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A novel deep learning approach for investigating liquid fuel injection in combustion system

Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Cost-Effective Optimization of Sizing and Charging Profiles for PHEV Parking Lots in Smart Microgrids Using Harmony Search Algorithm

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IEEE Access 2025年 13卷 79053-79069页

作者： Kamarposhti, Mehrdad Ahmadi Shokouhandeh, Hassan Ahmed, Emad M. Zaki, Zaki A. Colak, Ilhami Barhoumi, El Manaa Eguchi, Kei Islamic Azad University Department of Electrical Engineering Jo.C. Jouybar Iran Department of Electrical Engineering Tehran*** Iran Jouf University College of Engineering Department of Electrical Engineering Al-Jouf Sakaka72388 Saudi Arabia Istinye University Faculty of Engineering and Natural Science Department of Electrical and Electronics Engineering Istanbul34010 Turkey Dhofar University College of Engineering Department of Electrical and Computer Engineering Salalah Oman Fukuoka Institute of Technology Department of Information Electronics Fukuoka811-0295 Japan

This paper presents an optimization approach for managing the charging and discharging of electric vehicles (EVs) in parking lots using the Harmony Search (HS) and Differential Evolution (DE) algorithms. The study is conducted on a standard IEEE 33-bus grid considering three EV penetration levels: 11.3%, 35%, and 45%. The objective is to minimize operational costs while improving grid performance. Simulation results indicate that increasing EV penetration slightly raises overall expenses due to the higher cost of vehicle charging compared to the revenue from discharging. However, EV participation significantly reduces ohmic losses and improves the grid load profile. The proposed HS algorithm outperforms the DE algorithm by achieving lower microgrid costs and better convergence efficiency. Specifically, HS reduces energy losses by up to 40%, demonstrating its effectiveness in optimizing energy management for microgrids with high EV integration. © 2013 IEEE.

关键词： Electric buses

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The Role of Generative AI in Deepfake Detection: A Systematic Literature Review

The Role of Generative AI in Deepfake Detection: A Systemati...

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International Conference on Smart Business and technology, ICSBT 2024

作者： Acim, Btissam Kharmoum, Nassim Lagmiri, Souad Najoua Ziti, Soumia Department of Computer Science Intelligent Processing & Security of Systems Faculty of Sciences Mohammed V University in Rabat Rabat Morocco Rabat Morocco IT Networking Security and Management Laboratory Higher Institute of Management Administration and Computer Engineering Rabat Morocco

ISBN: (纸本)9783031866975

There is a possibility that deepfakes will increase a great deal of false information in a very realistic way, but it seems that today such technology appears to be extremely dangerous. While generative Artificial Intelligence (AI) does expand possibilities and give an opportunity to achieve terrific outcomes and has innumerable potential uses. This systematic literature review focuses on the role of generative AI in the improvement of deepfake detection technologies, providing a comparison between different methods and assessing their effectiveness. By analyzing recent studies, it depicts the techniques employed most frequently in deepfake detection, such as the ones concentrating on the intricacies between the visual and auditory elements, the temporal domains, as well as any minor signs of alteration. Due to the fact that generative AI is rapidly improving, the methods that are used to detect its use are often inadequate, which calls for more studies on the subject matter. Besides, by combining audio and visual elements when performing the analysis, calling it as a method that can be useful in focusing the detection growth potential with diverse and unstructured data. Also, this study evaluates the advantages and limitations of the techniques existing today and demonstrates their relative effectiveness in controlled and real conditions. Clarifying that measures aimed at detecting AI generated content should be preoccupied with a technique that is able to keep pace with changes in generative AI. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Integrated Data Representation and Formal Analysis for Human and In Silico Experimentation in Cognitive Psychology 11th

Integrated Data Representation and Formal Analysis for Hum...

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11th International Symposium on From Data to Models and Back, DataMod 2023

作者： Cerone, Antonio Pluck, Graham Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Faculty of Psychology Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9783031872167

This paper presents a novel framework aimed at enhancing the experimental methodology within cognitive psychology, integrating both informal and formal components to accommodate varied reasoning approaches. The framework supports the design and performing of experiments, and the structured storage of the information on outcomes in a way that is independent of specific interpretations and findings. This facilitates the replication and reproduction of the experiments and their interpretation under alternative theories. Moreover, the formal components of the information stored can be used for in silico simulation as well as within rigorous analytical approaches, such as formal methods and process mining. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： BRDL Cognitive psychology Data storage Experiment builder Experimental variables Formal methods In silico simulation Process mining Real-Time Maude

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Design and optimization for industrial reactor and crude separation process via coupling mechanistic kinetics with heat/momentum transfers: Acetic acid hydrogenation to ethanol

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International Journal of Hydrogen Energy 2025年 130卷 462-479页

作者： Zhou, Can You, Chen-Xi Liu, Yang Shi, Hui Cui, Chengtian Tang, Jihai Cui, Mifen Qiao, Xu Xia, Ming State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical Engineering Nanjing Tech University Nanjing211816 China School of Chemistry and Molecular Engineering Nanjing Tech University Nanjing211816 China Nanjing211800 China Department of Chemical Engineering Delft University of Technology Van der Maasweg 9 Delft2629 HZ Netherlands Process and Systems Engineering Laboratory Faculty of Science and Engineering Åbo Akademi University Henrikinkatu 2 TurkuFI-20500 Finland

Ethanol is used to produce various value-added chemicals and as automobile fuel. Acetic acid hydrogenation to ethanol is of practical significance to meet the increasing market. However, limited engineering research for reactor and crude separation process for the acetic acid hydrogenation to ethanol despite the increasingly mature catalyst system. Moreover, the traditional approach of industrial reactor design mainly relies on point data and inadequately quantifies the strong coupling between reaction rate and transfers within the reactor, which is prone to local and loose design and optimization. In this work, a coupled design approach that combines kinetics with transfers is proposed for designing and optimizing the multi-tubular fixed-bed reactor for the acetic acid hydrogenation to ethanol. To efficiently achieve the products crude separation, staged cooling/flash/absorption/desorption units featuring with N-methyl-2-pyrrolidone as an absorbent is proposed, numerically designed and optimized. Further heuristic heat integration is also investigated to conserve extra energy of the preliminary process, which features that a by-product steam generated from ethanol synthesis reactor is utilized to drive the reboiler of the desorption. It is demonstrated that the heat-integrated process presents significant economic and emission advantages compared with the preliminary process, specifically with 36.5 % and 10.9 % reductions in operating cost and total annual cost respectively, as well as 58.1 % reductions in CO2 emissions. The cost of synthesizing ethanol with 100 ktpy production is as low as 14.25 $/t. This work could provide a feasible and promising reactor and crude separation process for acetic acid hydrogenation to ethanol, which features economic, high-efficient, energy-saving, and low-carbon. © 2025 Hydrogen Energy Publications LLC

关键词： Reaction rates

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Gupacker: Generalized Unpacking Framework for Android Malware

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4338-4352页

作者： Zheng, Tao Hou, Qiyu Chen, Xingshu Ren, Hao Li, Meng Li, Hongwei Shen, Changxiang Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University School of Cyber Science and Engineering Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province School of Computer Science and Information Engineering Hefei230002 China University of Padua Department of Mathematics HIT Center Padua35131 Italy University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University Cyber Science Research Institute Key Laboratory of Data Protection and Intelligent Management Ministry of Education Chengdu610065 China

Android malware authors often use packers to evade analysis. Although many unpacking tools have been proposed, they face two significant challenges: 1) They are easily impeded by anti-analysis techniques employed by packers, preventing efficient collection of hidden Dex data. 2) They are typically designed to unpack a specific packer and cannot handle malware packed with mixed packers. Consequently, many packed malware samples evade detection. To bridge this gap, we propose Gupacker , a novel generalized unpacking framework. Gupacker offers a generic solution for first-generation holistic packer by customizing the Android system source code. It identifies the type of packer and selects an appropriate unpacking function, constructs a deeper active call chain to achieve generic unpacking of second-generation function extraction packers, and uses JNI function and instruction monitoring to handle third-generation virtual obfuscation packer. On this basis, we counteract a diverse array of anti-analysis techniques. We conduct extensive experiments on 5K packed Android malware samples, comparing Gupacker with 2 commercial and 4 state-of-the-art academic unpacking tools. The results demonstrate that Gupacker significantly improves the efficiency of Android malware unpacking with acceptable system overhead. We analyze real packed applications based on Gupacker and found several are second-packed by attackers, including WPS for Android, with tens of millions of users. We receive and responsibly report 13 0day vulnerabilities and also assist in the remediation of all vulnerabilities. © 2005-2012 IEEE.

关键词： Android malware

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Decentralized Online Examination Platforms Employing Smart Contracts for Security 12th

Decentralized Online Examination Platforms Employing Smart C...

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12th International Conference on Big Data Analytics, BDA 2024

作者： Sarode, Rashmi P. Watanobe, Yutaka Bhalla, Subhash Department of Computer Science and Engineering IIT Madras Chennai India Department of Information Systems University of Aizu Aizuwakamatsu Japan School of IT IIT Delhi Delhi India

ISBN: (纸本)9783031861925

There are multiple challenges posed by the traditional online examination system in terms of security, scalability, and transparency. This manuscript considers providing a new and secure management system by using Distributed Ledger Technology (DLT). The proposed system employs smart contracts to demonstrate its capabilities to manage examination data with a high degree of security and efficiency. The creation, deployment and execution the smart contracts are tested. This decentralized solution not only enhances the integrity and transparency of the examination process but also enhances access for any institution, which makes this solution rather robust and reliable. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Smart contract

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Mining Quantitative Association Rules 17th

Mining Quantitative Association Rules

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Yen, Show-Jane Lee, Yue-Shi Lee, Wen-Hao Department of Computer Science and Information Engineering Ming Chuan University Taipei Taiwan Department of Management Information Systems National Chiayi University Chiayi City Taiwan

ISBN: (纸本)9789819606948

Mining quantitative association rules involves identifying the relationships between items based on their purchased quantities from a transaction database. In general, items with varying quantities are treated as distinct new items, making it challenging to meet the criteria for these new items. Previous methods have often relaxed these criteria to uncover quantitative association rules;however, excessive relaxation can lead to the discovery of many irrelevant rules, while insufficient relaxation may result in few or no rules being found. Additionally, the quantity ranges associated with items can be divided into intervals, and some of these intervals need to be combined, potentially leading to information loss during the process. In this article, we present new definitions and an approach for discovering quantitative association rules that address the issues of user-defined criteria, range segmentation, and interval integration, allowing us to identify the quantitative association rules that genuinely interest users. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data mining

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A Customized Mobile Application for Exploring Chennai City: An Ultimate Sightseeing Companion 5th

A Customized Mobile Application for Exploring Chennai City: ...

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5th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2024

作者： Priya, L. Sandhiya, M. Poornimathi, K. Department of Information Technology Rajalakshmi Engineering College Chennai India Department of Computer Science and Business Systems Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819788606

“TourChennai” is a customized Tourism Suggestion Android application designed to provide a seamless and user-friendly experience for exploring the city of Chennai. Developed using Android Studio, Java, and XML, the app offers comprehensive information about tour attractions, events, hotels, and other points of interest in Chennai. Its intelligent features provide real-time suggestions and personalized recommendations to enhance the user's journey. Rigorous testing with multiple users confirmed the app's robustness and user-friendliness, making it an ultimate sightseeing companion for travelers in Chennai. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Android studio Application Database Events Tourism

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