International Journal of Intelligent Systems and Applications @ijisa
Статьи журнала - International Journal of Intelligent Systems and Applications
Все статьи: 1238
Simulation of High Step-Up DC–DC Converter for Photovoltaic Module Application using MATLAB/SIMULINK
Статья научная
As per the present scenario lot of power shortages are there in all over the world especially country like India the grid transferring problem is also high. Almost the power from the fossil fuels are becoming so less some of the examples of the fossil fuels are (coal, lignite, oil, and gases).So most of them looking in forward for the power from green or renewable based energies like solar, wind, biomass, tidal etc. Which does not cause any pollution to the environment. In this paper the simulation and analysis of the PV panel and also high efficient boost converter design and simulation is also performed. Even though the solar based systems are renewable based energies when compared to other renewable energies like wind, biomass it does not connect to more number of grid connections. Lot of necessary steps want to be taken one of the main important factor that high efficient boost converter is needed, here in this paper the input voltage to the boost converter is given as 15V and receives the output voltage of 55.64V
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Simulation on Different Proportions of Coal and Natural Gas Co-combustion in a Rotary Lime Kiln
Статья научная
Co-combustion of coal and natural gas is a promising technology in the production of active lime. For this technology, proper fuel proportion of coal and natural gas (α) is one of the key parameters that requires significant thought. By means of numerical simulation, contrast studies on co-combustion with five different fuel proportions were carried out. This paper firstly puts forward the models used to describe the system based on the actual conditions. Then, numerical simulation results were analysed in detail to illustrate the co-combustion process and the velocity and temperature distribution in the kiln. Finally, comparisons of high temperature region, char conversion, length of calcining zone, CO and NOx emission and total heat transfer rate to the material bed were made in order to make a decision on fuel proportion. Synthetically considering, α=30% is a balance between benefits and costs for the rotary lime kiln studied.
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Single and Multi-Area Optimal Dispatch by Modified Salp Swarm Algorithm
Статья научная
This paper presents modified salp swarm algorithm (MSSA) for solution of power system scheduling problems with diverse complexity level. Salp swarm algorithm (SSA) is a recently proposed efficient nature inspired (NI) optimization method inspired by foraging behaviour of salps found in deep ocean. SSA sometimes suffers to stagnation at local minima, to overcome this problem and enhancing searching capability by both exploration and exploitation MSSA is proposed in this paper. MSSA applied and tested on two types of problems. Type one is having five benchmark functions of diverse nature, whereas type two is related with real world problem of power system scheduling of a standard IEEE 114 bus system with 54 thermal units for (i) single area system, (ii) two area system and (iii) three area system. Finally Outcome of simulation results are validated with reported results by other method available in literature.
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Single and Multiple Hand Gesture Recognition Systems: A Comparative Analysis
Статья научная
With the evolution of higher computing speed, efficient communication technologies, and advanced display techniques the legacy HCI techniques become obsolete and are no more helpful in accurate and fast flow of information in present day computing devices. Hence the need of user friendly human machine interfaces for real time interfaces for human computer interaction have to be designed and developed to make the man machine interaction more intuitive and user friendly. The vision based hand gesture recognition affords users with the ability to interact with computers in more natural and intuitive ways. These gesture recognition systems generally consist of three main modules like hand segmentation, hand tracking and gesture recognition from hand features, designed using different image processing techniques which are further integrated with different applications. An increase use of new interfaces based on hand gesture recognition designed to cope up with the computing devices for interaction. This paper is an effort to provide a comparative analysis between such real time vision based hand gesture recognition systems which are based on interaction using single and multiple hand gestures. Single hand gesture based recognition systems (SHGRS) have fewer complexes to implement, with a constraint to the count of different gestures which is large enough with various permutations and combinations of gesture, which is possible with multiple hands in multiple hand gesture recognition systems (MHGRS). The thorough comparative analysis has been done on various other vital parameters for the recognition systems.
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Skin Diseases Expert System using Dempster-Shafer Theory
Статья научная
Based on World Health Organization (WHO) report in the 2011 Skin diseases still remain common in many rural communities in developing countries, with serious economic and social consequences as well as health implications. Directly or indirectly, skin diseases are responsible for much disability (and loss of economic potential), disfigurement, and distress due to symptoms such as itching or pain. In this research, we are using Dempster-Shafer Theory for detecting skin diseases and displaying the result of detection process. We describe five symptoms as major symptoms which include blister, itch, scaly skin, fever, and pain in the rash. Dempster-Shafer theory to quantify the degree of belief, our approach uses Dempster-Shafer theory to combine beliefs under conditions of uncertainty and ignorance, and allows quantitative measurement of the belief and plausibility in our identification result. The result reveal that Skin Diseases Expert System has been successfully detecting skin diseases and displaying the result of identification process.
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Sky-CNN: a CNN-based learning approach for skyline scene understanding
Статья научная
Skyline scenes are a scientific matter of interest for some geographers and urbanists. These scenes have not been well-handled in computer vision tasks. Understanding the context of a skyline scene could refer to approaches based on hand-crafted features combined with linear classifiers; which are somewhat side-lined in favor of the Convolutional Neural Networks based approaches. In this paper, we proposed a new CNN learning approach to categorize skyline scenes. The proposed model requires a pre-processing step enhancing the deep-learned features and the training time. To evaluate our suggested system; we constructed the SKYLINEScene database. This new DB contains 2000 images of urban and rural landscape scenes with a skyline view. In order to examine the performance of our Sky-CNN system, many fair comparisons were carried out using well-known CNN architectures and the SKYLINEScene DB for tests. Our approach shows it robustness in Skyline context understanding and outperforms the hand-crafted approaches based on global and local features.
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Статья
This study presents a deep learning-based approach to automated resume and job matching that uses semantic similarity between texts. The solution is based on SimCSE RoBERTa transformer embeddings and a Siamese neural architecture trained using the MSELoss loss function. Unlike traditional filtering systems by keywords or characteristics, the proposed model learns to place semantically compatible pairs (resume-vacancy) in a common vector space. Unlike traditional keyword-based or attributive matching systems, our method is designed to capture deep semantic alignment between resumes and job descriptions. To evaluate the effectiveness of this architecture, we conducted extensive experiments on a labelled dataset of over 7,000 resume–vacancy pairs obtained from the HuggingFace repository. The dataset includes three classes (Good Fit, Potential Fit, No Fit), which we restructured into a binary classification task. Annotation labels reflect textual compatibility based on skills, responsibilities, and experience, ensuring task relevance. It resulted in a moderately imbalanced dataset with approximately 66% positive and 34% negative examples. Labels were assigned based on semantic compatibility, including skill match, job responsibilities, and experience alignment. Our model achieved accuracy = 72%, precision = 70%, recall = 74%, F1-score = 72%, and Precision@10 = 75%, significantly outperforming both classical (TF-IDF + cosine similarity) and neural (Sentence-BERT without fine-tuning) baselines. These results validate the empirical effectiveness of our architecture for candidate ranking and selection. To justify the use of a complex Siamese architecture, the system was compared to two baselines: (1) a classical TF-IDF + cosine similarity method, and (2) a pretrained Sentence-BERT model without task-specific fine-tuning. The proposed model significantly outperformed both baselines across all evaluation metrics, confirming that its complexity translates to meaningful performance gains. A basic self-learning mechanism is implemented and functional. Recruiters can provide binary feedback (Fit / No Fit) for each recommended candidate, which is stored in a feedback table. This feedback can be used to retrain or fine-tune the model periodically, enabling adaptive behaviour over time. While initial retraining experiments were conducted offline, full automation and continuous integration of feedback into training pipelines remain a goal for future development. The system offers sub-5-second response times, integration with vector databases, and a web-based user interface. It is designed for use in HR departments, recruiting agencies, and employment platforms, with potential for broader commercial deployment and domain adaptation. We additionally implemented a feedback-driven retraining loop that enables future self-supervised adaptation. While UI and vector retrieval infrastructure were developed to support prototyping and deployment, the primary research innovation centres on the modelling framework, learning setup, and comparative evaluation methodology. This work contributes to the advancement of semantically-aware intelligent recruiting systems and offers a replicable baseline for future studies in neural recommendation for HR applications. The risks of algorithmic bias are emphasised separately: even in the absence of obvious demographic characteristics in the input data, the model can implicitly reproduce social or historical inequalities inherent in the data. In this regard, the study outlines areas for further development, in particular equity auditing, bias reduction techniques, and the integration of human validation in decision-making.
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Smart Parking System with Image Processing Facility
Статья научная
Smart Parking Systems obtain information about available parking spaces, process it and then place the car at a certain position. A prototype of the parking assistance system based on the proposed architecture was constructed here. The adopted hardware, software, and implementation solutions in this prototype construction are described in this paper. The effective circular design is introduced here having rack-pinion special mechanism which is used to lift and place the car in the certain position. The design of rack pinion mechanism is also simulated using AUTODESK INVENTOR and COMSOL software.
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Статья научная
Manually, to manage stocks amounts spending the every day in the rays to count for each product the number which it remains in stores, or to record by a scanner head barcode information dependent of each product. However, the mission become increasingly difficult if several warehouses are found, that involves much time to pass from a product to another, moreover that requires agents to carry out these spots. In this article we use a network architecture neuron combined with the readers bar code of technology vision, this method allows to know in real time information concerning each product in stock. It will allow besides introducing the concept of real stocks rather than physical. However The basic classical use of data and to feed it will be completely changed by the spheres of knowledge which generates the NN (Neural Network) to store information on the quantity at a given time (Dynamic inventory), the entries(delivery of suppliers ) and the outputs ( delivery or sale with the customers and use of manufacturing pieces or repair ).
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Solving Economic Load Dispatch Problem Using Particle Swarm Optimization Technique
Статья научная
Economic load dispatch (ELD) problem is a common task in the operational planning of a power system, which requires to be optimized. This paper presents an effective and reliable particle swarm optimization (PSO) technique for the economic load dispatch problem. The results have been demonstrated for ELD of standard 3-generator and 6-generator systems with and without consideration of transmission losses. The final results obtained using PSO are compared with conventional quadratic programming and found to be encouraging.
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Solving Practical Economic Dispatch Problems Using Improved Artificial Bee Colony Method
Статья научная
This paper presents an improved artificial bee colony (IABC) optimization method to solving practical economic dispatch taking into account the nonlinear generator characteristics such as valve-point loading effects. In order to exploit the performance of this new variant based ABC method to solving practical economic dispatch, a new local search mechanism (LSM) associated to the original ABC algorithm; it allows exploiting effectively the promising region to locate the best solution. The proposed approach has been examined and applied to many practical electrical power systems, the 13 generating units, and to the large electrical system with 40 generating units considering valve point loading effects. From the different case studies, it is observed that the results compared with the other recent techniques demonstrate the potential of the proposed approach and show clearly its effectiveness to solve practical and large ED.
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Solving Traveling Salesman Problem Through Genetic Algorithm with Clustering
Статья научная
The Traveling Salesman Problem (TSP) is a well-known NP-hard combinatorial optimization problem, commonly studied in computer science and operations research. Due to its complexity and broad applicability, various algorithms have been designed and developed from the viewpoint of intelligent search. In this paper, we propose a two-stage method based on the clustering concept integrated with an intelligent search technique. In the first stage, a set of clustering techniques - fuzzy c-means (FCM), k-means (KM), and k-mediods (KMD) - are employed independently to generate feasible routes for the TSP. These routes are then optimized in the second stage using an improved Genetic Algorithm (IGA). Actually, we enhance the traditional Genetic Algorithm (GA) through an advanced selection strategy, a new position-based heuristic crossover, and a supervised mutation mechanism (FIB). This IGA is implemented to the feasible routes generated in the clustering stage to search the optimized route. The overall solution approach results in three distinct pathways: FCM+IGA, KM+IGA, and KMD+IGA. Simulation results with 47 benchmark TSP datasets demonstrate that the proposed FCM+IGA performs better than both KM+IGA and KMD+IGA. Moreover, FCM+IGA outperforms other clustering-based algorithms and several state-of-the-art methods in terms of solution quality.
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Solving a Class of Non-Smooth Optimal Control Problems
Статья научная
In this paper, we first propose a new generalized derivative for non-smooth functions and then we utilize this generalized derivative to convert a class of non-smooth optimal control problem to the corresponding smooth form. In the next step, we apply the discretization method to approximate the obtained smooth problem to the nonlinear programming problem. Finally, by solving the last problem, we obtain an approximate optimal solution for main problem.
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Solving a Linear Programming with Fuzzy Constraint and Objective Coefficients
Статья научная
In this paper, we consider a method for solving a linear programming problem with fuzzy objective and coefficient matrix, where the fuzzy numbers are supposed to be triangular. By the proposed method, the Decision Maker will have the flexibility of choosing. The solving method is based on the Pareto algorithm, which converts the problem to a weighted-objective linear programming. For more illustration, after discussing the problem and the algorithm, we present an example, which its solutions are independent from the objective weights.
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Solving school bus routing problem using genetic algorithm-based model
Статья научная
School Bus Routing Problem is an optimization problem which falls under the class of the Vehicle Routing Problem. It involves the use of a fleet of vehicles to efficiently and optimally transport students to and from their schools. To solve this problem, optimal school bus routes are found by minimizing the number of buses, the number of routes and the total distance traversed along all routes. Manual routing of school buses have led to creation of many routes, increased number of buses and several buses navigating the same route, thereby incurring more cost. One of such methods used in solving school bus routing problems is meta-heuristic method which has proven better results in terms of optimal solution and reduced time complexity. In this study, Genetic algorithm is utilized to solve the school bus routing problem because of its simplicity and ability to generate many possible solutions. The algorithm is implemented in C# programming language and tested using secondary data obtained from Ondo State Free-School Bus Shuttle Scheme, Akure, Nigeria. The result shows that of all four nodes (bus stops) used in performance evaluation, Alakure to Oke-Aro junction bus stop presents as the best route which covers a total of 69 nodes with a total distance of 34.5km. This shows that there can be less number of buses in use and reduced number of routes in which the buses are assigned.
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Some Results of Intuitionistic Fuzzy Soft Matrix
Статья научная
The purpose of this article is to consider the notions of intuitionistic fuzzy soft matrices and some basic results. This work deals particularly with the definition of transpose of intuitionistic fuzzy soft matrices and then some properties of transpose of intuitionistic fuzzy soft matrices are studied. After that symmetric intuitionistic fuzzy matrices are also defined and some properties are discussed. Numerical examples are provided to make the concept clear.
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Sorted r-Train: an improved dynamic data structure for handling big data
Статья научная
In today’s computing era, the world is dealing with big data which has enormously expanded in terms of 7Vs (volume, velocity, veracity, variability, value, variety, visualization). The conventional data structures like arrays, linked list, trees, graphs etc. are not able to effectively handle these big data. Therefore new and dynamic tools and techniques which can handle these big data effectively and efficiently are the need of the hour. This paper aims to provide an enhancement to the recently proposed “dynamic” data structure “r-Train” for handling big data. With the emergence of the “Internet of Things (IoT)” technology, real-time handling of requests and services are pivotal. Therefore it becomes necessary to promptly fetch the required data as and when required from the enormous piles of big data that are generally located at different sites. Therefore an effective searching and retrieval mechanism must be provided that can handle these challenging issues. The primary aim of this proposed refinement is to provide an effective means of insertion, deletion and searching techniques to efficiently handle the big data.
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Source Code Author Attribution Using Author's Programming Style and Code Smells
Статья научная
Source code is an intellectual property and using it without author's permission is a violation of property right. Source code authorship attribution is vital for dealing with software theft, copyright issues and piracies. Characterizing author's signature for identifying their footprints is the core task of authorship attribution. Different aspects of source code have been considered for characterizing signatures including author's coding style and programming structure, etc. The objective of this research is to explore another trait of authors' coding behavior for personifying their footprints. The main question that we want to address is that "can code smells are useful for characterizing authors' signatures? A machine learning based methodology is described not only to address the question but also for designing a system. Two different aspects of source code are considered for its representation into features: author's style and code smells. The author's style related feature representation is used as baseline. Results have shown that code smell can improves the authorship attribution.
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Speed up linear scan in high-dimensions by sorting one-dimensional projections
Статья научная
High-dimensional indexing is a pervasive challenge faced in multimedia retrieval. Existing indexing methods applying linear scan strategy, such as VA-file and its variations, are still efficient when the dimensionality is high. In this paper, we propose a new access idea implemented on linear scan based methods to speed up the nearest-neighbor queries. The idea is to map high-dimensional points into two kinds of one-dimensional values using projection and distance computation. The projection values on the line determined by the first Principal Component are sorted and indexed using a B+-tree, and the distances of each point to a reference point are also embedded into leaf node of the B+-tree. When performing nearest neighbor search, the Partial Distortion Searching and triangular inequality are employed to prune search space. In the new search algorithm, only a small portion of data points need to be linearly accessed by computing the bounded distance on the one-dimensional line, which can reduce the I/O and processor time dramatically. Experiment results on large image databases show that the new access method provides a faster search speed than existing high-dimensional index methods.
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Spiking neural network and bull genetic algorithm for active vibration control
Статья научная
Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.
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