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An Optimized Ant Gardient Convolutional Neural Network for Disease Detection in Apple Leaves

An Optimized Ant Gardient Convolutional Neural Network for D...

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Innovation in Technology (INOCON), ieee international conference for

作者： Arshleen Kaur Raman Chadha Computer Science and Engineering Chandigarh University Gharuan Mohali (Punjab) India

This article implements a new method of classification or detection of the diseased section in apple leaf images. Apple leaf disease segmentation and classification is the main processing phase for ROI extraction in agriculture-based IP (image processing). Generally, in the case of live images, it is also crucial to separate the apple leaf images from the background. A frequent experiment in processing leaf images is the automated separation of apple leaves from the background. In the research work, ALS (Alternaria leaf spot), BS (brown spot), Mosaic, etc., are five normal categories of ALD (apple leaf diseases) that rigorously affect apple production. Though the previous work lacks accuracy and faster detection of apple diseases to confirm the healthy growth of apple production, it implements a DL method based on the CNN classifier for detecting apple leaf diseases. The OAGCNN detection model is proposed, which combines an ant colony optimizer and stochastic gradient descent method with the CNN model. The simulation outcomes define the OAGCNN model performance of 98.5 per cent on the ALDD (apple leaf disease database), with a maximum detection time of 2 seconds. The outcomes validate that the OA-CNN classifier model gives maximum performance for the early-stage detection of ALLD and may execute real-time detection of these diseases with maximum accuracy rate and detection complexity than existing techniques such as Vgg-Icep and multi-label classifier methods.

关键词： Technological innovation image segmentation Databases Stochastic processes Production Real-time systems Complexity theory

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Vision Transformers(ViT) Pretraining on 3D ABUS image and Dual-CapsViT: Enhancing ViT Decoding via Dual-Channel Dynamic Routing

Vision Transformers(ViT) Pretraining on 3D ABUS Image and Du...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingwang Xu Wei Wang Kuanquan Wang Suyu Dong Pengzhong Sun Jinwei Sun Gongning Luo Faculty of Computing Harbin Institute of Technology Harbin China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer and Control Engineering Northeast Forestry University Harbin China School of Instrumentation Science and Engineering Harbin Institute of Technology Harbin China

Breast cancer continues to be a pressing global health concern, emphasizing the essential need for effective diagnostic techniques. Automated Breast Ultrasound Systems (ABUS) provide a promising advance in breast tumor detection, yet they require significant expertise in interpreting 3D ABUS images, a task fraught with distinctive challenges. Although Vision Transformers (ViT) display remarkable potential for image processing, their low inductive bias and significant data requirements pose obstacles, particularly in the data-constrained medical field. To mitigate these issues, we introduce a Mask-Recover strategy for pretraining Transformer models on 3D ABUS images, enhancing model adaptability and reducing the data demands of the ViT model. Moreover, recognizing the risk that ViTs’ average pooling approach may unintentionally mask small but vital features, we propose Dual-CapsViT, an inventive model combining Transformers and Capsule Networks. This integration affords efficient token routing while preserving fine-grained details. To reconcile potential inconsistencies between capsules and tokens, we engineer a novel dual-channel routing algorithm, strengthening the decoder’s performance. We benchmarked our models against well-known standards such as ResNet and ViT for classifying breast tumors in ABUS images. Our models exhibited superior performance, as evidenced by improved accuracy, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC) metrics, thereby affirming Dual-CapsViT’s potential to enhance breast cancer diagnostics.

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AI Integration in IoT: A Comprehensive Overview of applications and Implications

AI Integration in IoT: A Comprehensive Overview of Applicati...

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Measurement, Instrumentation, Control and Automation (ICMICA), 2020 First ieee international conference on

作者： Shivam Hans Raman Kumar Saloni Manhas Arti Badhoutiya Ashima Juyal Ahmed Alkhayyat Department of CSE Chandigarh University Mohali India Department of Mechanical Engineering Rayat Bahra University Mohali India Department of Computer Applications Chandigarh Engineering College Chandigarh Group of Colleges Mohali India Department of Electrical Engineering GLA University Mathura India Department of ECE Uttaranchal Institute of Technology Uttaranchal University Dehradun India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350355048

ISBN: (纸本)9798350355055

The complex relationship between AI and the IoT is explored in this study paper, which looks at the two technologies side by side and how they may work together. The study delves into several AI-enhanced IoT applications, such as machine learning, deep learning, and natural language processing, and how these integrate to enhance device and system capabilities. Information transmitted via Data sent and received from workstations, as well as data collected by sensors, will soon be available on the internet. Data storage, latency, channel constraint, and network congestion are all potential issues that may arise as the number of workstations and sensors continues to rise. This study explores the revolutionary effects of AI on several sectors of the Internet of Things (IoT) ecosystem by doing a thorough literature review and analysing real-world use cases. By discussing the pros and cons of AI integration into the IoT, the study provides useful information for academics, practitioners, and policymakers. Ultimately, the goal of this extensive investigation is to add to the current conversation about the changing world of AI-enabled IoT, therefore paving the way for new developments in this exciting and fast-paced industry.

关键词： Industries Instruments Neural networks Ecosystems Memory Oral communication Natural language processing Workstations Sensors Internet of Things

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An Exploration of the Effectiveness of Machine Learning Algorithms for Text Classification

An Exploration of the Effectiveness of Machine Learning Algo...

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Paradigm Shift in Information Technologies with Innovative applications in Global Scenario (ICPSITIAGS), ieee international conference on

作者： Ankita Agarwal Aravindan M K Manish Shrivastava S. Sathya Devika A. Verma Jagmeet Sohal Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai - 127 Department of Computer Engineering Centre of Vishwakarma Institute of Information Technology Pune India Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Text type is an important project in natural language processing (NLP), with programs in facts retrieval, sentiment evaluation, and report categorization. With the exponential increase of virtual information, the call for automatic text category has multiplied. Conventional text category techniques along with rule-based totally structures and statistical methods have obstacles in managing complex and massive datasets. System studying (ML) has emerged as a promising technique to text type, leveraging algorithms which can automatically learn from information and make correct predictions. This paper gives an exploration of the effectiveness of different machine getting to know algorithms for text category responsibilities. We evaluate and compare the performance of famous algorithms consisting of decision bushes, aid vector machines (SVM), ok-nearest pals (KNN), and deep learning fashions which include convolutional neural networks (CNN) and recurrent neural networks (RNN).

关键词： Support vector machines Machine learning algorithms Recurrent neural networks Statistical analysis Text categorization Prediction algorithms Natural language processing

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Duck Egg Setter System with Sorting Process Using image processing

Duck Egg Setter System with Sorting Process Using Image Proc...

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international conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Ryan C. Reyes Roel M. Mendoza Rovenson V. Sevilla John Gelvic C. Calagday Renielle Carl C. General Samantha Joynna J. Leyco Jose Noel C. Lozano Kristine R. Monteser Edmon O. Fernandez Jennifer C. Dela Cruz Electronics Engineering Department Technological University of the Philippines Manila Electronics and Computer Engineering School of Electrical Mapua University Manila Philippines

ISBN: (数字)9798350381177

ISBN: (纸本)9798350381184

The project focused on the development of a machine that automates the duck egg incubation and sorting process with the utilization of image processing for the balut and penoy production industries in the Philippine Agricultural Sector. The study attempted to mitigate the challenges and problems faced in the traditional setup of duck egg-incubated production systems. The project involved the fabrication of a machine that is capable of moving eggs through three different processes, specifically incubation, candling, and sorting, by means of motor mechanisms, computer programming, and image processing. The incubation stage utilized a ceramic bulb setup as a source of heat, and a pre-programmed microcontroller allowed a consistent temperature in the egg incubator cabin by activating an exhaust fan, while the system was able to maintain a stable humidity ranging from 50% to 80% and a temperature varying from 36°C to 38°C. The candling stage was equipped with a vertically moving camera surface linked with an image processing threshold software program to detect the maturity of the embryo inside the eggs. After several trials, test results showed 66.67% to 91.67% accuracy in correctly determining the correct egg maturity stage in the candling stage, and 91.67% of all processed eggs have not incurred any damage caused by movement from the conveyors and sorting mechanisms.

关键词： Temperature distribution Technological innovation image processing Prototypes Process control Programming Software

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Advanced Learning-Based Segmentation of Liver and Tumor 3D images for Early Disease Diagnosis

Advanced Learning-Based Segmentation of Liver and Tumor 3D I...

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ieee international conference on Research, Innovation and Vision for the Future

作者： Manh-Tien Nguyen Thai Dinh Kim Manh-Hung Ha Anh-Luyen Do Lan-Anh Nguyen Dieu-Linh Ngo International School Vietnam National University Hanoi Vietnam

Accurate liver and tumor image segmentation is crucial for medical applications including liver functional assessment, disease diagnosis, treatment planning, and computer-aided diagnosis for liver cancer. This study introduces a novel approach for transforming 3D CT scans to segment liver tumors. We propose a novel deep learning technique utilizing convolutional neural networks (CNN) and an Unet-based architecture for liver tumor segmentation. Our suggested model achieves state-of-the-art performance on the publicly available Liver Tumor Segmen-tation (LiTS) challenge dataset, demonstrating its effectiveness in precisely segmenting liver tumors. We evaluate different models and report their performance using dice coefficient scores. The results show that the proposed model Unet-3D has the highest accuracy, with dice scores for liver and liver tumors being 0.8684 and 0.4821, respectively.

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CalorieMe: An image-based Calorie Estimator System

CalorieMe: An Image-based Calorie Estimator System

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ieee international conference on Intelligent Computing and Information Systems (ICICIS)

作者： Bavlly Magid Mohamed Ibrahim Yomna A. Kawashti Mazen Mohamed Mohamed Sabry Hanan Hindy Mazen Khaled Waleed Mohamed Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

In recent years, with the growing interest in healthy eating, various food photo recognition applications for tracking meals have emerged. However, some of these applications still require human intervention for calorie estimation, such as manual input or consultation with a nutrition expert. Furthermore, even automated systems often have limitations in food category recognition, or they demand multiple viewpoints for accurate results. Meanwhile, advancements in image recognition have been substantial, thanks to the advent of Convolutional Neural Networks (CNN). CNNs have significantly improved the accuracy of various image recognition tasks, including classification and object detection. This paper presents a comprehensive solution for estimating the calories in food photos containing multiple ingredients. The proposed method employs two deep learning models: one to detect ingredients and their respective locations, and another to segment the ingredients and measure their portion sizes. Moreover, the proposed method incorporates a reference object to enhance the precision of portion size measurement. Finally, the proposed model compares the food type and portion size against a dataset of food types and their corresponding calorific values per standard serving, thus estimating the total calorie count. In this study, the two-model methodology resulted in a 7% improvement in pixel accuracy and a 23% improvement in mean Intersection Over Union (mIOU) for recognition and segmentation tasks, respectively, compared to the latest state-of-the-art approach, which employed Deeplabv3+ exclusively on the same dataset.

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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ieee international conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongshuai Wang Xiaojun Cai Defeng Li Shiwei Sun Cheng Chen Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features.

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Detection and Location of Litchi Fruit based on Object Detector and Depth image

Detection and Location of Litchi Fruit based on Object Detec...

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Advances in Electrical Engineering and computer applications( AEECA), 2020 ieee international conference on

作者： Liang Mao Zhishang Liang Yinqiao Peng Ji Wang Linlin Wang Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China Research Center of Guangdong Smart Oceans Sensor Networks and Equipment Engineering Zhanjiang China School of Artificial Intelligence Shenzhen Polytechnic University Shenzhen China

computer vision is the core of harvesting robots, and computer vision-based litchi fruit detection is an important direction for research on automated harvesting. However, the current detection methods of litchi fruit cannot meet the detection requirements in the complex orchard environment and cannot simultaneously detect a single litchi fruit and calculate the spatial location of the fruit. In this paper, a detection and location method based on object detector and depth images was proposed in this paper for litchi fruit in an orchard environment. After comparing the performance of multiple object detectors, YOLOv5 was chosen as the base model for litchi detection. An attention function and an improved loss function are added to YOLOv5. Meanwhile, to transmit more shallow features to deep layers, the transmission path of the neck was adjusted. The experimental results show that the mAP and F1 scores of the improved YOLOv5 are 0.9534 and 0.9428, which are higher than the corresponding performance values of YOLOv5, and the improved YOLOv5 can overcome the influence of various factors on the detection performance in practical detection scenarios. The method was tested in the orchard environment Intel RealSense D435 was used to collect RGB images and depth images of litchi during the test. The improved YOLOv5 detected litchi fruits in RGB images, the values of their corresponding positions in depth images were read. The experimental results show that the proposed method has high accuracy and strong robustness.

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3D image Reconstruction and processing for Augmented and Virtual Reality applications: A computer Generated Environment

3D Image Reconstruction and Processing for Augmented and Vir...

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Communication, Security and Artificial Intelligence (ICCSAI), international conference on

作者： Radhika Baskar P. Muthu Krishnammal Anurag Aeron Syed Sumera Ali T. Thomas Leonid M R Arun Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh India Computer Science and Engineering School of Computing DIT University Dehradun Electronics and Computer Engineering Department CSMSS Chh Shahu College of Engineering Aurangabad Maharashtra India ECE dept KCG college of Technology Chennai Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Science and Technology Chennai India

New technologies like AR and VR are changing our online and offline experiences. Augmented and virtual reality applications need 3D picture reconstruction and processing. In augmented and virtual reality, this abstract explains 3D image reconstruction and processing concepts, techniques, and effects. Creating engaging AR and VR experiences starts with 3D data. Depth sensors, stereoscopic images, and photogrammetry are essential for accurate 3D reconstructions of real-world objects and settings. These data streams support spatial cognition and interaction in AR/VR. Reconstruction technologies like point cloud and mesh reconstruction are essential for turning 3D data into models. Virtual features that complement or replace the actual environment in AR experiences or build fully fabricated VR settings are based on these models. By applying 2D textures to 3D geometry, “texture mapping” enhances scene realism. Realtime augmented and virtual reality processing requires effective rendering, tracking, and physics simulation algorithms. Rendering engines provide beautiful, responsive experiences, while SLAM lets users explore and interact with virtual information in real life. Occlusion culling and level-of-detail rendering are two optimisation methods needed for smooth performance on high-end VR systems and mobile AR devices. Virtual reality is defined by 3D object interaction and control. Natural and engaging interfaces are created using gesture detection and real-time physics simulations. AR and VR software developers encounter new challenges with each iteration. AR requires accurate spatial mapping, dependable tracking, and simple user interfaces to blend the virtual and real worlds. In contrast, virtual reality (VR) immerses users in realistic computer settings. Portability across operating systems is crucial for greater adoption. Developers must optimise programmes for several hardware platforms to reach the most people.

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