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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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Drone Spray For Stem Base Rot Disease Detection In Oil Palm Plants

Drone Spray For Stem Base Rot Disease Detection In Oil Palm ...

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International Conference on Informatics and Computing (ICIC)

作者： Inna Novianty Gema Parasti Mindara Ade Astri Muliasari Faldiena Marcelita Lathifunnisa Fathonah Muhammad Achirul Nanda Computer Engineering Technology Program College of Vocational Studies IPB University Bogor Indonesia Technology and Management of Plantation Program College of Vocational Studies IPB University Bogor Indonesia Department of Agricultural and Biosystem Engineering Faculty of Agro-Industrial Technology Universitas Padjadjaran Sumedang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Early detection of stem base rot disease in oil palm is a considerable challenge in plantation management, given that the early symptoms of the disease are difficult to identify visually. This research develops a drone-based detection system using the Pi NoIR V2 camera and NIR image analysis, supported by the Mamdani fuzzy method for data interpretation. The dataset consists of 70 oil palm trees selected based on the variation of image conditions captured by the camera, the data taken for analysis at a height of 50 meters above the ground to produce a precise image. This research fills a gap in the automatic detection of stem base rot disease, with novelty in the integration of drone technology, NIR analysis, and fuzzy logic. NDVI and reflectance analysis of the captured images enabled classification of tree health, with results showing the difference between healthy and disease-indicated trees.

关键词： Fuzzy logic Oils Plantations Vegetation Sensor phenomena and characterization Cameras Reliability Sustainable development Diseases Drones

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Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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An IoT Based Smart Garbage Monitoring and Disposal Support System 5

An IoT Based Smart Garbage Monitoring and Disposal Support S...

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5th International Conference on Computing Methodologies and Communication, ICCMC 2021

作者： Vamsi, T.M.N. Kalyan Chakravarthi, G. Lanka, Pratibha Divakar, B. Dept of Computer Science and Engineering Engineering and Technology Program Visakhapatnam AP India Depts of Computer Science Visakhapatnam AP India Department of Computer Applications Visakhapatnam AP India

ISBN: (纸本)9781665403603

Today globally people designed different systems based on Internet of Things (IoT) principles and made human with smart living. In this article, an important application is proposed for our daily usage and is named as Smart Garbage Monitoring and Disposal Support System (SGMDSS). Since the advent of mobile communication and the spread of the Internet anywhere and anytime, there is a lot of scopes to control the devices from remote places. Solid garbage is one of the prior concerns today globally because improper execution of garbage removal from the villages, towns, and cities affects the people living there and it is the ultimate cause for new diseases. So, proper garbage management by using technology is a major concern of the hour. In the traditional existing system, the monitoring and disposal of garbage are done by humans and it is cumbersome, therefore supporting the traditional system with the flavor of IoT, then monitoring of garbage bins will be easy. This helps the people who are involved in the traditional garbage collection system and it will be the best feasible solution. The SGMDSS is a very innovative information management control system that helps the metros, cities, and villages hygiene and clean with a better garbage disposal. This system uses an advanced approach in which waste monitoring and disposal support are automated. SGMDSS monitors the garbage bins located at different locations and notifies about the level of garbage accumulated in the garbage bins through an android mobile application to the cleaning personnel for disposal and provides the shortest path to the garbage bin location that is almost filled. This information is also sent to the webpage and the entire data is stored and accessed through the cloud. Also, an alert message is sent to the worker. © 2021 IEEE.

关键词： Internet of things

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Evaluation of an artificial intelligence-based model in diagnosing periodontal radiographic bone loss

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Clinical Oral Investigations 2025年第4期29卷 1-8页

作者： Cassiano, Luanny de Brito Avelino da Silva, Jordão Paulino Cassiano Martins, Agnes Andrade Barbosa, Matheus Targino Rodrigues, Katryne Targino Barbosa, Ádylla Rominne Lima da Silva Gomes, Gabriela Ellen Maia, Paulo Raphael Leite de Oliveira, Patrícia Teixeira de Sousa Lopes, Maria Luiza Diniz da Silva, Ivanovitch Medeiros Dantas de Aquino Martins, Ana Rafaela Luz Department of Dentistry Federal University of Rio Grande do Norte - UFRN Rio Grande do Norte Natal Brazil Postgraduate Program in Electrical and Computer Engineering Center of Technology Rio Grande do Norte Natal Brazil Department of Dentistry Federal University of Rio Grande do Norte Av. Senador Salgado Filho 1787 Rio Grande do Norte Natal 59056-000 Brazil

Objective: To develop an artificial intelligence model based on convolutional neural network for detecting and measuring periodontal radiographic bone loss (RBL). Materials and methods: Keypoint annotations were carried out in 595 digital bitewing radiographic images using a computer Vision Annotation Tool. The dataset was splitted: 416 of these images were trained using the You Only Look Once version 8 architecture with pose estimation (YOLO-v8-pose), 119 images were destined for the validation set, and 60 images were used for the test set, resulting in a model capable of detecting keypoints related to the cementoenamel junction (CEJ) and alveolar bone crest (ABC). In order to evaluate the performance of the obtained model, the following metrics were analyzed: F1-Score, precision, sensitivity and mean average precision (mAP). Then, an algorithm was implemented to measure the RBL by calculating the Euclidean distance between CEJ and ABC. Results: The model achieved an F1-Score of 66,89%, precision of 61,1%, a sensitivity of 73,9% and an mAP of 73.8%. Conclusions: The developed model and its algorithm for identifying and measuring periodontal radiographic bone loss demonstrated promising performance, thereby presenting a potential tool for assisting in periodontal diagnosis. Further studies comparing the developed model with manual measurements performed by specialists are necessary for its validation. Clinical relevance: Applying artificial intelligence in clinical dental practice can support diagnosis, streamline clinical workflows, and inform treatment planning, representing a significant advancement in dental automation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Alveolar bone loss computer neural networks Deep learning Periodontitis Radiography

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International Electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis.

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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Design and Development of Low-cost Wearable Electroencephalograms (EEG) Headset

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Intelligent Automation & Soft Computing 2023年第3期35卷 2821-2835页

作者： Riaz Muhammad Ahmed Ali M.Abid Anwar Toufique Ahmed Soomro Omar AlShorman Adel Alshahrani Mahmoud Masadeh Ghulam Md Ashraf Naif H.Ali Muhammad Irfan Athanasios Alexiou Department of Electrical Engineering Sukkur IBA University65200SukkurPakistan Department of Electronic Engineering Quaid-e-Awam University of EngineeringScience and TechnologyLarkana77150Pakistan Faculty of Engineering and AlShrouk Trading Company Najran UniversityNajran61441Saudi Arabia Department of Medical Rehabilitation Sciences-Physiotherapy Program College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Computer Engineering Department Yarmouk UniversityIrbid21163Jordan Pre-Clinical Research Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Internal Medicine Medical CollegeNajran UniversityNajran61441Saudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia Novel Global Community Educational Foundation HebershamNSW 2770AustraliaAFNP Med AustriaWien2770Austria

Electroencephalogram(EEG)is a method of capturing the electrophy-siological signal of the *** EEG headset is a wearable device that records electrophysiological data from the *** paper presents the design and fab-rication of a customized low-cost Electroencephalogram(EEG)headset based on the open-source OpenBCI Ultracortex Mark IV *** electrode placement locations are modiﬁed under a 10–20 standard *** fabricated headset is then compared to commercially available headsets based on the following para-meters:affordability,accessibility,noise,signal quality,and ***,the data is recorded from 20 subjects who used the EEG Headset,and signals were ***,the participants marked the accuracy,set up time,participant comfort,and participant perceived ease of set-up on a scale of 1 to 7(7 being excellent).Thirdly,the self-designed EEG headband is used by 5 participants for slide *** raw EEG signal is decomposed into a series of band sig-nals using discrete wavelet transform(DWT).Lastly,theseﬁndings have been compared to previously reported *** concluded that when used for slide-changing control,our self-designed EEG headband had an accuracy of 82.0 *** also concluded from the results that our headset performed well on the cost-effectiveness scale,had a reduced setup time of 2±0.5 min(the short-est among all being compared),and demonstrated greater ease of use.

关键词： Brain-computer interface EEG consumer-grade accessibility headset

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Sentiment Analysis of Cooking Oil using Bidirectional Encoder Representations from Transformers 5

Sentiment Analysis of Cooking Oil using Bidirectional Encode...

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5th International Conference on Information and Communications technology, ICOIACT 2022

作者： Widianto, Mochammad Haldi Heryadi, Yaya Lukas Suparta, Wayan Wibowo, Antoni Bina Nusantara University Computer Science Department Binus Graduate Program - Doctor of Computer Science Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Departement of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (数字)9781665451406

ISBN: (纸本)9781665451406

Recently, cooking oil has been widely discussed on social media. Many people ask how to analyze this phenomenon, one of which is using a sentiment analysis application on Twitter that performs a scientific analysis of the tweets of this media user. This study aims to test the performance of the sentiment analysis algorithm utilizing the Bidirectional Encoder Representations from Transformers (BERT) algorithm from Twitter, which is used to classify the types of tweets processed in 2000 tweets. The study shows the results of the confusion matrix, accuracy, and precision. The study obtained a fairly good accuracy and precision performance of 84%, which occurred at a learning rate of 1e-6 and epoch 5. The author also discusses the factors that affect the performance of BERT, such as;pre-processing, data quantity, and data variation. © 2022 IEEE.

关键词： Sentiment analysis

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