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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Mehta, Khushi Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

In this rapidly changing era of the world, healthcare devices are revolutionizing, which enables the continuous measurement of crucial biological markers for diagnostic purposes, physiological health monitoring, and evaluation. Hence, wearable devices have transformed into accessories, embedded clothing, body adaptors, and body implants. Additionally, the emergence of wearable healthcare devices that facilitates the diagnosis and prognosis by using tiny sensing devices and biomedical devices has greatly enhanced the efficiency as well as the quality of medical care facilities results in the tremendous advancements in semiconductor technology, biomedical technologies, and nano-sized materials over the past few decades. Meanwhile, the devices could provide real-time feedback, allowing users to analyze their health and monitor in a specific period that has been assigned to the smart wearable devices, even though the industries and manufacturers are intensely interested in interpreting the factors that influence the adoption of new smart technologies, which helps to improve the performance and viability of wearable devices to fascinate consumers. Hence, the advancements in technology are regarded as trustworthy equipment for everlasting monitoring of health that are frequently used to monitor and control a diverse of health tracking predictors in the surroundings, including monitoring health and wellness of the body. In recent years, it has been focused on the study of a wide range of parameters closer to advancements in smart technology. As a result, these smart devices are now utilized in a variety of healthcare monitoring applications, one of the most crucial aspects of data collection is the various factors based on bodily activities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electronic health record

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Artificial Intelligence as Automated Technology for Prediction of Breast Cancer 3rd

Artificial Intelligence as Automated Technology for Predicti...

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3rd International Conference on computational Electronics for Wireless Communications, ICCWC 2023

作者： Chauhan, Ritu Kaur, Harleen Choudhary, Tisya Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India Amity Institute of Biotechnology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819719426

Artificial intelligence techniques are utilized in cancer research and oncology which involves medical data in the detection of cancer, subtype classification, and optimization of cancer treatment. This paper discusses artificial intelligence techniques, such as K-means cluster analysis and its data interpretation based on breast cancer diagnostic dataset. This study focuses on the database of digital images of fine needle aspirate (FNA) of breast mass which shows abnormal lump, and a clustering technique was applied on this dataset. This study represents various descriptive statistics techniques of the breast cancer diagnostic dataset. Furthermore, we have also tried to show a variety of techniques of descriptive statistics like KMO and Bartlett’s test, total variance representing initial eigenvalues and extraction sum of squared loadings, scree plots, and clustering techniques like ANOVA table, initial and final cluster centers, and the distance between initial and final cluster centers of K-means cluster analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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Multiclass Classification of EEG Motor Imagery Signals Based on Transfer Learning 8

Multiclass Classification of EEG Motor Imagery Signals Based...

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8th International Conference on Applied System Innovation, ICASI 2022

作者： Chen, Chun-Yu Wang, Wei-Jen Chen, Chun-Chuan National Central University Laboratory of Computational Neuroscience Department of Biomedical Sciences and Engineering Taoyuan Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan

ISBN: (纸本)9781665496506

Multiclass Classification of EEG signal is essential for brain computer interface (BCI) applications but extremely time consuming. We proposed a subject-weighted adaptive transfer learning method in conjunction with MLP and CNN classifiers for fast classification of multiclass EEG *** results show that CNN generally outperforms MLP in this multi-class classification. The use of transfer learning is efficient for building the predictive model without decreasing the accuracy and 2D CNN is more robust to between-subject variabilities. © 2022 IEEE.

关键词： Deep learning

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Medical Personal Protective Equipment Detection Using YOLOV7

Medical Personal Protective Equipment Detection Using YOLOV7

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Ali, Md. Anas Haque, Mahmudul Alam, Saadia Binte Rahman, Rashedur Amin, Mashraful Kobashi, Syoji Graduate School of Engineering University of Hyogo Himeji Hyogo Japan Independent University Department of Computer Science and Engineering Bangladesh Dhaka1299 Bangladesh Independent University Center for Computational & Data Sciences Bangladesh Dhaka1299 Bangladesh

ISBN: (纸本)9798350303780

In recent years, healthcare and safety have been a major focus of deep learning research. This paper focuses on the detection of Medical Personal Protective Equipment (MPPE) in the health-care sector using YOLOv7. Improper use of personal protective equipment (PPE) can result in the contamination and cross-contamination of infectious diseases, so it is crucial for healthcare professionals to use it correctly. The CPPE-5 dataset was used to train the model, which contains 1029 high-quality images divided into five categories: coveralls, face shield, gloves, masks, and goggles. The objective of this research is to create an accurate model for future applications and development using a suitable medical PPE dataset. The proposed model outperforms previous studies, with an optimal mAP of 90.93 %, indicating that it is a promising method for detecting MPPE in the healthcare sector. © 2023 IEEE.

关键词： Object detection

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Matching individual Ladoga ringed seals across short-term image sequences (vol 102, pg 957, 2022)

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MAMMALIAN BIOLOGY 2022年第3期102卷 1045-1045页

作者： Nepovinnykh, E. Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering School of Engineering Science Lappeenranta-Lahti University of Technology LUT P.O.Box 20 53851 Lappeenranta Finland Department of Artificial Intelligence Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University Polytechnicheskaya 29 Saint Petersburg Russian Federation 195251 Department of Computer Science and Computational Experiment Southern Federal University Rostov-on-Don Russian Federation 344006 Interregional Charitable Public Organization “Biologists for Nature Conservation” (BFNC) 24 line 3-7 Saint Petersburg Russian Federation 199106

Automated wildlife reidentification has attracted increasing attention in recent years as it provides a non-invasive tool to identify and to track individual wild animals over time. In this paper, the first steps are taken towards the automatic photo-identification of the Ladoga ringed seals (Pusa hispida ladogensis). A method is proposed that takes a sequence of images, each containing multiple individuals as the input, and produces cropped images of seals grouped based on one certain individual per group. The method starts by detecting each seal from the images and proceeds to matching the individual seals between the images. It is shown that high grouping accuracy can be obtained with a general-purpose image retrieval method on an image sequence taken from the same location within a relatively short period of time. Each resulting group contains multiple images of one individual with slightly different variations, for example, in pose and illumination. Utilizing these images simultaneously provides more information for the individual re-identification compared to the traditional approach, i.e., which utilizes just one image at a time. It is further demonstrated that a convolutional neural network based method can be used to extract the unique pelage patterns of the seals despite the low contrast. Finally, a method is proposed and experiments with the novel Ladoga ringed seals data are carried out to provide a proof-of-concept for the individual re-identification.

关键词： Animal re-identification Convolutional neural networks Instance segmentation Ladoga ringed seal Photo-identification

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Machine Learning based Secure Data Transmission and Improvement in MANET through Internet of Things (IoT)

Machine Learning based Secure Data Transmission and Improvem...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Dinesh, G. Pawar, Yogesh Shivaji Dinshi, P. Soundararajan, S. Husain, S. Syed Reddy, Ch. Venkata Krishna Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Tamil Nadu Chennai India Bhabha University Department of Electrical Engineering Madhya Pradesh Bhopal India Koneru Lakshmaiah Educational Foundation Department of Computer Science Engineering Andhra Pradesh Guntur India Velammal Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India K. Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Tiruchirappalli India Chaitanya Bharathi Institute of Technology Department of Electrical and Electronics Engineering Telangana Hyderabad India

ISBN: (纸本)9781665474511

The term MANET stands for Mobile Ad hoc Network. MANET is a structure of wireless communication network that is combined with various nodes that are movable in various directions in the network. They are accompanied with the absence of infrastructure which helps the nodes to interconnect with the neighboring one in a random fashion. They are highly dynamic in nature. The internet of things device have the ability to communicate with the various heterogeneous networks. These IoT nodes helps to communicate with each other without the need of infrastructure. The cloud helps to store the data and tends to transmit through the nodes in the network. The secure data transmission includes the intrusion detection system, neglecting malicious attacks and selfish nodes in the network. The secure data transmission system without any constraints helps in the improvement of overall efficiency of the system. This is done through the machine learning accompanied with genetic algorithm. These biologically inspired algorithms helps to obtain exact optimum solutions helps to provide secure data transmission. © 2023 IEEE.

关键词： Internet of things

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Neural networks built from enzymatic reactions can operate as linear and nonlinear classifiers 63

Neural networks built from enzymatic reactions can operate a...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Samaniego, Christian Cuba Wallace, Emily Blanchini, Franco Franco, Elisa Giordano, Giulia Carnegie Mellon University Department of Computational Biology PittsburghPA United States Michigan University Ann ArborMI United States University of Udine Department of Mathematics Computer Science and Physics Italy University of California Department of Mechanical and Aerospace Engineering Los AngelesCA United States University of Trento Department of Industrial Engineering Italy

ISBN: (纸本)9798350316339

The engineering of molecular programs capable of processing patterns of multi-input biomarkers holds great potential in applications ranging from in vitro diagnostics (e.g., viral detection, including COVID-19) to therapeutic interventions (e.g., discriminating cancer cells from normal cells). For this reason, mechanisms to design molecular networks for pattern recognition are highly sought after. In this work, we explore how enzymatic networks can be used for both linear and nonlinear classification tasks. By leveraging steady-state analysis and showing global stability, we demonstrate that these networks can function as molecular perceptrons, fundamental units of artificial neural networks - capable of processing multiple inputs associated with positive and negative weights to achieve linear classification. Furthermore, by composing orthogonal enzymatic reactions, we show that multi-layer networks can be constructed to achieve nonlinear classification. © 2024 IEEE.

关键词： Cell engineering

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Fine- Tuning Pre-trained Models for Aspect-Based Sentiment Analysis of Amazon Food Reviews

Fine- Tuning Pre-trained Models for Aspect-Based Sentiment A...

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2025 International Conference on computer, Electrical and Communication engineering, ICCECE 2025

作者： Sridhar, D. Manikandan, K.B. Padma Priya, R.S. Pradeepa, D. Vasumathi, B. Shankar, R. Alliane University Alliance College of Engineering and Design Central Campus Karnataka Bangalore India Hyderabad India Vignan's Foundation for Science Technology and Research Department of Information Technology and Computer Applications Guntur India Reva University School of Computer Science and Applications Bangalore India Brainware University Department of Computational Sciences West Bengal India Bharathiar University Research in Commerce Coimbatore India

ISBN: (纸本)9798331513894

The process of classifying consumer responses to a particular product review as neutral, negative, or positive is known as sentiment analysis. Product owners can utilize sentiment analysis for assistance in them decide whether to continue producing their product, increase production, or eliminate production altogether. As a result, they will gain a deeper comprehension of how customers feel about their products. The method used to determine the opinions expressed in the review regarding a specific feature or aspect of the product is called Aspect Based Sentiment Analysis, or ABSA. Numerous machine learning and natural language processing models are currently available for ABSA. The inability to precisely identify and categories the views expressed towards particular features is a major drawback of the aspect-based sentiment analysis (ABSA) techniques presently in usage for Amazon food assessment tasks. The Hybrid Contextual Aspect-Based Sentiment Analyzer (HCABSA), a hybridized framework for aspect-based sentiment analysis, is developed in this research to accurately assess the sentiment of Amazon food reviews through a number of processes. This research starts by collecting dataset for Amazon food reviews from Kaggle, preprocessing the data to eliminate noise and irrelevant information, and then using POS tagging to identify the specific aspects and sentiment words related to those aspects, which are nouns and adjectives in the review using the Lexicon based approach. In the following stage, the frequency-based candidate extraction method is utilized to pick the characteristics that are used to determine the product's internal and exterior features. The raw data is then transformed into features that the machine learning models can access using the Bag of Words feature extraction process. The Gradient Boosting Decision Tree technique is then used to categories the sentiment as either positive or negative. Using performance metrics like accuracy, precision, recall,

关键词： Decision trees

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An Energy-Efficient Multi-swarm Optimization in Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第5期36卷 1571-1583页

作者： Reem Alkanhel Kalaiselvi Chinnathambi C.Thilagavathi Mohamed Abouhawwash Mona A.Al duailij Manal Abdullah Alohali Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electronics and Communication Engineering Kongu Engineering CollegePerundurai638060India Department of Information Technology M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast LansingMI48824USA Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Wireless Sensor Networks are a group of sensors with inadequate power sources that are installed in a particular region to gather information from the *** energy-efﬁcient data gathering methods in large-scale Wireless Sensor Networks(WSN)is one of the most difﬁcult areas of *** every sensor node has aﬁnite amount of *** power is the most signiﬁcant source in the *** is a well-known technique for enhan-cing the power feature in *** the proposed method multi-Swarm optimiza-tion based on a Genetic Algorithm and Adaptive Hierarchical clustering-based routing protocol are used for enhancing the network’s lifespan and routing *** using distributed data transmission modiﬁcation,an adaptive hier-archical clustering-based routing algorithm for power consumption is presented to ensure continuous coverage of the entire *** begin,a hierarchical cluster-ing-based routing protocol is presented in terms of balancing node energy *** Multi-Swarm optimization(MSO)based Genetic Algorithms are proposed to select an efﬁcient Cluster Head(CH).It also improves the network’s longevity and optimizes the *** a result of the study’sﬁndings,the pro-posed MSO-Genetic Algorithm with Hill climbing(GAHC)is effective,as it increases the number of clusters created,average energy expended,lifespan com-putation reduces average packet loss,and end-to-end delay.

关键词： Clustering energy consumption genetic algorithm multi swarm optimization adaptive hierarchical clustering routing cluster head

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An Automatic Deep Neural Network Model for Fingerprint Classification

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Intelligent Automation & Soft Computing 2023年第5期36卷 2007-2023页

作者： Amira Tarek Mahmoud Wael AAwad Gamal Behery Mohamed Abouhawwash Mehedi Masud Hanan Aljuaid Ahmed Ismail Ebada Department of Computer Science Faculty of SciencePort Said UniversityPort SaidEgypt Department of Computer Science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI 48824USA Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)P.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaEgypt

The accuracy ofﬁngerprint recognition model is extremely important due to its usage in forensic and securityﬁ***ﬁngerprint recognition system has particular network architecture whereas many other networks achieve higher *** solve this problem in a uniﬁed model,this paper proposes a model that can automatically specify ***,it is called an automatic deep neural net-work(ADNN).Our algorithm can specify the appropriate architecture of the neur-al network used and some signiﬁcant parameters of this *** parameters are the number ofﬁlters,epochs,and *** guarantees the high-est accuracy by updating itself until achieving 99%accuracy then it stops and out-puts the ***,this paper proposes an end-to-end methodology for recognizing a person’s identity from the inputﬁngerprint image based on a resi-dual convolutional neural *** is a complete system and is fully automated whether in the features extraction stage or the classiﬁcation *** goal is to automate thisﬁngerprint recognition system because the more automatic the sys-tem is,the more time and effort it *** model also allows users to react by inputting the initial values of these ***,the model updates itself until itﬁnds the optimal values for the parameters and achieves the best *** advantage of our algorithm is that it can recognize people from their thumb and otherﬁngers and its ability to recognize distorted *** algo-rithm achieved 99.75%accuracy on the publicﬁngerprint dataset(SOCOFing).This is the best accuracy compared with other models.

关键词： Automatic system ﬁngerprint classiﬁcation residual networks deep learning

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