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Optimizing water purification in double slope solar stills using abc algorithm and machine learning techniques

DESALINATION AND WATER TREATMENT 2025年 322卷

作者： Mcluret Gnanaraj, S. Joe Patrick St Mother Theresa Engn Coll Dept Mech Engn Tuticorin Tamil Nadu India

The double-slope solar still is a promising technology for efficient water desalination using solar energy. It harnesses solar radiation for sustainable freshwater production in arid regions. In this era of intelligent optimization, this study integrates metaheuristic algorithms, computational intelligence, and statistical modeling to enhance performance. The Taguchi method refines experimental design, while machine learning and the Artificial Bee Colony (abc) algorithm optimize efficiency. The setup includes a 4 mm glass cover, 10 mm basin water depth, glass wool-enhanced fins, and 15 mm heat storage material. Experiments in Tuticorin, Tamil Nadu, validated system performance under real conditions. Results reveal 5380 ml/m2/day (101.51 % efficiency) with Taguchi optimization and 6000 ml/m2/day (113.21 % efficiency) using machine learning. The abc algorithm produced a lower 4295 ml/m2/day (81.04 % efficiency). This study marks a new era in AI-driven solar desalination for sustainable water purification.

关键词： Double slope solar still Machine learning abc algorithm Water distillation

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abc algorithm based optimization of 1-D hidden Markov model for hand gesture recognition applications

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COMPUTERS IN INDUSTRY 2018年 99卷 313-323页

作者： Sagayam, K. Martin Hemanth, D. Jude Karunya Univ Dept ECE Coimbatore Tamil Nadu India

Hand gestures are extensively used to communicate based on non-verbal interaction with computers. This mode of communication is made possible by implementing machine learning algorithms for pattern recognition. A stochastic mathematical approach is used to interpret the hand gesture pattern for classification. In this work, a predominant method is used by 1-D hidden Markov model (1-D HMM) for classifying the patterns and to measure its performance. During training phase, 1-D HMM is used to predict its next state sequence of hand gestures using dynamic programming methods such as Baum-Welch algorithm and Viterbi algorithm. However, dynamic programming based prediction methodologies are complex. To enhance the performance of 1-D HMM model, its parameter and observation state sequence must be optimized using bio-inspired heuristic approaches. In this work, Artificial Bee Colony (abc) algorithm is used for optimization. A hybrid 1-D HMM model with abc optimization has been proposed which has yielded a better performance metrics like recognition rate and error rate for Cambridge hand gesture dataset.

关键词： HCI Virtual reality 1-D HMM Baum-Welch algorithm Viterbi algorithm abc algorithm Cambridge hand gesture

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Minimum dose path planning for facility inspection based on the discrete Rao-combined abc algorithm in radioactive environments with obstacles

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Nuclear Science and Techniques 2023年第4期34卷 26-40页

作者： Kwon Ryong Hong Su Il O Ryon Hui Kim Tae Song Kim Jang Su Kim Faculty of Energy Science Kim Il Sung UniversityPyongyangDemocratic People’s Republic of Korea High-Tech Research&Development Centre Institute of Information TechnologyKim Il Sung UniversityPyongyangDemocratic People’s Republic of Korea

Workers who conduct regular facility inspections in radioactive environments will inevitably be affected by ***,it is important to optimize the inspection path to ensure that workers are exposed to the least amount of *** study proposes a discrete Rao-combined artificial bee colony(abc)algorithm for planning inspection paths with minimum exposure doses in radioactive environments with *** this algorithm,retaining the framework of the traditional abc algorithm,we applied the directional solution update rules of Rao algorithms at the employed bee stage and onlooker bee stage to increase the exploitation ability of the algorithm and implement discretion using the swap operator and swap *** increase the randomness of solution generation,the chaos algorithm was used at the initialization *** K-opt operation technique was introduced at the scout bee stage to increase the exploration ability of the *** path planning in an environment with complex structural obstacles,an obstacle detour technique using a recursive algorithm was *** evaluate the performance of the proposed algorithm,we performed experimental simulations in three hypothetical environments and compared the results with those of improved particle swarm optimization,chaos particle swarm optimization,improved ant colony optimization,and discrete Rao’s *** experimental results show the high performance of the proposed discrete Rao-combined abc algorithm and its obstacle detour capability.

关键词： Minimum dose Path planning Nuclear facility inspection abc algorithm Rao algorithms Swap sequence K-opt operation

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abc algorithm for URL Extraction 17th

ABC Algorithm for URL Extraction

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17th International Conference on Web Engineering (ICWE)

作者： Sanagavarapu, Lalit Mohan Sarangi, Sourav Reddy, Y. Raghu Int Inst Informat Technol Hyderabad Telangana India

ISBN: (纸本)9783319744339;9783319744322

Seed URLs, Content Classification, Indexing and Ranking are key factors for search results relevance. Domain specific search engines (DSSE) provide more relevant search results as they have lesser ambiguity issues. For wide usage of DSSEs, identification of seed URLs and related child URLs is required. Identification of seed URLs has been manual and takes longer duration for building/decisioning on URL availability for DSSE. We propose nature inspired Artificial Bee Colony algorithm for identification and scoring of seed and child URLs. We implemented the algorithm on 'Security' domain and extracted 34,007 seed URLs from Wikipedia data dump and 323,488 child URLs using the seed URLs. Based on the volume and the relevance of the extracted URLs, a decision for building a DSSE can be made easily.

关键词： abc algorithm URL extraction Domain specific search Crawler

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abc algorithm as Feature Selection for Biomarker Discovery in Mass Spectrometry Analysis

ABC Algorithm as Feature Selection for Biomarker Discovery i...

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4th Conference on Data Mining and Optimization (DMO)

作者： SyarifahAdilah, M. Y. Abdullah, Rosni Venkat, Ibrahim Univ Teknol MARA Pulau Pinang Dept Comp Sci & Math George Town 13500 Malaysia Univ Sci Malaysia Sch Comp Sci George Town 11800 Malaysia

ISBN: (纸本)9781467327183

Mass spectrometry technique is gradually gaining momentum among the recent techniques deployed by several analytical research labs which intends to study biological or chemical properties of complex structures such as protein sequences. Literature reveals that reasoning voluminous mass spectrometry data via sophisticated computational techniques inspired by observing natural processes adapted by biological life has been yielding fruitful results towards the advancement of fields including bioinformatics and proteomics. Such advanced approaches provide efficient ways to mine mass spectrometry data in order to extract discriminating features that aid in discovering vital information, specifically discovering disease-related protein patterns in complex protein sequences. This study reveals the use of artificial bee colony (abc) as a new feature selection technique incorporated with SVM classifier. Results achieved96 and 100% for sensitivity and specificity respectively in discriminating cirrhosis and liver cancer cases.

关键词： feature selection abc algorithm biomarker discovery mass spectrometry

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Fixed-time cooperative trajectory optimisation strategy for multiple hypersonic gliding vehicles based on neural network and abc algorithm

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AERONAUTICAL JOURNAL 2023年第1316期127卷 1737-1751页

作者： Zhang, X. Liu, S. Yan, J. Liu, S. Yan, B. Northwestern Polytech Univ Unmanned Syst Res Inst Xian 710072 Peoples R China Shanghai Arospace Equipment Manufacturer Co Ltd Shanghai 200245 Peoples R China Northwestern Polytech Univ Sch Astronaut Xian 710072 Peoples R China

Collaborative planning for multiple hypersonic vehicles can effectively improve operational effectiveness. Time coordination is one of the main forms of cooperation among multi-hypersonic glide vehicles, and time cooperation trajectory optimisation is a key technology that can significantly increase the success rate of flight missions. However, it is difficult to obtain satisfactory time as a constraint condition during trajectory optimisation. To solve this problem, a multilayer Perceptrona is trained and adopted in a time-decision module, whose input is a four-dimensional vector selected according to the trajectory characteristics. Additionally, the MLP will be capable of determining the optimal initial heading angle of each aircraft to reduce unnecessary manoeuvering performance consumption in the flight mission. Subsequently, to improve the cooperative flight performance of hypersonic glide vehicles, the speed-dependent angle-of-attack and bank command were designed and optimised using the Artificial Bee Colony algorithm. The final simulation results show that the novel strategy proposed in this study can satisfy terminal space constraints and collaborative time constraints simultaneously. Meanwhile, each aircraft saves an average of 13.08% flight range, and the terminal speed is increased by 315.6m/s compared to the optimisation results of general purpose optimal control software (GPOPS) tools.

关键词： cooperative trajectory optimization hypersonic gliding vehicle neural network abc algorithm

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Reliability Assessment and Profit Optimization of Multi-Unit Mixed Configured System Using abc algorithm Under Preventive Maintenance

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IEEE ACCESS 2024年 12卷 85190-85202页

作者： Ahmadini, Abdullah Ali H. Singla, Shakuntla Mangla, Diksha Modibbo, Umar Muhammad Rani, Shilpa Jazan Univ Coll Sci Dept Math Jazan 45142 Saudi Arabia Maharishi Markandeshwar MMEC Dept Math & Humanities Ambala 133207 India Modibbo Adama Univ Fac Phys Sci Dept Operat Res Yola 640231 Nigeria

This paper presents an arrangement of parallel and series concepts to determine the reliability features of a multifaceted system. The profit analysis examined the overall gain of the setup after maintenance, preventive maintenance, and corrective maintenance. The components' low efficiency is due to internal or external factors such as friction between the components and the corrosion of parts, and sometimes overloaded workloads slow the process of working, which can be reversed by applying proper preventive maintenance. The machine's complete breakdown can be corrected using proper corrective maintenance. From the system's compromised or malfunctioning state to its operational condition, maintenance rates are factored into the calculations in this study's computation. The notions of the Markov process and Kolmogorov's differential equations are utilized to develop the mathematical model of the planned structure. To optimize reliability indices, an artificial bee colony is used to maximize ATTF, availability, busy periods, and the expected visits of a preventive maintenance repairman. The profit is found after reliability indices. Here, we provide a mathematical illustration with graphical results presented.

关键词： abc algorithm preventive and corrective maintenance food source bees selection Markov birth process

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A Malicious Node Detection Strategy Based on Fuzzy Trust Model and the abc algorithm in Wireless Sensor Network

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IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第8期10卷 1613-1617页

作者： Pang, Baohe Teng, Zhijun Sun, Huiyang Du, Chunqiu Li, Meng Zhu, Weihua Northeast Elect Power Univ Sch Elect Engn Jilin 132000 Jilin Peoples R China Northeast Elect Power Univ Key Lab Modern Power Syst Simulat & Control & Ren Minist Educ Jilin 132000 Jilin Peoples R China Beijing Elect Sci & Technol Inst Dept Cryptog Sci & Technol Beijing 100000 Peoples R China Jilin Technol Coll Elect Informat Sch Informat Technol Jilin 132000 Jilin Peoples R China

Wireless sensor network (WSN) nodes owing to their openness, are susceptible to several threats, one of which is dishonest recommendation attacks providing false trust values that favor the attacker. In this letter, a malicious node detection strategy is proposed based on a fuzzy trust model and artificial bee colony algorithm (abc) (FTM-abc). The fuzzy trust model (FTM) is introduced to calculate the indirect trust, and the abc algorithm is applied to optimize the trust model for detecting dishonest recommendation attacks. Besides, fitness function includes recommended deviation and interaction index deviation to enhance the effectiveness. Simulation results reveal the improved FTM-abc maintains a high recognition rate and a low false-positive rate, even if the number of dishonest nodes reaches 50%.

关键词： Wireless sensor networks Indexes Bayes methods Artificial intelligence Time series analysis Stochastic processes Simulation Wireless sensor network dishonest recommendation attacks fuzzy trust abc algorithm

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Harmonics elimination in multilevel inverter with unequal DC sources by fuzzy-abc algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2015年第3期27卷 273-292页

作者： Malarvizhi, M. Gnanambal, I. Gnanamani Coll Technol Dept Elect & Elect Engn Namakkal Tamil Nadu India Govt Coll Engn Dept Elect & Elect Engn Salem Tamil Nadu India

The harmonious appearance in multilevel inverter output voltage is more for the case of unequal DC sources. In this paper, a hybrid technique incorporating fuzzy inference system (FIS) and artificial bee's colony (abc) algorithm is proposed. FIS is a rule-based artificial intelligent technique which is used for generating the data set in terms of switching angle, harmonic voltage and harmonic distortion. The data set is generated as per the behaviour of the multilevel inverter without using any harmonic elimination technique. In the generated data set, the switching angle and the harmonic voltage are categorised as SMALL, MEDIUM and LARGE. Then, the abc algorithm is used to optimise the selection of switching angles from the training data set. The performance of the proposed hybrid technique is tested on a 7-level cascade H-bridge inverter for different voltage levels of unequal DC sources using MATLAB/SIMULINK platform. The effectiveness and superiority of the proposed technique is evaluated by comparing the reduction capacity of total harmonic distortion for different voltage levels of unequal DC sources with particle swarm optimisation (PSO) algorithm and fuzzy-PSO algorithm.

关键词： abc algorithm harmonic voltage fuzzy inference system hybrid technique unequal DC source harmonic elimination switching angle

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Optimal Planning of Residential Microgrids Based on Multiple Demand Response Programs Using abc algorithm

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IEEE ACCESS 2022年 10卷 116564-116626页

作者： Habib, Habib Ur Rahman Waqar, Asad Junejo, Abdul Khalique Ismail, Moustafa Magdi Hossen, Monir Jahangiri, Mehdi Kabir, Asif Khan, Sheheryar Kim, Yun-Su Univ Engn & Technol Taxila Fac Elect & Elect Engn Dept Elect Engn Taxila 47050 Pakistan Bahria Univ Dept Elect Engn Islamabad 44000 Pakistan Qaid e Awam Univ Engn Sci & Technol Dept Elect Engn Nawabshah 67450 Pakistan Minia Univ Sch Elect & Elect Engn El Minya 61519 Egypt Khulna Univ Engn & Technol Dept Elect & Commun Engn Khulna 9203 Bangladesh Islamic Azad Univ Energy Res Ctr Shahrekord Branch Shahrekord *** Iran Univ Kotli Dept CS & IT Kotli 11100 Azad Jammu & Ka Pakistan Hong Kong Polytech Univ Coll Profess & Continuing Educ Sch Profess Educ & Execut Dev Hung Hom Hong Kong Peoples R China Gwangju Inst Sci & Technol GIST Grad Sch Energy Convergence Gwangju 61005 South Korea

The smart grid has revolutionized the conventional electricity grid with the proposition of demand-side management (DSM). A DSM program enables the user to schedule its energy consumption in compliance with any pricing signal. This scheduling helps the grid operator to reduce the peak load demand and jointly benefits the user to reduce its electricity costs. Despite that, while doing so, it jeopardizes the user's comfort. In the present paper, the authors have investigated the impact of communal DSM programs on the consumption patterns of users, including single as well as multiple households. The objective is to simultaneously minimize the electricity costs and user discomfort to make a win-win situation for both the grid operator and the user. Therefore, a multi-objective optimization problem (MOOP) has been formed to simultaneously minimize the daily electricity cost, peak to average ratio (PAR) of load demand, user discomfort, environmental emission, and total net present cost (TNPC). In order to evaluate the best scheduling method, sizing scenarios for a residential microgrid in a Southern Pakistani metropolis surrounded by rural areas are presented in this paper. The originality of this article comes from a comparison of the techno-economic and environmental performance of several sizing options for a residential load powered by renewable energy. The artificial bee colony (abc) algorithm has been selected to solve the MOOP. The DSM programs are based upon different pricing signals, including real-time electricity pricing (RTEP), critical peak pricing (CPP), time of use (TOU), and day-ahead pricing (DAP) pricing. The results of the proposed abc algorithm are compared with GA and standard algorithms, and they reveal the effectiveness of the proposed method. When demand response is used, the suggested optimization technique shows that the SH spring with PV/WG/grid-connected microgrid is the most investable-reliable sizing option with a minimal TNPC of $1405.18 fo

关键词： Demand side management multi-objective optimization peak to average ratio electricity cost user comfort environmental emission RTEP CPP TOU DAP residential microgrid optimal operation load shifting genetic algorithm abc algorithm

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