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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Mondal, Sudipto Rashid, Mamun Or Biswas, Shuvra Prokash Islam, Md. Rabiul Rajshahi University of Engineering & Technology Department of Electronics & Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering & Technology Department of Mechatronics Engineering Rajshahi6204 Bangladesh School of Electrical Computer & Telecommunications Engineering University of Wollongong NSW2522 Australia

ISBN: (纸本)9798331533892

Transformer-less grid-connected inverters gained its popularity in recent times due to their compact design, lower cost, lower weight and overall higher efficiency compared to transformer-based systems. However, among the other issues of the transformer-less solution, leakage current caused by common-mode (CM) voltage fluctuations remains a significant problem, impacting power output, system safety. This paper proposes an optimized switching scheme for a modified H7 transformerless photovoltaic (TLPV) inverter to address the issue by reducing the leakage current and increasing the power quality. Moreover, an optimized unipolar pulse width modulation (UPPWM) strategy is also presented to maintain fairly constant common mode voltage (CMV), effectively suppressing leakage current further. The modified H7 TLPV along with the optimized UPPWM strategy also ensures lower semiconductor power loss and overall better efficiency. The total harmonic distortion (THD) of the grid voltage and current with the optimized pulse width modulation (PWM) strategy found satisfactory which met the standards of IEEE-519. Simulation is conducted in the MATLAB/Simulink environment to validate the performance criteria over the other TLPV structures, feasibility of leakage current reduction and overall power loss. This novel strategy provides an efficient and minimal solution for the next generation TLPV. © 2025 IEEE.

关键词： Pulse width modulation

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Lightweight security Mechanism for IoT Applications using Substitution-Box 4

Lightweight security Mechanism for IoT Applications using Su...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Hanif, Fozia Shams, Rehan Waheed, Urooj University of Karachi Department of Mathematics Karachi Pakistan Sir Syed University of Engineering and Technology Karachi Department of Telecommunication Pakistan DHA Suffa University Karachi Department of Computer Science Pakistan

ISBN: (纸本)9798331518882

In recent times the use of small computing devices for gathering information from the real world is increasing day by day. The devices like wireless sensors, RIFD tags, embedded devices and IoT devices are required to transmit huge amounts of confidential data for monitoring and controlling. IoT applications have now become an important factor across various industries. That is why the security of IoT device is always been an important issue, because they transmit private and sensitive data. For handling the security issue cryptography is one the way for handling the security issues of IoT devices but sometimes cryptographic algorithms are cracked and as well as very costly. Lightweight cryptography requires less memory and less computational cost. Substitution box is a very useful concept in generating the lightweight cryptographic algorithm that provide robust security in the process of encryption. This study produces lightweight cryptographic encryption and decryption algorithm with the help of substitution boxes. The provided scheme not only provides the encryption procedure but also generating a flexible key structure which enhances the security for IoT devices where lightweight image encryption is crucial. The provided technique will be beneficial for those places where confidential public data has to be secured. The aim of this paper is to recommend effective original and straightforward techniques of encryption and decryption by using the substitution box. Experimental results show the efficiency of the suggested technique in terms of execution time and throughput. © 2025 IEEE.

关键词： Encryption algorithms

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Combining LLMs and Simheuristics: An Application to the Project Portfolio Selection Problem 2nd

Combining LLMs and Simheuristics: An Application to the P...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Saiz, Miguel Calvet, Laura Computer Science Department Universitat Oberta de Catalunya Barcelona08860 Spain Telecommunication and System Engineering Department Universitat Autònoma de Barcelona Sabadell08202 Spain

ISBN: (纸本)9783031782374

Large Language Models (LLMs) are very large deep learning models, which are pre-trained on vast amounts of data, able to achieve general-purpose language understanding and generation. GPT-4 is an increasingly popular example from OpenAI. We introduce a novel framework called Generative Heuristics, which hybridize (meta)heuristics for its quantitative capabilities with the qualitative proficiency of LLMs. To illustrate this framework, we address a rich version of the Project Portfolio Selection Problem with a Generative Heuristic relying on a Simheuristic Algorithm. Our solving approach explores candidate portfolios, optimizing quantitative outcomes, such as the Net Present Value, while maintaining a focus on non-quantifiable strategic contributions. To maximize realism and applicability, the strategic plan from the LIFE program (EU’s funding instrument for environment and climate action) and project summaries from actual LIFE-funded projects are utilized. A computational experiment is carried out to illustrate the feasibility and potential of our approach, which enhance quantitative search procedures with cognitive capabilities, offering decision-makers both a more comprehensive and versatile set of options. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Comparison of Machine Learning Algorithms for Detection of Stuttering in Speech

Comparison of Machine Learning Algorithms for Detection of S...

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International Conference on Machine Learning and Big Data Analytics, ICMLBDA 2023

作者： Gudlavalleti, Sarvagna Devi, P. Sunitha Lakka, Ramyasri Kuchanpally, Rithika Dudekula, Sai Sonali Department of Computer Science and Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India Department of Electronics and Telecommunication Engineering G Narayanamma Institute of Technology and Science Shaikpet Telangana Hyderabad India

ISBN: (纸本)9783031513374

Stuttering is a speech disorder characterized by the repetition of sounds, syllables, or words and the prolongation of sounds. An individual who stutters exactly knows what he or she would like to say but has trouble producing a normal flow of speech. This project aims to use machine learning algorithms to detect stuttering behavior in Telugu language speech samples. Stuttering is a speech disorder that affects the flow of speech, making it difficult to communicate with others. The study will collect voice samples from individuals reading the same Telugu script and generate 8- and 16-kHz samples in .wav files. The data will be annotated and coded on a 0–8 scale and will be generated as a dataset. Machine learning algorithms will be used to build a model for detecting stuttering patterns in the audio files. Finally, the model will be integrated with a user interface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User interfaces

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Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques

Advances in Type II Diabetes Prediction: A Comprehensive Rev...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Kushwah, Virendra Singh Krishnan, Sivaneasan Bala Upreti, Kamal Kumar, Manoj Kshirsagar, Pravin R. Singh, Vinai K. Singapore Institute of Technology Singapore School of Sciences Christ University Department of Computer Science DelhiNCR Ghaziabad India Gurukula Kangri University Department of Mathematics and Statistics Haridwar India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Gl Bajaj Group of Institutions Department of Computer Science & Engineering Mathura India

ISBN: (纸本)9798331527495

Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. © 2025 IEEE.

关键词： Support vector regression

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An efficient fault diagnosis model using Lappet Falco optimisation based on a deep neural network for the VSI under varying load conditions

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International Journal of Power Electronics 2025年第3期21卷 247-278页

作者： Baste, Vaishali Shende, Dipali Idhate, Seema Deshpande, Arya Department of Electronics and Telecommunication SKNCOE Vadgaon Pune India Department of Electronics and Telecommunication PCCOER Ravet India School of Computer Engineering and Technology MIT-WPU Pune India

Numerous industrial applications employ three-phase converters that are based on insulated-gate bipolar transistors (IGBTs). However, the functioning and safety of power electronic devices and loads can be considerably impacted by IGBT faults. Maintaining high-power quality and system availability requires timely and accurate detection of power inverter failures. Constantly monitoring the failures in three-phase voltage source inverter (VSI) has greatly improved maintenance efficiency and stability. Hence, the developed research employs the discrete wavelet transform (DWT) and Lappet Falco optimised deep neural network (LFO-DNN) model to create an open circuit fault detection model for the VSI circuit. Data collection involves extracting features such as three-phase voltage, current, speed, and torque from erroneous data. The DNN classifier trained on these features uses the average three-phase current value to identify faulty switches. The VSI acting as a load with variable frequency reference is connected to a three-phase induction motor. The proposed Lappet Falco optimisation accurately yields impressive results in terms of prediction accuracy of 96.34%, precision of 96.34%, recall of 96.24%, F1 measure of 94.23%, MSE of 3.66, and specificity of 95.28%, demonstrating high efficiency for both 90% training and a k-fold value of 10. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Insulated gate bipolar transistors (IGBT)

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Sustainable Optimization of Construction Project Portfolio Management Through a Data-Driven Approach 2nd

Sustainable Optimization of Construction Project Portfolio ...

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2nd Decision Science Alliance International Summer Conference, DSA ISC 2024

作者： Lostumbo, Marisa A. Iniesta, Marc Bara Calvet, Laura Computer Science Department Universitat Oberta de Catalunya Barcelona Spain EAE Business School Barcelona Spain Telecommunication and System Engineering Department Universitat Autónoma de Barcelona Sabadell Spain

ISBN: (纸本)9783031782374

Effective management of construction project portfolios demands informed decisions driven by data and mathematical models, aiming to enhance decision-making and address complex decision problems. This article introduces an innovative approach that prioritizes sustainability criteria selection, utilizing data-driven insights and mathematical optimization to optimize project portfolio management. Through multi-objective optimization, our study targets improved resource efficiency, time reduction, and minimized environmental impact, offering portfolio managers a powerful tool for decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Project management

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An Algorithmic Approach to Intrusion Detection in Ad Hoc Wireless Networks Based on Artificial Intelligence

An Algorithmic Approach to Intrusion Detection in Ad Hoc Wir...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Sonekar, Shrikant V. Kshirsagar, Pravin R. Unhelkar, Bhuvan Chakrabarti, Prasun Maram, Balajee Upreti, Kamal J D College of Engineering & Management Department of Computer Science & Engineering Nagpur India J D College of Engineering & Management Department of Electronics & Telecommunication Engineering Nagpur India Muma College of Business University of South Florida Department of Infornation Systems and Decision Sciences Sarasata United States Sir Padampat Singhania University Department of Computer Science & Engineering Rajasthan Udaipur3313606 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India Christ University Department of Computer Science Delhi NCR Ghaziabad India

ISBN: (纸本)9798331527495

The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs. © 2025 IEEE.

关键词： computer viruses

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Unveiling Hidden Water Resources: Deep Learning and Remote Sensing for Subsurface Hydrology for Environmental Health

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 352-364页

作者： Babu, Jampani Satish G, Dinesh Suganya, R. Radhakrishnan, P. Sutar, Anand S, Alex David Rao, L. Malleswara Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur AP Vaddeswaram India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur (KTR) Chennai India Department of Computer Science and Engineering (Data Science) New Horizon College of Engineering Karnataka Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Electronics and Telecommunication Bharati Vidyapeeth College of Engineering Navi Mumbai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R & d Institute of Science and Technology Chennai India Department of Physics Sri Y N College (A) West Godavari District Andhra Pradesh Narsapur India

Water scarcity in arid regions necessitates innovative solutions to uncover and manage subsurface water resources for environmental health. This study explores the potential of GeoHydroNet, a specialized deep learning framework, in identifying hidden water resources within arid regions. Utilizing remote sensing data such as multispectral and hyperspectral imagery, geological maps, and soil moisture datasets, GeoHydroNet combines graph neural Networks (GNNs), AutoEncoders, and generative adversarial networks (GANs) to address challenges in subsurface hydrology. The model achieved an accuracy of 93.5%, surpassing traditional models like CNNs (85.4%), LSTMs (88.1%), and RNNs (82.6%). GeoHydroNet demonstrates significant precision (0.88), recall (0.87), and sensitivity to data imbalance (0.91). Its physics-informed learning approach incorporates hydrological principles, ensuring realistic predictions. By analyzing subsurface water patterns, recharge zones, and geological influences, the model not only enhances prediction accuracy but also aids in sustainable groundwater management. GeoHydroNet’s performance in identifying groundwater levels, with minimal errors ranging from 0.62 to 3.07%, indicates its robustness in tackling water scarcity challenges in data-constrained environments. These findings highlight its transformative role in hydrological modeling and sustainable water resource management in arid regions. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Aquifers Arid regions Deep learning Generative adversarial networks GeoHydroNet Physics-informed learning Remote sensing Subsurface hydrology Sustainable water management

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Performance improvement of MIMO[sbnd]OFDM communication systems based on non-linear equalization

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computers and Electrical engineering 2025年 124卷

作者： Ramadan, Khaled Department of Telecommunication Engineering Collage of Engineering at Ahlia University Manama Bahrain Department of Communications and Computer Engineering The Higher Institute of Engineering at Al-Shorouk City Cairo Egypt

This paper introduces a nonlinear equalizer for Multiple Input Multiple Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems based on Discrete Wavelet Transform (DWT). It is referred to as the Joint Non-Linear Combining Zero Forcing-Successive Interference Cancellation (JNLCZF-SIC) equalizer. Unlike traditional equalizers, the JNLCZF-SIC equalizer performs both equalization and Carrier Frequency Offset (CFO) correction simultaneously. The main advantage of the proposed JNLCZF-SIC equalizer over existing methods is its ability to reduce simulation times without compromising performance by minimizing the number of arithmetic operations required. To assess its effectiveness, we evaluate its Bit-Error-Rate (BER) and computational load against standard equalization techniques such as Linear Zero-Forcing (LZF), Linear Minimum Mean Squared Error (LMMSE), and MMSE-Successive Interference Cancellation (MMSE-SIC) equalizers. Simulation results reveal that the JNLCZF-SIC equalizer not only achieves a lower BER but also significantly decreases computational complexity. This improvement in simulation time is particularly beneficial for large-scale MIMO systems, where DWT is employed to accelerate processing by breaking down the signal into multiple frequency bands, optimizing the equalization process. The equalizer's ability to simultaneously handle equalization and CFO compensation with reduced computational demands makes it highly suitable for real-time applications and large-scale implementations. Additionally, a closed-form expression for the count of arithmetic operations for the proposed equalizer in a general 2σ × 2σ MIMO configuration is provided. © 2025

关键词： Mean square error

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