International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1187
Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods
Статья научная
In the real world, most of the applications are inherently dynamic in nature i.e. their underlying data distribution changes with time. As a result, the concept drifts occur very frequently in the data stream. Concept drifts in data stream increase the challenges in learning as well, it also significantly decreases the accuracy of the classifier. However, recently many algorithms have been proposed that exclusively designed for data stream mining while considering drifting concept in the data stream.This paper presents an empirical evaluation of these algorithms on datasets having four possible types of concept drifts namely; sudden, gradual, incremental, and recurring drifts.
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Empirical Support for Concept Drifting Approaches: Results Based on New Performance Metrics
Статья научная
Various types of online learning algorithms have been developed so far to handle concept drift in data streams. We perform more detailed evaluation of these algorithms through new performance metrics - prequential accuracy, kappa statistic, CPU evaluation time, model cost, and memory usage. Experimental evaluation using various artificial and real-world datasets prove that the various concept drifting algorithms provide highly accurate results in classifying new data instances even in a resource constrained environment, irrespective of size of dataset, type of drift or presence of noise in the dataset. We also present empirically the impact of various features- size of ensemble, period value, threshold value, multiplicative factor and the presence of noise on all the key performance metrics.
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Employing Fuzzy-Histogram Equalization to Combat Illumination Invariance in Face Recognition Systems
Статья научная
With the recent surge in acceptance of face recognition systems, more and more work is needed to perfect the existing grey areas. A major concern is the issue of illumination intensities in the images used as probe and images trained in the database. This paper presents the adoption and use of fuzzy histogram equalization in combating illumination variations in face recognition systems. The face recognition algorithm used is Principal Component Analysis, PCA. Histogram equalization was enhanced using some fuzzy rules in order to get an efficient light normalization. The algorithms were implemented and tested exhaustively with and without the application of fuzzy histogram equalization to test our approach. A good and considerable result was achieved.
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Energy Aware Ad Hoc On-Demand Multipath Distance Vector Routing
Статья научная
The current disjoint path Ad hoc On-Demand Multi-path Distance Vector (AOMDV) routing protocol does not have any energy-awareness guarantees. When AOMDV is used in wireless sensor networks (WSNs) energy is an important consideration. To enhance the AOMDV protocol an extra energy metric is added along with the hop count metric. This Energy aware or EA-AOMDV improves path selection using a trade-off between energy and hop count, thus giving more longevity to WSNs. EA-AOMDV is compared to the current AOMDV routing protocol to prove its worth in the context of WSNs. It is found that EA-AOMDV leads to better WSN energy-awareness in resource constrained WSNs.
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Energy Efficient Fitness Based Routing Protocol for Underwater Sensor Network
Статья научная
Underwater sensor network is one of the potential research arenas that opens the window of pleasing a lot of researcher in studying the field. Network layer of the underwater sensor networks must be one of the most attractive fields to build up anew protocol. In this paper, we proposed a underwater sensor network routing protocol named Energy Efficient Fitness based routing protocol for under water sensor networks (EEF) that promises the best use of total energy consumptions. The proposed routing protocol takes into account residual energy, depth and distance from the forwarding node to the sink node to guide a packet from source to the destination node. The prominent advantages of the proposed protocol are to confirm higher network life time and less end to end delay. The proposed protocol does not use control packet that causes much energy consumption and end to end delay. Simulation has been performed to certify the better result of the proposed routing protocol.
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Energy Optimized Ad hoc on-Demand Multipath Routing Protocol for Mobile Ad hoc Networks
Статья научная
As the wireless nodes are having limited battery life, energy efficiency is the most important design consideration in mobile ad hoc networks. Many multipath routing schemes are possibly exploiting multiple disjoint routes between any pair of source and destination in order to provide aggregated bandwidth, fault-tolerance and load-balancing properties. Hence we propose an optimized energy efficient routing scheme by slightly modifying MMRE-AOMDV route update rules in order to generate more energy efficient routes than MMRE-AOMDV routing protocol, called an Optimized Minimal Maximal nodal Residual Energy AOMDV (OMMRE-AOMDV) protocol. It reduces the energy consumption, average end to end delay, routing overhead and normalized routing overhead. It also improves packet delivery ratio and throughput. Simulation results show that the OMMRE-AOMDV routing protocol has performed better than AOMDV and MMRE-AOMDV routing protocols.
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Energy Resource Management of Assembly Line Balancing Problem using Modified Current Search Method
Статья научная
This paper aims to apply a modified current search method, adaptive current search (ACS), for assembly line balancing problems. The ACS algorithm possesses the memory list (ML) to escape from local entrapment and the adaptive radius (AR) mechanism to speed up the search process. The ACS is tested against five benchmark unconstrained and three constrained optimization problems compared with genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS outperforms other algorithms and provides superior results. The ACS is used to address the number of tasks assigned for each workstation, while the heuristic sequencing (HS) technique is conducted to assign the sequence of tasks for each workstation according to precedence constraints. The workload variance and the idle time are performed as the multiple-objective functions. The proposed approach is tested against four benchmark ALB problems compared with the GA, TS and CS. As results, the ACS associated with the HS technique is capable of producing solutions superior to other techniques. In addition, the ACS is an alternative potential algorithm to solve other optimization problems.
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Статья научная
Scheduling is an NP-hard problem, and heuristic algorithms are unable to find approximate solutions within a feasible time frame. Efficient task scheduling in Cloud Computing (CC) remains a critical challenge due to the need to balance energy consumption and deadline adherence. Existing scheduling approaches often suffer from high energy consumption and inefficient resource utilization, failing to meet stringent deadline constraints, especially under dynamic workload variations. To address these limitations, this study proposes an Energy-Deadline Aware Task Scheduling using the Water Wave Optimization (EDATSWWO) algorithm. Inspired by the propagation and interaction of water waves, EDATSWWO optimally allocates tasks to available resources by dynamically balancing energy efficiency and deadline adherence. The algorithm evaluates tasks based on their energy requirements and deadlines, assigning them to virtual machines (VMs) in the multi-cloud environment to minimize overall energy consumption while ensuring timely execution. Google Cloud workloads were used as the benchmark dataset to simulate real-world scenarios and validate the algorithm's performance. Simulation results demonstrate that EDATSWWO significantly outperforms existing scheduling algorithms in terms of energy efficiency and deadline compliance. The algorithm achieved an average reduction of energy consumption by 21.4%, improved task deadline adherence by 18.6%, and optimized resource utilization under varying workloads. This study highlights the potential of EDATSWWO to enhance the sustainability and efficiency of multi-cloud systems. Its robust design and adaptability to dynamic workloads make it a viable solution for modern cloud computing environments, where energy consumption and task deadlines are critical factors.
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Energy efficient routing protocol for delay tolerant network based on fuzzy logic and ant colony
Статья научная
The messages routing in a DTN network is a complicated challenge, due on the one hand of intermittent connection between the nodes, the lack of the end-to-end path between source / destination and on the other hand, the constraints related to the capacity of the buffer and the battery. To ensure messages delivery in such an environment, the proposed routing protocols use multiple copies of each message in order to increase the delivery ratio. Most of these routing protocols do not take into account the remaining energy of nodes and the history on the relays that have already received a copy of the message in order to select the nodes that will participate in the message routing. This paper proposes a new approach named EERPFAnt inspired by the ant colony intelligence and improved by the fuzzy logic technique to select the best relay by combining the energy level of the nodes, as well as the information on the relay that have already received a copy of the message to estimate intelligently, the energy level of the nodes at the time of encounter with the desired destination. Simulation results will show that the proposed approach performances are better than those of Epidemic routing protocols, Spray and Wait and ProPHET.
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Energy-Sustainable Framework and Performance Analysis of Power Scheme for Operating Systems: A Tool
Статья научная
Recently, an Information and Communications Technology (ICT) devices has become more user-friendly, which raised the problem of power dissipation across the globe and computer systems are one among them. This emerging issue of power dissipation has imposed a very significant issue on the system and software design. The concept of ‘green computing’ gaining popularity and is being considered as one of the most promising technology by the designers of Information Technology (IT) industry, which demonstrate the environmentally responsible way to reduce the power consumption and maximize the energy efficiency. In this paper, we have proposed an energy sustainable framework of the power schemes for operating systems to reduce the power consumption by computer systems and presented a Green Power tool (GP tool). This tool is designed using JAVA technology, which requires least configuration to make a decision for reducing the power consumption and proposed Swift mode algorithm, allows users to input the working time of their choice then after the end of time algorithm starts detection of human activity on the computer system. We also compared the Swift mode algorithm with existing power scheme in the operating system that provides up to 66% of the power saving. Finally, we have profiled the proposed framework to analyze the memory and Central Processing Unit (CPU) performance, which demonstrated that there is no memory leakage or CPU degradation problem and framework’s behavior remain constant under various overhead scenarios of the memory as well as CPU. The proposed framework requires 3–7 MB memory space during its execution.
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Energy-efficient and Load Distributed Clustering Algorithm for Dense Wireless Sensor Networks
Статья научная
Wireless sensor networks (WSNs) consist of a large number of tiny sensors with sensing, processing and transmission capabilities. Reducing energy consumption of nodes is one of the major objectives in the design of wireless sensor networks, as sensors have low power batteries. As data collection is the primary objective of WSNs and it consumes more energy, energy-efficient routing is a prominent solution to reduce the sensors energy consumption. This paper proposes an Energy-efficient and Load Distributed Clustering Algorithm (ELDCA) for routing in dense wireless sensor networks to reduce and distribute the sensors' energy consumption. The network is divided into number of virtual congruent square grids of defined sizes. The algorithm constructs optimal and load balanced clusters at every inner crossing point of grid cells using defined cluster fitness value. As early energy depletion is a major design issue in clustering protocols, the proposed algorithm provides a local substitution for energy suffering clusterheads (CHs). To prove the excellence of the proposed algorithm, extensive simulation experiments are performed under different network scenario. The results are compared with latest routing algorithms in terms of network lifetime, energy dissemination, and energy utilization.
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Enhanced Face Recognition using Data Fusion
Статья научная
In this paper we scrutinize the influence of fusion on the face recognition performance. In pattern recognition task, benefiting from different uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance. In features fusion approach, we fuse (concatenate) the feature vectors obtained using different feature extractors for the same image. Classification is then performed using different similarity measures. In decisions fusion approach, the fusion is performed at decisions level, where decisions from different algorithms are fused using majority voting. The proposed method was tested using face images having different facial expressions and conditions obtained from ORL and FRAV2D databases. Simulations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.
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Enhanced Hopfield Network for Pattern Satisfiability Optimization
Статья научная
Highly-interconnected Hopfield network with Content Addressable Memory (CAM) are shown to be extremely effective in constraint optimization problem. The emergent of the Hopfield network has producing a prolific amount of research. Recently, 3 Satisfiability (3-SAT) has becoming a tool to represent a variety combinatorial problems. Incorporated with 3-SAT, Hopfield neural network (HNN-3SAT) can be used to optimize pattern satisfiability (Pattern-SAT) problem. Hence, we proposed the HNN-3SAT with Hyperbolic Tangent activation function and the conventional McCulloch-Pitts function. The aim of this study is to investigate the accuracy of the pattern generated by our proposed algorithms. Microsoft Visual C++ 2013 is used as a platform for training, testing and validating our Pattern-SAT design. The detailed performance of HNN-3SAT of our proposed algorithms in doing Pattern-SAT will be discussed based on global pattern-SAT and running time. The result obtained from the simulation demonstrate the effectiveness of HNN-3SAT in doing Pattern-SAT.
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Enhanced Metaheuristic Algorithms for the Identification of Cancer MDPs
Статья научная
Cancer research revolves around the study of diseases that involve unregulated cell growth. This direction facilitated the development of a wide range of cancer genomics projects that are designed to support the identification of mutated driver pathways in several cancer types. In this research, a maximum weight submatrix problem is used to identify the driver pathway in a specific type of cancer. To solve this problem, we propose two new metaheuristic algorithms. The first is an improved harmony search (IHS) algorithm and the second is an enhanced genetic algorithm (EGA). Results show that EGA enables better performance and entails less computational time than does conventional GA. Furthermore, the new IHS offers a higher number of suggested gene set solutions for mutated genes than does the standard genetic algorithm.
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Enhanced Quantum Inspired Grey Wolf Optimizer for Feature Selection
Статья научная
Grey wolf optimizer (GWO) is a nature inspired optimization algorithm. It can be used to solve both minimization and maximization problems. The binary version of GWO (BGWO) uses binary values for wolves’ positions rather than probabilistic values in the original GWO. Integrating BGWO with quantum inspired operations produce a novel enhanced quantum inspired binary grey wolf algorithm (EQI-BGWO). In this paper we used feature selection as an optimization problem to evaluate the performance of our proposed algorithm EQI-BGWO. Our method was evaluated against BGWO method by comparing the fitness value, number of eliminated features and global optima iteration number. it showed a better accuracy and eliminates higher number of features with good performance. Results show that the average error rate enhanced from 0.09 to 0.06 and from 0.53 to 0.52 and from 0.26 to 0.23 for zoo, Lymphography and diabetes dataset respectively using EQI-BGWO, Where the average number of eliminated features was reduced from 6.6 to 6.7 for zoo dataset and from 7.3 to 7.1 for Lymphography dataset and from 2.9 to 3.2 for diabetes dataset.
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Статья научная
In the present era with the development of the innovation and the globalization, attrition of customer is considered as the vital metric which decides the incomes and gainfulness of the association. It is relevant for all the business spaces regardless of the measure of the business notwithstanding including the new companies. As per the business organization, about 65% of income comes from the customer's client. The objective of the customer attrition analysis is to anticipate the client who is probably going to exit from the present business association. The attrition analysis also termed as churn analysis. The point of this paper is to assemble a precise prescient model using the Enhanced Deep Feed Forward Neural Network Model to predict the customer whittling down in the Banking Domain. The result obtained through the proposed model is compared with various classes of machine learning algorithms Logistic regression, Decision tree, Gaussian Naïve Bayes Algorithm, and Artificial Neural Network. The outcome demonstrates that the proposed Enhanced Deep Feed Forward Neural Network Model performs best in accuracy compared with the existing machine learning model in predicting the customer attrition rate with the Banking Sector.
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Статья научная
The batch back prorogation algorithm is anew style for weight updating. The drawback of the BBP algorithm is its slow learning rate and easy convergence to the local minimum. The learning rate and momentum factor are the are the most significant parameter for increasing the efficiency of the BBP algorithm. We created the dynamic learning rate and dynamic momentum factor for increasing the efficiency of the algorithm. We used several data set for testing the effects of the dynamic learning rate and dynamic momentum factor that we created in this paper. All the experiments for both algorithms were performed on Matlab 2016 a. The stop training was determined ten power -5. The average accuracy training is 0.9909 and average processing time improved of dynamic algorithm is 430 times faster than the BBP algorithm. From the experimental results, the dynamic algorithm provides superior performance in terms of faster training with highest accuracy training compared to the manual algorithm. The dynamic parameters which created in this paper helped the algorithm to escape the local minimum and eliminate training saturation, thereby reducing training time and the number of epochs. The dynamic algorithm was achieving a superior level of performance compared with existing works (latest studies).
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Enhancement of Indoor Localization in WSN using PSO tuned EKF
Статья научная
In Wireless Sensor Networks, nodes are positioned arbitrarily and finding location of nodes is difficult. In this network, the nodes need to know their location is important for indoor applications. In this applications signals are affected by various factors such as noise, multipath, NLOS etc. This impact on inaccurate location information of node, which leads finding path to the destination node is difficult. Cooperative location based routing is alternative solution for finding better path. In this paper a solution is proposed for effective route in indoor application of WSN. The proposed solution uses Particle Swarm Optimization assisted Adaptive Extended Kalman Filter (PSO-AKF) for finding location of nodes. In this mechanism, finding accurate position of node impact on network performance such as minimization of delay, location error and also minimizes complexity.
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Enhancing Early Alzheimer's Disease Detection: Leveraging Pre-trained Networks and Transfer Learning
Статья научная
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide. Early and accurate AD detection is crucial for timely intervention and improving patient outcomes. Lately, there have been notable advancements in using deep learning approaches to classify neuroimaging data associated with Alzheimer's disease. These methods have shown substantial progress in achieving accurate classification results. Nevertheless, the concept of end-to-end learning, which has the potential to harness the benefits of deep learning fully, has yet to garner extensive focus in the realm of neuroimaging. This is attributed mainly to the persistent challenge in neuroimaging, namely the limited data availability. This study employs neuroimages and Transfer Learning (TL) to identify early signs of AD and different phases of cognitive impairment. By employing transfer learning, the study uses Magnetic Resonance Imaging (MRI) images from the Alzheimer's Disease Neuroimaging (ADNI) database to classify images into various categories, such as Cognitively Normal (CN), Early Mild Cognitive Impairment (EMCI), Mild Cognitive Impairment (MCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). The classification task involves training and testing three pre-trained networks: VGG-19, ResNet-50, and Inception V3. The study evaluates the performance of these networks using the confusion matrix and its associated metrics. Among the three models, ResNet-50 achieves the highest recall rate of 99.25%, making it more efficient in detecting the early stages of AD development. The study further examines the performance of the pre-trained networks on a class-by-class basis using the parameters derived from the confusion matrix. This comprehensive analysis provides insights into how each model performs for different classes within the AD classification framework. Overall, the research underscores the potential of deep learning and transfer learning in advancing early AD detection and emphasizes the significance of utilizing pre-trained models for this purpose.
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Enhancing Suicide Risk Prediction through BERT: Leveraging Textual Biomarkers for Early Detection
Статья научная
Suicide remains a critical global public health issue, claiming vast number of lives each year. Traditional assessment methods, often reliant on subjective evaluations, have limited effectiveness. This study examines the potential of Bidirectional Encoder Representations from Transformers (BERT) in revolutionizing suicide risk prediction by extracting textual biomarkers from relevant data. The research focuses on the efficacy of BERT in classifying suicide-related text data and introduces a novel BERT-based approach that achieves state-of-the-art accuracy, surpassing 97%. These findings highlight BERT's exceptional capability in handling complex text classification tasks, suggesting broad applicability in mental healthcare. The application of Artificial Intelligence (AI) in mental health poses unique challenges, including the absence of established biological markers for suicide risk and the dependence on subjective data, which necessitates careful consideration of potential biases in training datasets. Additionally, ethical considerations surrounding data privacy and responsible AI development are paramount. This study emphasizes the substantial potential of BERT and similar Natural Language Processing (NLP) techniques to significantly improve the accuracy and effectiveness of suicide risk prediction, paving the way for enhanced early detection and intervention strategies. The research acknowledges the inherent limitations of AI-based approaches and stresses the importance of ongoing efforts to address these issues, ensuring ethical and responsible AI application in mental health.
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