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Applied artificial intelligence and Computing (ICAAIC), International Conference on

作者： Nilankar Bhanja Akila A Devulapalli Sudheer Ashok Kumar Pramit Brata Chanda Rakesh Dani Department of Electronics and Communication Engineering Techno Engineering College Banipur Habra West Bengal India Department of Artificial Intelligence and Data Science R.M.K College of Engineering and Technology Puduvoyal Tamil Nadu India Department of Computer Science and Engineering Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computer Science and Engineering Kalyani Government Engineering College Kalyani West Bengal India Department of Hospitality Management Graphic Era Deemed to be University Dehradun Uttarakhand India

The problem of atmospheric air pollution is one of the key environmental problems. In order to determine the factors that make the greatest contribution to air pollution and to counter them in a timely manner, it becomes necessary to constantly monitor the air environment. Currently, monitoring is carried out at stationary sources of pollutants, however, the share of pollution by exhaust gases of motor vehicles has increased. Thus, in order to obtain an objective picture, it is necessary to monitor pollution by motor vehicles, which, with the classical approach, using a variety of gas analyzers, is extremely costly. It is proposed to assess the state of the atmosphere indirectly, through calculations, based on the state of weather conditions, terrain, traffic intensity and car models, from which it is possible to obtain information on the type and amount of emitted pollutants. The article discusses the applicability of machine learning algorithms to the problem of predicting the state of air pollution. A review of the main prediction models was carried out, as well as the effectiveness of their application. Model prediction time estimates are obtained for a fixed error value.

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PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight Factor of Whale Optimization Algorithm

PAPR Reduction in MIMO-OFDM using Nonlinear Inertia Weight F...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Mohammed I. Habelalmateen R Archana Reddy Chetana Reddy N. Nagarjuna Pradeep Kumar S Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor Malaysia Department of Computer Technical Engineering College of Technical Engineering the Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana India Department of Electronics and Communication Oxford College of Engineering Bangalore Department of CSIT CVR College of Engineering Hyderabad India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10 −2.5 , SNR 10 −3.4 and PAPR of . 10 −4.2 , underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems.

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_{2}$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Classification of Normal and Cancer Cells by Using Signal Processing Techniques-A Survey

Classification of Normal and Cancer Cells by Using Signal Pr...

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Computer, Power and Communications (ICCPC), International Conference on

作者： G. Dency Flora S.R. Indurekaa S. Dhivya V. Janet Mercy S. Saranya C. Ganesh Department of CCE Sri Eshwar College of Engineering Coimbatore India Department of Artificial Intelligence and data science Dr. Mahalingam College of Engineering and Technology Coimbatore India Department of ECE Sri Ramakrishna Institute of Technology Coimbatore India Department of ECE University VOC College of Engineering Thoothukudi India

Cancer is the most dreadful complaint amongst all the diseases. Relating cancer by the use of natural trials is a time consuming process. With the advancements in the Genomic signal processing, the genes are obtained and signal processing techniques is used for the classification of normal and abnormal genes which helps in earlier detection of cancer. In this study, diverse numerical illustration schemes for the conversion of DNA collection right into a discrete time sign is presented. Various virtual sign processing strategies along with Discrete Fourier Transform (DFT), Discrete Wavelet Transform (DWT), Entropy, Parametric Models and Digital Filters used for the prediction is likewise surveyed.

关键词： Proteins Wavelet domain Discrete Fourier transforms Genomics Wavelet analysis Feature extraction Discrete wavelet transforms

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Hybrid Optimization-Based Resource Allocation and Admission Control for QoS in 5G Network

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International Journal of Communication Systems 2025年第10期38卷

作者： Kumar, Kulbir Noliya, Amandeep Kumar, Dharmender Mathur, Samiksha Department of Computer Science and Engineering Guru Jambheshwar University of Science and Technology Haryana Hisar India Department of Artificial Intelligence and Data Science Guru Jambheshwar University of Science and Technology Haryana Hisar India

In the present world, network slicing is a viable approach to provide customized logical and digitalized applications in the fifth generation (5G). Furthermore, effective implementation of network slicing requires resource allocation (RA) and call admission control (CAC). As a result, this paper proposes the Al-Biruni Remora Optimization Algorithm (BEROA)-based RA and Deep Q Net (DQN)-based CAC in a 5G network. The entire approach consists of 5G network simulation, optimal RA, and CAC. The 5G network simulation is the initial process, and the optimal RA is employed to allocate resources using the proposed BEROA. Furthermore, the DQN is utilized to perform the CAC using the user parameters like throughput, path loss, Reference Signal Received Power (RSRP), signal-to-interference-plus-noise ratio (SINR), and delay as well as the cell parameters like the count of users, cell throughput, cell power, and subcarrier power. After the admission of the new user in the cell, the count of active users in every cell is updated. Moreover, the BEROA + DQN-based RA and CAC attain the finest throughput of 15.88 Mbps, delay of 0.232 s, number of user drops of 11.89, cell power of 0.264 dbm, call blocking probability of the new users of 9, 0.313, call blocking probability of the handoff users of 0.435, call dropping probability of the new users of 0.510, and call dropping probability of the handoff users of 0.474. © 2025 John Wiley & Sons Ltd.

关键词： Resource allocation

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Mitochondrial Haplogroup Assignment for High-Throughput Sequencing data from Single Individual and Mixed DNA Samples 1

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16th International Symposium on Bioinformatics Research and Applications, ISBRA 2020

作者： Alqahtani, Fahad Măndoiu, Ion I. Computer Science and Engineering Department University of Connecticut StorrsCT United States National Center for Artificial Intelligence and Big Data Technology King Abdulaziz City for Science and Technology Riyadh Saudi Arabia

ISBN: (数字)9783030578213

ISBN: (纸本)9783030578206

The inference of mitochondrial haplogroups is an important step in forensic analysis of DNA samples collected at a crime scene. In this paper we introduced efficient inference algorithms based on Jaccard similarity between variants called from high-throughput sequencing data of such DNA samples and mutations collected in public databases such as PhyloTree. Experimental results on real and simulated datasets show that our mutation analysis methods have accuracy comparable to that of state-of-the-art methods based on haplogroup frequency estimation for both single-individual samples and two-individual mixtures, with a much lower running time. © 2020, Springer Nature Switzerland AG.

关键词： Inference engines

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Developing Eye Tracking Exercise System for Treatment of Lazy Eye

Developing Eye Tracking Exercise System for Treatment of Laz...

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Medical Technologies National Conference (TIPTEKNO)

作者： Kaan Kurtel Melahat Kizil Umuthan Ercan Suzan E. Ozsimsek Hasan Baris Mihci Department of Software Engineering Izmir University of Economics Balcova Izmir Turkey Izmir University of Economics Paramedic Program Vocational School of Health Services Luleå University of Technology MSc. Student in Applied Artificial Intelligence E-commerce Project Manager Macquarie University MSc. Student in Data Science

The aim of this study is to develop a fun and effective computer application game for children with amblyopia and to prevent eye strain in people whose work or lifestyle involves constantly looking at screens. The main motivation is to use children’s enthusiasm for playing video games for the treatment of amblyopia. For this, hardware and software products were brought together, and a software was developed according to software engineering principles. The system was tested with a group of volunteers and necessary alterations were made.

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Recent Advances in Space-Mapping-Based Multiphysics Optimization for Microwave Filters 4

Recent Advances in Space-Mapping-Based Multiphysics Optimiza...

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4th International Applied Computational Electromagnetics Society Symposium in China, ACES-China 2021

作者： Zhang, Wei Feng, Feng Yan, Shuxia Liu, Wenyuan Na, Weicong Zhang, Qi-Jun Carleton University Department of Electronics OttawaON Canada School of Microelectronics Tianjin University Tianjin China Tianjin Polytechnic University Department of Electronics and Information Engineering China School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology China Beijing University of Technology Faculty of Information Technology Beijing100022 China

ISBN: (纸本)9781733509619

Multiphysics-based optimization is a useful technique for high performance microwave design. An overview for recent advancements in multiphysics design using space mapping (SM) techniques is provided in this paper. We develop a surrogate model including two mapping modules and an EM coarse model using SM. This technique provide a effective and larger update for every iteration by utilizing the information of the coarse model represented using inexpensive EM domain samples, resulting in optimal solutions being obtained more efficient than the existing methods. A high power microwave filter is used to illustrate this technique. © 2021 Applied Computational Electromagnetics Society (ACES).

关键词： Multiphysics

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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Telecommunications Conference (ITC-Egypt), International

作者： Mark Emad Sobhi Abdelmalak Nima Khodadadi Ahmed Mohamed Zaki Marwa M. Eid Faris H. Rizk Abdelhameed Ibrahim Abdelaziz A. Abdelhamid Laith Abualigah El-Sayed M. El-kenawy Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Department of Civil and Architectural Engineering University of Miami Coral Gables FL USA Computer Science and Intelligent Systems Research Center Blacksburg Virginia USA Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Faculty of Engineering School of ICT Design and Information & Communications Technology (EDICT) Bahrain Polytechnic Isa Town Bahrain Department of Computer Science Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan MEU Research Unit Middle East University Amman Jordan Jadara Research Center Jadara University Irbid Jordan

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies.

关键词： Computer vision Analytical models Adaptation models Asphalt Roads Computational modeling Hardware

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IoT-based Smart Home Automation Systems for Energy Conservation

IoT-based Smart Home Automation Systems for Energy Conservat...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Sudhakar K N P.T. Devadarshini Ramu K N Deshveer N.V.S. Suryanarayana V Raviteja Kanakala Department of ISE CMR Institute of Technology Bengaluru Karnataka India Department of Artificial Intelligence and Data Science Easwari Engineering College Chennai Tamil Nadu India Department of Mechanical Engineering S.A. Engineering College Thiruverkadu Tamil Nadu India Department of Electrical Engineering Ganga Institute of Technology and Management Kablana Haryana India Administrative Officer Central Tribal University of Andhra Pradesh Vizianagaram India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India

This research study explores the numerous components of smart home automation systems, including actuators, sensors, and controllers, and the integration of these components with Internet of Things (IoT) technology. To demonstrate the system's ability to reduce energy consumption, this study involves an experimental analysis of a prototype system. In order to scientifically evaluate the system's performance, the test data must be statistically analysed. The results of this research study have the potential to contribute towards the enhancement of energy efficiency and sustainability of the proposed system by offering insights for the construction of sustainable residential settings. This study investigates the effects of the residential environments. The use of complex and innovative systems has the potential to significantly improve the efforts to save energy and promote a sustainable future.

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