12-21 Edyta Gołąb-Andrzejak
The use of AI-based solutions is currently discussed in relation to various industries. The proliferation of tools based on generative artificial intelligence (GAI), including the emergence of Chat...
-
Learning Disentangled Representation for Chromosome Straightening Cybern. Syst. (IF 1.7) Pub Date : 2023-12-21 Tao Liu, Yifeng Peng, Ran Chen, Yi Lai, Haoxi Zhang, Edward Szczerbicki
Chromosome straightening plays an important role in karyotype analysis. Common straightening methods usually adopt geometric algorithms, which tend to affect the chromosome banding patterns in the ...
-
Improving Landslides Prediction: Meteorological Data Preprocessing Based on Supervised and Unsupervised Learning Cybern. Syst. (IF 1.7) Pub Date : 2023-11-07 Byron Guerrero-Rodriguez, Jaime Salvador-Meneses, Jose Garcia-Rodriguez, Christian Mejia-Escobar
The hazard of landslides has been demonstrated over time with numerous events causing damage to human lives and high material costs. Several previous studies have shown that one of the predominant ...
-
Analysis of Machine Learning Based Imputation of Missing Data Cybern. Syst. (IF 1.7) Pub Date : 2023-09-09 Syed Tahir Hussain Rizvi, Muhammad Yasir Latif, Muhammad Saad Amin, Achraf Jabeur Telmoudi, Nasir Ali Shah
Abstract Data analysis and classification can be affected by the availability of missing data in datasets. To deal with missing data, either deletion- or imputation-based methods are used that result in the reduction of data records or imputation of incorrect predicted value. Quality of imputed data can be significantly improved if missing values are generated accurately using machine learning algorithms
-
Model-Checking of Concurrent Real-Time Software Using High-Level Colored Time Petri Nets with Stopwatches Cybern. Syst. (IF 1.7) Pub Date : 2023-09-06 Imane Haur, Jean-Luc Béchennec, Olivier H. Roux
Abstract The control of real-time systems often requires taking into account simultaneous access in true parallelism to shared resources. This is particularly the case for multicore execution platforms. Timed automata or time Petri nets do not capture these features directly. We first define High-level Colored Time Petri Net (HCTPN) that extends time Petri Nets with color and high-level functionality
-
Models, Algorithms and Approximation Results for a Bi-Level Synchronized Knapsack Problem Cybern. Syst. (IF 1.7) Pub Date : 2023-09-05 Fatiha Bendali, Jean Mailfert, Eloise Mole Kamga, Alain Quilliot, Hélène Toussaint
Abstract We describe here a bi-level Knapsack problem which involves an Eater/Feeder interaction between 2 Knapsack systems and expresses the collaboration between a local resource provider and a consumer. We first describe the SFEK: Synchronized Feeder/Eater Knapsack problem in an accurate way, and set an ILP model, enhanced with additional valid cuts. Next we focus on the complexity issue. We design
-
A Reconfiguration Method for Muti-Robot Monitoring Patrols Cybern. Syst. (IF 1.7) Pub Date : 2023-09-07 Sara Hsaini, Rabah Ammour, Leonardo Brenner, My El Hassan Charaf, Isabel Demongodin, Dimitri Lefebvre
Abstract This paper addresses the problem of multi-robot task allocation and trajectory planning in industrial environments. The objective is to optimize the overall cost of robot surveillance patrols in a dynamic high-risk environment. In this context, a hybrid beam search based approach is proposed to plan the patrol trajectories iteratively to accommodate environmental changes under some functional
-
Cardiovascular Anomaly Detection with Heterogeneous Wave Segment Harmonization for Lightweight Systems Cybern. Syst. (IF 1.7) Pub Date : 2023-08-31 Kürşat Çakal, Mehmet Önder Efe
Abstract The proposed study brings a novel classification and detection ability for stochastic environmental conditions by its WSS (Wave-Segment Synthesizing) functionality by tightly adapting a Lightweight CNN with an increase of about 40% accuracy over the literature. This study synthesizes heterogeneously coherent ECG signals and adapts them to the input system of wearable devices. It consists of
-
Backstepping-Based Artificial Intelligence Algorithm for Photovoltaic-Quadratic Boost Converter System Cybern. Syst. (IF 1.7) Pub Date : 2023-08-26 Belgacem Mbarki, Jaouher Chrouta, Fethi Farhani, Abderrahmen Zaafouri
Abstract With energy demands rising, fossil fuel resources depleting, and global warming caused by carbon emissions, there’s an urgent need for efficient, renewable, and clean energy. Solar energy, especially photovoltaic systems, is gaining popularity due to its simple structure, low energy production costs, and eco-friendliness. However, these systems suffer from insufficient efficiency due to environmental
-
Optimized Sliding Mode Control Based on Cuckoo Search Algorithm: Application for 2DF Robot Manipulator Cybern. Syst. (IF 1.7) Pub Date : 2023-08-24 Hatem Tlijani, Ameni Jouila, Khaled Nouri
Abstract Robot manipulators are used in high-accuracy processes they provide flexibility, adaptability, and a large workspace. Hence, in the presence of uncertainties, disturbance, and high-performance control approaches are necessary to improve their accuracy to achieve high-tracking precision requirements and transient response under load variation. We present in this article, a novel concept of
-
On Using Metaheuristics for the Allocation of Electric Vehicles to Charging Stations Cybern. Syst. (IF 1.7) Pub Date : 2023-08-24 Chaima Taieb, Takwa Tlili, Issam Nouaouri, Saoussen Krichen, Hamid Allaoui
Abstract In this article, we present a comprehensive study regarding the problem of allocating a fleet of electric vehicles to charging stations according to charging time and battery constraints. Each charging station’s capacity as well as the necessary charging time is known in advance while each vehicle’s arrival time is provided by a GPS device. We provide an integer programming model solved with
-
Real-Time and Intelligent Methods for Cybernetics and Systems Modeling Cybern. Syst. (IF 1.7) Pub Date : 2023-08-22 Achraf Jabeur Telmoudi, Enrique Herrera-Viedma, Syed Tahir Hussain Rizvi
Published in Cybernetics and Systems: An International Journal (Ahead of Print, 2023)
-
On the Use of Gradient-Based Solver and Deep Learning Approach in Hierarchical Control: Application to Grand Refrigerators Cybern. Syst. (IF 1.7) Pub Date : 2023-08-21 Xuan-Huy Pham, François Bonne, Mazen Alamir
Abstract This paper extends the work that has been recently studied on hierarchical control proposed by Alamir et al. (2017). This framework is designed to control interconnecting subsystems such as cryogenic processes or power plants. Based on the previous study, Pham et al. (2022) have shown that handling constraints and non-linearities could dispute the real-time feasibility of the approach. In
-
Texture Characterization Fuzzy Logic-Based Model for Melanoma Diagnosis Cybern. Syst. (IF 1.7) Pub Date : 2023-08-21 Jinen Daghrir, Lotfi Tlig, Moez Bouchouicha, Noureddine Litaiem, Faten Zeglaoui, Mounir Sayadi
Abstract Over the past two decades, the world has known a significant number of deaths from cancer. More specifically, melanoma which is considered the deadliest form of skin cancer causes a remarkable percentage of all cancer deaths. Therefore, the health and disease management community has exceedingly invested in creating efficient devices that help dermatologists to early evaluate and inspect a
-
Design Optimization and Analysis of on-Grid Hybrid Renewable Energy System for Audio-Visual Chain Cybern. Syst. (IF 1.7) Pub Date : 2023-08-18 Saidi Mohamed, Habib Cherif, Othman Hasnaoui, Jamel Belhadj
Abstract Rapid advancement of various renewable energies (especially wind and solar energies) as well as energy storage technologies dramatically changes the current energy configuration including the television buildings. In this paper, a micro-grid with high penetration of clean energy systems has been designed for an audio-visual chain. The design and performance optimization of on-grid hybrid renewable
-
An Optimized and Stable CF2 Mobile Robot Motion Planning Approach and Adaptation for Moving Targets Cybern. Syst. (IF 1.7) Pub Date : 2023-08-17 Safa Ziadi, Mohamed Njah
Abstract In this paper, the mobile robot motion planning approach (PSO-CF2-mt: PSO-CF2 for moving targets) is improved to track a moving target following a smooth path. PSO-CF2-mt was tested previously in various static and dynamic environments and proved its capacity to track moving targets whatever the form of the target’s trajectory. The problem with PSO-CF2-mt that we want to face in this paper
-
Soft-Computing Techniques to Address Industrial and Environmental Challenges Cybern. Syst. (IF 1.7) Pub Date : 2023-08-10 Álvaro Herrero, Daniel Urda, Esteban Jove, Mariusz Topolski, Emilio Corchado
Published in Cybernetics and Systems: An International Journal (Ahead of Print, 2023)
-
Using Congestion to Improve Short-Term Velocity Forecasting with Machine Learning Models Cybern. Syst. (IF 1.7) Pub Date : 2023-08-09 Cristián Lira, Aldo Araya, Bastián Véjar, Fernando Ordóñez, Sebastián Ríos
Abstract The ability to estimate future velocity on a road network is relevant for applications such as vehicle navigation systems and emergency vehicle dispatching. The existence of traffic congestion severely impacts travellers’ travel time. In this paper, we investigate the use of congestion prediction in velocity forecasting models. Using a data-driven approach, we classify traffic observations
-
Multivariate Adaptive Downsampling Algorithm for Industry 4.0 Visual Analytics Cybern. Syst. (IF 1.7) Pub Date : 2023-08-08 Javier Franco, Ander Garcia, Amaia Gil, Juan Luis Ferrando, Xabier Badiola, Mikel Saez de Buruaga
Abstract Many industrial companies capture high volume of time series data from their industrial processes. However, to visualize it, regular visualization approaches require specialized hardware. Thus, downsampling algorithms are required to create a simplified view of the original data. Although industrial processes involve synchronized variables that should be visualized together for their analysis
-
A Clustering Extension of HUEPs for the Analysis of Performance Anomalies in Robots Cybern. Syst. (IF 1.7) Pub Date : 2023-08-04 Nuño Basurto, Carlos Cambra, Álvaro Herrero, Daniel Urda
Abstract Errors in Cyber-Physical Systems present a major problem given the current state of technological complexity. Self-diagnosis can contribute to address it, being the standpoint of the present paper. Hence, an application of exploratory Machine Learning models to assess the functioning of robot software in order to identify anomalies that lead to low performance is proposed. More precisely,
-
Multi-Instance Attention Network for Anomaly Detection from Multivariate Time Series Cybern. Syst. (IF 1.7) Pub Date : 2023-08-03 Gye-Bong Jang, Sung-Bae Cho
Abstract Anomaly detection and state prediction research using multivariate data is being actively conducted in various industrial fields. However, since most dynamically operating industrial machines perform different operating conditions, they contain different types of abnormal conditions, making it difficult to detect anomalies and predict the remaining life. This white paper proposes a condition
-
Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment Cybern. Syst. (IF 1.7) Pub Date : 2023-07-27 Miroslav Dvorak, Petr Dolezel, Dominik Stursa, Mohamed Chouai
Abstract Unmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle.
-
Attack Detection in Wireless Sensor Network: A Big Data Perspective Cybern. Syst. (IF 1.7) Pub Date : 2023-05-17 Kulkarni A. V., Mithra V., Radhika Menon
Abstract For securing the network, intrusion detection systems are frequently used in wireless sensor networks to fight against insider attacks by adopting the appropriate trust-based methods. Still, the sensors could create an enormous amount of data that reduces the efficacy of trust computation in the big data era. This paper aims to introduce a new attack detection system under the big data perspective
-
An Efficient Cloud Storage Model for GOP-Level Video Deduplication using Adaptive GOP Structure Cybern. Syst. (IF 1.7) Pub Date : 2023-04-09 G. Sujatha, A. Devipriya, D. Brindha, G. Premalatha
Abstract Cloud storage systems may be used by users and businesses to transfer their enormous volumes of data for storage, processing, and analysis. Cloud storage may be used efficiently by employing the deduplication technique to prevent duplicate copies. Users may save any type of material, including audio, video, images, text, and more. The handling of deduplication techniques for these distinct
-
Efficient Energy Management Strategy for an Electric Vehicle Powered by a Hybrid Energy Storage System Based on Hybrid GBDT-RSA Approach Cybern. Syst. (IF 1.7) Pub Date : 2023-03-31 K. V. Kandaswamy, A. Jagadeeshwaran, R. Anand
Abstract This paper proposes an efficient energy management scheme for an EV with a hybrid energy storage system like super capacitor and battery based on hybrid optimization method. The proposed hybrid approach is a parallel performance of gradient boosting decision tree algorithm and reptile search algorithm. The major purpose is to diminish the variance among real and reference power on battery
-
An Efficient DDoS Attack Detection Using Chaos Henry Gas Solubility Optimization Weight Initialization Based Rectified Linear Unit Cybern. Syst. (IF 1.7) Pub Date : 2023-03-17 Selvam Lakshmanan, Uma Maheswari Gnaniyan Ponnusamy, Senthilkumar Andi
Abstract The Denial of Service (DoS) attacks is one of the main issues faced by cloud service providers due to their intricate nature. The main aim of this attack is to disrupt the services of authorized users by forwarding massive malicious requests to the victim system. Even though the modern Artificial Intelligence-powered intrusion detection system offers improved benefits, it suffers from analyzing
-
Design of UPQC with Solar PV and Battery Storage Systems for Power Quality Improvement Cybern. Syst. (IF 1.7) Pub Date : 2023-03-15 Koganti Srilakshmi, K. Krishna Jyothi, G. Kalyani, Y. Sai Prakash Goud
Abstract In this paper, the unified PQ conditioner (UPQC) is associated with photovoltaic (PV) and battery-storage systems (BSSs) to address the PQ issues. The hybrid fuzzy-sliding mode control (HFSMC) based maximum-power-point tracking system (MPPTs) is adopted for solar PV system to extract maximum output. To minimize the complexity of the conventional system, levenberg-marquardt trained artificial
-
Recognition of Kannada Character Scripts Using Hybrid Feature Extraction and Ensemble Learning Approaches Cybern. Syst. (IF 1.7) Pub Date : 2023-03-09 Supreetha Patel Tiptur Parashivamurthy, Sannangi Viswaradhya Rajashekaradhya
Abstract An automated handwritten script identification system seeks more attention in the academic research field and commercial applications. Recognizing the handwritten Kannada scripts in recent years is an active research area. But, it is a much more challenging one in the pattern recognition field owing to the complexity of structural hierarchy, huge vocabulary count, and distinct people’s diverse
-
Leukemia Detection Using Invariant Structural Cascade Segmentation Based on Deep Vectorized Scaling Neural Network Cybern. Syst. (IF 1.7) Pub Date : 2023-03-01 A. Arthi, V. Vennila, U. Arun Kumar
Abstract Leukemia is one of the deadliest diseases that occur in white blood cells which is identified in microscopic images from blood samples. Researchers have developed various techniques to diagnose leukemia using a machine learning approach by analyzing micro imaging blood samples. During image screening, the Damaged cells be identified based on manually counting on similar structure lymphocytes
-
MapReduce Framework Based Sequential Association Rule Mining with Deep Learning Enabled Classification in Retail Scenario Cybern. Syst. (IF 1.7) Pub Date : 2023-02-27 Khaled M. Matrouk, Jagannath E. Nalavade, Saeed Alhasen, Meena Chavan, Neha Verma
Abstract Association Rule Mining is used for data mining using the associate rules for detecting frequent items. Hence, this research introduces a novel sequential rule mining for predicting user category using the proposed hybrid Tasmanian Water Devil Optimization based map reduce framework. The introduced Tasmanian Water Devil Optimization integrates the feeding behavior of the Tasmanian devil in
-
Dingo Optimizer Plus Black Widow Optimization-Based Optimal Design of Multiband U-Slot Microstrip Patch Antenna Cybern. Syst. (IF 1.7) Pub Date : 2023-02-26 Ajitha S. S.
Abstract Currently, for high-performance appliances the easiness of installation, weight, size, and cost are major constraints. Spacecraft, aircraft, missile, and satellite appliances require lower profile antennas, and U-slot MPA can be well fitted for such appliances because of its wide-band nature. MPA integrating U-shape slots are renowned for including comparatively larger bandwidth impedance
-
Fractional-Sea Lion Optimization Based Routing and Charge Scheduling in Internet of Electric Vehicles Cybern. Syst. (IF 1.7) Pub Date : 2023-02-24 Suresh Pandian, Agalya Vedi, Belsam Jeba Ananth Manasea Selvin, Ramya Ganesan
Abstract The increasing impact of emissions from fuel vehicles accounted to mitigate the emissions worldwide. This study develops a multi-objective model for charge scheduling in the Internet of Electric Vehicles (IoEV). The objective is to create a method for charging EVs in the IoEV network that is energy conscious. Here, the position of the charge station and the location of the EV are used to simulate
-
FPGA Based Integrated Control of Brushless DC Motor for Renewable Energy Storage System Cybern. Syst. (IF 1.7) Pub Date : 2023-02-23 Karthikeyan S., Lakshmi K.
Abstract To reduce air pollution and global warming, renewable energy technologies may generate power. Wind, solar PV, and fuel cell energy are the primary sources. Solar PV system-powered brushless direct current motor (BLDC) drives are used in the automobile industry due to their importance. In this study, Sheppard–Taylor (S-T) converter and Pulse Width Modulated (PWM) Inverter-fed BLDC provide steady
-
BRDO: Blockchain Assisted Intrusion Detection Using Optimized Deep Stacked Network Cybern. Syst. (IF 1.7) Pub Date : 2023-02-21 Kumaran N, Shyam Mohan J S
Abstract The blockchain model exposed its adaptability in various areas, including inter-banking, supply chain management, international payment, etc. The anomaly intrusion in blockchain mostly threatens the privacy and security of information, thus secure intrusion detection technique is highly essential. Presently, blockchain is incorporated into intrusion detection model for improving the overall
-
On the Discrete-Time Minimum Principle in Multiple-Mode Systems Cybern. Syst. (IF 1.7) Pub Date : 2023-02-22 José Daniel López-Barrientos, Manuel Jiménez-Lizárraga, Beatris Adriana Escobedo-Trujillo
Abstract This paper presents a robust version of the discrete-time minimum principle for uncertain systems. The uncertainty enters the dynamics of the system by an unknown value that belongs to a finite set. Each element of such a set represents a possible dynamic realization of the discrete-time trajectory. Following a mathematical programming approach, we reformulate the problem to obtain general
-
Combination of Deep Learning Models for Student’s Performance Prediction with a Development of Entropy Weighted Rough Set Feature Mining Cybern. Syst. (IF 1.7) Pub Date : 2023-02-22 Sateesh Nayani, Srinivasa Rao P, Rajya Lakshmi D
Abstract Nowadays, the prediction of student performance is still complicated to analyze the talent of individuals and the effort to improve their academic performance. Moreover, the researchers are performed to analyze the outcomes of student performance but the educational database consists of a huge data volume, which is hard to train the small sample. In this research work, a new hybrid deep learning
-
Resource Allocation in Macrocell–Femtocells via Combined Rain and Whale Optimization Algorithm Cybern. Syst. (IF 1.7) Pub Date : 2023-02-21 Shailaja Sanjay Mohite, Uttam D. Kolekar
Abstract Femtocell (FC) dense exploitation imposes a novel challenge for the resource allotment in a 2-tier network, like: (1) dynamic resources allotment among the 2 tiers by considering the off-load traffic from macrocell (MC), (2) permitting access to users when ensuring QoS for FC subscribers, and (3) suitable power setting, which discovers cooperation among the overall bandwidth allotted to FCs
-
OptiMD-3D DCNN: A Framework for Restoring the Haze-Free Images by Image Dehazing Techniques Using Heuristic Approach of Adaptive Lifting Wavelet Transform Cybern. Syst. (IF 1.7) Pub Date : 2023-02-21 Rachamadugu Prakash Kumar, Manjanaik Naganaik
Abstract The recently implemented illustrative techniques are focused to restore haze-free images by using learning approaches and also physical models. Managing finer details of the images when completely removing the haze is a complicated task especially in performing the hazing on single images. Generally, hazy images comprise distorted colors, poor contrast, and lower visibility, which lead to
-
MDHO: Mayfly Deer Hunting Optimization Algorithm for Optimal Obstacle Avoidance Based Path Planning Using Mobile Robots Cybern. Syst. (IF 1.7) Pub Date : 2023-02-20 Sakthitharan Subramanian, Sudha Rajesh, Preethika Immaculate Britto, Sakthivel Sankaran
Abstract Mobile robot becomes more significant in human life and industry, whereas navigation of robot in the dynamic environment results a challenging problem and it need to be solved in an efficient way. Path planning gained more attention in recent decades and puts its practical usage in different industries. Path planning for the mobile robot is to determine feasible path to reach target location
-
Enhance Security and Privacy in VANET Based Sensor Monitoring and Emergency Services Cybern. Syst. (IF 1.7) Pub Date : 2023-02-15 R. M. Rajeswari, S. Rajesh
Abstract VANET performs analysis of patient records and sends security warnings to the carer through VANET so that they may provide urgent assistance while keeping patient data safe. Threat actors might potentially access data packets that go between biological sensors and smart ambulances in the vehicular network. When healthcare professionals use a VANET, they run the risk of receiving inaccurate
-
A Detection of Intrusions Based on Deep Learning Cybern. Syst. (IF 1.7) Pub Date : 2023-02-15 D. Kamalakkannan, D. Menaga, S. Shobana, K. V. Daya Sagar, R. Rajagopal, Mohit Tiwari
Abstract The use of network intrusion detection systems is expanding as cloud computing becomes more widespread. Network intrusion detection systems (NIDS) are crucial to network security since network traffic is increasing and cyberattacks are being launched more frequently. Algorithms for detecting anomalies in intruder detection use either machine learning systems or pattern matching systems. Pattern-matching
-
A Comprehensive Literature of Genetics Cryptographic Algorithms for Data Security in Cloud Computing Cybern. Syst. (IF 1.7) Pub Date : 2023-02-15 Ozgu Can, Fursan Thabit, Asia Othman Aljahdali, Sharaf Al-Homdy, Hoda A. Alkhzaimi
Abstract Cloud computing has revolutionized the world, opening up new horizons with bright potential due to its performance, accessibility, low cost, and many other benefits. Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computational power. Cloud computing refers to data centers accessible to numerous customers over the Internet
-
Cloud Based Electric Vehicle’s Temperature Monitoring System Using IOT Cybern. Syst. (IF 1.7) Pub Date : 2023-02-15 S. V. N. Sreenivasu, T. Sathesh Kumar, Omer Bin Hussain, Ajay Reddy Yeruva, Subash Ranjan Kabat, Abhay Chaturvedi
Abstract The use of electric mobility must be part of transportation in the future. The detection, assessment, and scenario of defects in electric drives improve the trustworthiness of electric cars (EV). Permanent magnet synchronous motor (PMSM) drives are worn in a multiplicity of usage appropriate to their enhanced tactical suppleness, superior control thickness, and higher efficiency. In this learning
-
Enhanced Multimodal Fake News Detection with Optimal Feature Fusion and Modified Bi-LSTM Architecture Cybern. Syst. (IF 1.7) Pub Date : 2023-02-14 Vikash Kishore, Mukesh Kumar
Abstract Numerous enhancements have been made to the mobile internet, which leads to an increase the people’s attention to posting more multi-modal posts among the social media platforms. Hence, this paper aims to design a multimodal fake news detection model with enhanced deep learning architecture. Initially, the multi-modal information including images and text are gathered from real-time social
-
Remora Jaya Optimization-Enabled Deep Quantum Neural Network for Underwater Target Tracking Using Radar Images Cybern. Syst. (IF 1.7) Pub Date : 2023-02-14 D. Thiruselvan, J. P. Ananth
Abstract The Ocean boundaries can be protected by pursuing an underwater uncooperative target and permits for the exploitation of ocean resources. This research design a novel technique, named Remora Jaya Optimization (RJO)-enabled Deep Quantum Neural Network (DQN) by considering the impacts of an unknown underwater environment for effective underwater target tracking in radar images. Here, the image
-
Adaptive Fuzzy-Based SMC for Controlling Torque Ripples in Brushless DC Motor Drive Applications Cybern. Syst. (IF 1.7) Pub Date : 2023-02-14 R. Senthilkumar, R. Balamurugan
Abstract The use of Brushless-DC (BLDC) motor drives in many industrial settings has grown in recent years. In an ideal situation, the torque generated via BLDC motors with a trapezoidal back electromotive force (BEMF) remains unchanged. But, in actuality, the generated torque is distorted by torque ripples (TRs). These TRs make it essential that variable-speed drives operate smoothly. Power electronic
-
An Efficient Document Clustering Approach for Devising Semantic Clusters Cybern. Syst. (IF 1.7) Pub Date : 2023-02-11 E. K. Jasila, N. Saleena, K. A. Abdul Nazeer
Abstract The rise of superfluous information day by day has made the clustering of information into meaningful sets challenging. We propose an efficient approach for obtaining semantic clusters from a huge volume of documents. The preprocessing based on the lexical ontological information from WordNet helps in reducing the feature space and eliminating synonymy problems among the features. A considerable
-
Assured and Provable Data Expuncturing in cloud using Ciphertext Policy–Attribute Based Encryption (CP-ABE) Cybern. Syst. (IF 1.7) Pub Date : 2023-02-11 Abinaya P, Senthil Kumar J
Abstract Modern cloud computing strategies greatly reduce investment in infrastructure and data maintenance costs across startup companies and large organizations. This is mainly because of the emergence of the cloud-based IoT paradigm, which allows IoT-based devices to directly upload the data acquired from the environments to the remote cloud and allows the owner of those resources to manage their
-
HBRO-AlexNet: Honey Badger Remora Optimization Integrated AlexNet for Cooperative Spectrum Sensing in Cognitive Radio Network Cybern. Syst. (IF 1.7) Pub Date : 2023-02-11 Neelam Dewangan, Arun Kumar, R.N. Patel
Abstract Cognitive radio (CR) technology enables a secondary user (SU) to make the most use of the licensed spectrum when the primary user (PU) is inactive, hence increasing spectrum efficiency. SUs are required to complete the spectrum sensing process to identify the spectrum utilization. It is required to effectively detect PU signal to SU for using the idle licensed spectrum bands. Even though various
-
Semantic Segmentation for Brain Injury Using Multi-Class Deep Level Convolution Networks Cybern. Syst. (IF 1.7) Pub Date : 2023-02-10 S. Roselin Mary, Manmohan Singh, N. Aparna, D. Rosy Salomi Victoria
Abstract In the realm of analysis of medical image, semantic segmentation is a crucial yet difficult problem. Automatic labeling of various anatomical structures can aid in diagnosis of illness, planning of treatment and evaluation of development. However, because of the wide variation in form and appearance across participants, segmentation is challenging for mechanized labeling and for manual labeling
-
A Secure IoT Based Wireless Sensor Network Data Aggregation and Dissemination System Cybern. Syst. (IF 1.7) Pub Date : 2023-02-10 Charanjeet Singh, Syed Asif Basha, A. Vinay Bhushan, Mithra Venkatesan, Abhay Chaturvedi, Anurag Shrivastava
Abstract Cloud computing can greatly benefit web applications that have specific computation and storage requirements. With a scalable and adaptable design, the cloud is coupled with wireless sensor networks. Numerous application domains can be integrated directly with REST-based web services. An IP-based WSN testbed was used to develop a proof-of-concept REST API for remote data access. A user receives