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international Conference on trends in Electronics and informatics (iCEi)

作者： Govind Hanmantrao Balde Md. Abul Ala Walid srinivasan P Venkata Rajesh Yella M. soumya Ravi Rastogi Research and P.G. Department of Zoology N.T.V.S's G. T. Patil Arts Commerce and Science College Nandurbar M.S. India Department of Computer Science and Engineering Khulna University of Engineering and Technology (KUET) Khulna Bangladesh Department of ECE Sona college of Technology Salem Tamilnadu India Department of Biotechnology Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh INDIA Department of Computer Science & Artificial Intelligence SR University Warangal Telangana India Electronics Engineering NIELIT Gorakhpur Uttar Pradesh India

Cell density prediction can be defined as the process of predicting the number of cells in a given quantity of a culture or cell suspension. it is considered a common practice in cell biology since cell density had a significant impact on cell behavior and can be utilized for monitoring the health and growth of cell culture. Precise prediction of cell density was significant for a range of applications in cell biology., which includes bioprocessing, cell-based assays, and cell culture. therefore, this article develops a novel Cell Density Prediction design using Optimal Deep Learning with salp swarm Algorithm (CDP-ODLssA) technique. the presented CDP-ODLssA technique predicts the cell densities accurately on the images of cell suspensions or cultures. to do so, the presented CDP-ODLssA technique employs Long short term Memory-Autoencoder (LstM-AE) model for prediction of cell densities. in addition, the hyperparameter tuning of the LstM-AE model takes place by the use of salp swarm Algorithm (ssA). For experimental validation of the CDP-ODLssA technique, a wide range of simulations was taken place. the obtained values highlighted the superiority of the CDP-ODLssA technique compared to other approaches.

关键词： Deep learning Predictive models Prediction algorithms Market research Behavioral sciences informatics tuning

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Optimized Dilated Convolutional Neural Network with Quantum self-Attention Based on Hyper spectral Remote sensing image Classification

Optimized Dilated Convolutional Neural Network with Quantum ...

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international Conference on iCt in Business industry & Government (iCtBiG)

作者： t. santhosha H. Mohammed Ali seeniappan K Nagendar Yamsani R Maranan s. Kaliappan Department of Artificial Intelligence & Machine Learning Geetanjali College of Engineering and Technology T.S Department of Mechanical Engineering SRM Institute of Science and Technology Chennai Department of Mechanical Engineering Amity University Dubai United Arab Emirates School of Computer Science & Artificial Intelligence SR University Warangal Telangana Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Hyperspectral remote sensing (Rs) involves capturing and analyzing data across numerous, narrow spectral bands in the electromagnetic spectrum. With this technology, it is possible to identify what has been on the Earth surface with unprecedented depth that cannot be done with the mere multispectral images. A Hyperspectral sensor is able to capture hundreds of bands of data, and is therefore able to distinguish between very small differences in surface materials that we cannot even see or conventional multispectral sensors. Hyperspectral remote sensing is widely used in applications like agriculture, environmental monitoring, geology, and defence. to overcome the limitations of current approaches, new strategies are needed. in this manuscript Optimized Dilated Convolutional Neural Network with Quantum self-Attention Based on Hyper spectral Remote sensing image Classification (DCNN-QsA-PDOA) is proposed. the indian Pine dataset is where the data is first gathered. the multidirectional shearlet transform based feature extraction (MDst) model is then utilized to extract the hyperspectral image (Hsi) features. Following the completion of the feature extraction procedure, the Dilated Convolutional Neural Network with Quantum self-Attention (DCNN-QsA) is trained on the feature extracted images and used to classify the category information. Here, (PDOA) is utilized for tuning hyper parameters of DCNN-QsA. the proposed DCNN-QsA-PDOA method provides 37.23%, 18.74% and 24.84% higher accuracy compared with existing method like Classifying images from hyper spectral Rs using a DCNN-His, Dense residual three-dimensional convolutional neural networks are used for hyper spectral Rs picture classification (DR-3DCNN-His), Fully convolutional neural networks are used for the categorization of hyper spectral remote sensing pictures (CNN-His) respectively.

关键词： Accuracy transforms Feature extraction sensors Convolutional neural networks Data mining tuning Optimization Hyperspectral imaging image classification

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Exhaled Breath Analysis through the Lens of Molecular Communication: A survey

arXiv

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arXiv 2025年

作者： Bhattacharjee, sunasheer Bi, Dadi Hofmann, Pit Wietfeld, Alexander Becke, sophie Lommel, Michael Zhou, Pengjie Zheng, Ruifeng Kertzscher, Ulrich Deng, Yansha Kellerer, Wolfgang Fitzek, Frank H.P. Dressler, Falko School of Electrical Engineering and Computer Science TU Berlin Germany Department of Engineering King’s College London United Kingdom Deutsche Telekom Chair of Communication Networks Technische Universität Dresden Germany Dresden Germany Chair of Communication Networks Technical University of Munich Germany Institute of Computer-Assisted Cardiovascular Medicine Deutsches Herzzentrum der Charité Charité - Universitätsmedizin Berlin Germany

Molecular Communication (MC) has long been envisioned to enable an internet of Bio-Nano things (ioBNt) with medical applications, where nanomachines within the human body conduct monitoring, diagnosis, and therapy at micro- and nanoscale levels. MC involves information transfer via molecules and is supported by well-established theoretical models. However, practically achieving reliable, energy-efficient, and bio-compatible communication at these scales still remains a challenge. Air-Based Molecular Communication (ABMC) is a type of MC that operates over larger, meter-scale distances and extends even outside the human body. therefore, devices and techniques to realize ABMC are readily accessible, and associated use cases can be very promising in the near future. Exhaled breath analysis has previously been proposed. it provides a noninvasive approach for health monitoring, leveraging existing commercial sensor technologies and reducing deployment barriers. the breath contains a diverse range of molecules and particles that serve as biomarkers linked to various physiological and pathological conditions. the plethora of proven methods, models, and optimization approaches in MC enable macroscale breath analysis, treating human as the transmitter, the breath as the information carrier, and macroscale sensors as the receiver. Using ABMC to interface with the inherent dynamic networks of cells, tissues, and organs could create a novel internet of Bio things (ioBt), a preliminary macroscale stage of the ioBNt. this survey extensively reviews exhaled breath modeling and analysis through the lens of MC, offering insights into theoretical frameworks and practical implementations from ABMC, bringing the ioBt a step closer to real-world use. Copyright © 2025, the Authors. All rights reserved.

关键词： Medical nanotechnology

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Machine Learning Models for Brain signal Classification: A Focus on EEG Analysis in Epilepsy Cases

Machine Learning Models for Brain Signal Classification: A F...

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Cyber Resilience (iCCR), international Conference on

作者： Ali M Ali shadi Nashwan Ahmad Al-Qerem Ammar Almomani Mahmoud Al sakhnini Amjad Aldweesh Communications and Computer Engineering Department Faculty of Engineering AlAhliyya Amman University Amman Jordan Cybersecurity Department Faculty of Information Technology Middle East University (MEU) Amman Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan School of Computing Skyline University College Sharjah UAE I.T. department AlHuson University College AlBalqa Applied University Irbid Jordan College of Computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Electroencephalography (EEG) provides critical insights into brain function and neurological disorders. However, analyzing and classifying EEG signals remains challenging. Manual review is time-consuming, prone to bias, and lacks scalability. Hence, developing automated EEG classification has become vital for clinical assessment and treatment of epilepsy. this study presents a machine learning framework for classifying normal and epileptic EEG recordings. the approach involves feature extraction using discrete wavelet transform (DWt) and statistical methods, feature selection to identify the most discriminative attributes, followed by classification across multiple algorithms. specifically, DWt decomposes non-stationary signals for time-frequency representation. Principal component analysis and cosine similarity assist in selecting robust features. supervised classifiers including naive Bayes, decision trees, neural networks, k-nearest neighbors, random forest, and support vector machines categorize the signals. Results demonstrate 100% accuracy with neural networks, indicating highly reliable automated classification is achievable. By comparing multiple techniques, the optimal machine learning pipeline emerges. this epilepsy EEG classification framework demonstrates the potential for Ai to significantly improve screening, diagnosis, treatment, and management of neurological disorders. Ongoing research aims to further enhance efficiency, scalability, and real-time capabilities.

关键词： Neurological diseases Machine learning algorithms scalability Epilepsy transforms Feature extraction Electroencephalography

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A Development of 5G Wireless sensor Network using Man Design

A Development of 5G Wireless Sensor Network using Man Design

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Advance Computing and innovative technologies in engineering (iCACitE), international Conference on

作者： C. Vennila Muntather Almusawi K. sriram Kumar B Priya Vidhi Rawat G. Amirthayogam Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College TN Department of computers Techniques engineering College of technical engineering The Islamic University of Babylon Babylon Iraq Department of Information Technology Karpagam Institute of Technology TN Department of Computer Science and Business System Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electrical & Electronics Engg IES College of Technology Bhopal Madhya Pradesh India Department of Information Technology Hindustan Institute of Technology and Science (Deemed to Be University) Chennai Tamil Nadu India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

this article investigates the key drivers influencing the design of the forthcoming Metropolitan Area Network (MAN), focusing on the integration of two pivotal elements: Cloud Radio Access Network (C-RAN) for 5G implementation and the deployment of edge computing. As we delve into these aspects, the article systematically uncovers the crucial architectural components essential for effectively addressing the challenges inherent in implementing C-RAN and edge computing. through this strategic approach, the objective is to ensure a cost-effective solution that aligns with the specific requirements of these applications. the article aims to provide insights into the symbiotic relationship between CRAN and 5G, emphasizing their collaborative role in enhancing network efficiency. simultaneously, it underscores the significance of edge computing, highlighting its potential to optimize data processing and reduce latency within the evolving landscape of metropolitan networks. this comprehensive exploration contributes to a nuanced understanding of the future MAN design, grounded in innovative solutions and considerations for seamless integration.

关键词： symbiosis Wireless sensor networks 5G mobile communication shape Metropolitan area networks Urban areas switches

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Multimodal Feature Extraction for Memes sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

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2nd iEEE Conference on information technology and Data science, CitDs 2022

作者： Ouaari, sofiane tashu, tsegaye Misikir Horvath, tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

in this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. to make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. in this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 iEEE.

关键词： computer vision

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Multimodal E-Commerce Product Classification Using Hierarchical Fusion 2

Multimodal E-Commerce Product Classification Using Hierarchi...

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2nd iEEE Conference on information technology and Data science, CitDs 2022

作者： tashu, tsegaye Misikir Fattouh, sara Kiss, Peter Horvath, tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Faculty of Science Institute of Computer Science Pavol Jozef Šafárik University Jesenná5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

in this work, we present a multi-modal model for commercial product classification, that combines features extracted by multiple neural network models from textual (Camem-BERt and FlauBERt) and visual data (sE-ResNeXt-50), using simple fusion techniques. the proposed method significantly outperformed the performance of the unimodal models, as well as the reported performance of similar models on our specific task. We made experiments with multiple fusing techniques, and found, that the best preforming technique to combine the individual embedding of the unimodal network is based on the combination of concatenation and averaging the feature vectors. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label and multimodal classification problems. © 2022 iEEE.

关键词： Neural network models

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An Advanced Cognitive strategy for Drug Formulation Utilizing Deep Neural Networks and Feature-based Fusion

Research Square

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Research square 2025年

作者： singh, Jagendra Pal, Yogesh suryavanshi, Madhuri Jailani, s. Amer, A.A. beer Kumar, Rakesh Bennett University Greater Noida India Pimpri Chinchwad College of Engineering Pune India Alpha Pharma KAEC Rabigh Saudi Arabia Sadat Academy for Management and Sciences Canob Street Alexandria Egypt Department of Computer Science and I.T. Magadh University Bodh Gaya India

this research introduces an innovative approach to drug formulation optimization by integrating a deep neural network (DNN) model with feature-based fusion. the study leverages diverse datasets, including molecular databases, biological response datasets, and pharmacokinetic information, to develop a holistic understanding of the intricate factors influencing drug behavior within biological systems. the DNN model, chosen for its capacity to handle high-dimensional data and capture complex relationships, was complemented by feature-based fusion to enhance the cognitive strategy for drug formulation. the quantitative evaluations of the models, conducted over 10 trials, yielded promising results. the DNN model demonstrated commendable performance with an average accuracy of 91.8%, precision of 89.2%, recall of 93.5%, and F1-score of 91.3%. However, the Feature-based Fusion approach consistently outperformed the DNN, achieving an average accuracy of 93.5%, precision of 91.7%, recall of 94.6%, and F1-score of 92.8%. these results highlight the superiority of the Feature-based Fusion approach in optimizing drug formulations, showcasing higher quantitative metrics and a more balanced trade-off between precision and recall. this research advances the field of drug formulation by providing a robust framework that integrates advanced cognitive strategies, contributing to more efficient and personalized therapeutic interventions. © 2025, CC BY.

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Augmented Ensemble Learning Model for Biomarkers Prioritization to Enhance Disease identification Efficiency

Augmented Ensemble Learning Model for Biomarkers Prioritizat...

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international Conference on information systems and computer Networks (isCON)

作者： Dr. K. Vaishali saroj shambharkar Rachna K. somkunwar Roshan R. Kolte Jyothishmathi Institute of Technology and Science Karimnagar India Department of Information Technology K.I.T.S. Ramtek India Department of Computer Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune India Information Technology K.D. K. College of Engineering Nagpur India

Biomarkers assist in identification of various diseases present in biological beings. these markers include Lymphatic Vessel Endothelial Hyaluronan Receptor (LVYEi), Regenerating Family Member 1 Beta (REGiB), trefoil Factor 1 (tFF1), Regenerating Family Member 1 Alpha (REGiA), etc. Each of these markers assists in estimation of presence of a particular protein type, which in turn assists in improving disease detection capabilities. Biomarkers are currently used to identify multiple diseases including Alzheimer’s disease, pancreatic cancer, posttraumatic stress disorder (PtsD), breast cancer, brain cancer, etc. Algorithms designed to evaluate the presence of these diseases work via prioritization of biomarkers, which assists in evaluating their genomic strength. the currently proposed models for biomarker prioritization work on single type of disease, which limits their scalability and performance when applied to real time clinical applications. Moreover, these models require large training & validation delays, which further reduces their applicability. in order to reduce the delay needed for biomarker prioritization and improve its scalability, this paper proposes design of an augmented ensemble learning model to enhance disease identification efficiency. the model is applied to multiple disease types including Alzheimer’s disease, pancreatic cancer, brain cancer and breast cancer. Results indicate that the proposed model has high accuracy, and reduced delay when compared with existing state-of-the-art approaches. this is mainly due to fusion of GoogLeNet and inception net models, which assist in optimizing feature variance, thereby improving efficiency of prioritization. the proposed model is observed to have an average accuracy of 91%, with high precision & recall performance across multiple datasets; thereby it can be facilitating its use for real time applications.

关键词： Biological system modeling Pancreatic cancer Biomarkers Brain modeling Feature extraction Breast cancer Delays

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Design and Development of 6G in the X subnetworks with Wireless Communications

Design and Development of 6G in the X Subnetworks with Wirel...

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Advance Computing and innovative technologies in engineering (iCACitE), international Conference on

作者： A Reena Gyanendra Kumar Pandey Yogendra Kumar R. sundar L A Jeni Narayanan R. sreelakshmy Department of Informations Technology Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical & Electronics Engineering (EEE) IES Institute of Technology and Management IES University Bhopal M. P. India Department of Electrical Engineering (EE) GLA University Mathura Department of Electronics and Communication Engineering (E C & E) Karpagam College of Engineering Tn. Department of Computer Science and Engineering (C S & E) Nehru Institute of Engineering and Technology T.M.Palayam Tamil Nadu India Department of Electronics and Communication Engineering (E C & E) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

in this study, we suggest short-range, lightweight 6th subsequent generations (6 G) in-X network parts as a valuable radio concept responding to the demanding connectivity requirements of new applications like wearable control of arms equipped with robotics and critical on-body gadgets which include wireless heart pacemakers. the main requirements of ultra-high fidelity (exceeding 6 nines when) and sub-millisecond delay at all spatiotemporal contexts demand powerful radio systems contrary to fading and interference. Our study thoroughly explores technology drivers and methods for interference reduction in 6 G in-X subsidiary networks. the proposed methods include blind-repetition at pseudo-random frequency shifts and environment-aware tools for interference control through dynamic channel sharing. Additionally, we suggest two novel enhancements: real-time repeat order adaptation using current channel dynamics and preemptive packet duplication, during which subnetworks that are duplicate signals on a third channel group upon spotting possible nearby subnetwork interference. simulations in an industrial workplace setting with mobile in-X subnetworks, by employing 3GPP-defined models, show the need for large internet connectivity, packet repeats, and environment-aware interference synchronization to achieve low packet loss percentages and sub- $100 \mu$ s delay. the suggested improvements show large packet loss rate decrease for systems with life times $\gt 2$ cycle times.

关键词： Wireless communication 6G mobile communication Adaptation models Robot kinematics Packet loss interference Delays

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