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Using AI and Blockchain in Case of Algorithm Regulation 2

Using AI and Blockchain in Case of Algorithm Regulation

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Tripathi, Neha Mishra, Amit Kumar Dept. of Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350320923

The implication of AI is determined by some basic applications where the important analysis is properly modulated as much as possible. All of the basic applications are determined by the growth of AI technologies for finding a better perspective appropriately. Fundamentally, there are different aspects based on the advantages of AI developed by the basic application on the organized basic application of the various variable perspective. In addition, all of the importance regarding the basic development basis is properly incremented on the functional approach. Examination of some basic applications to determine all of the appropriate resolutions has to be evaluated in this research article. © 2023 IEEE.

关键词： Machine learning

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Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

Efficient Compressed Sensing for Real-Time Electrocardiogram...

引用

2024 IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2024

作者： Terrell, Mitchell Weissman, Jon University of Minnesota Dept. of Computer Science and Engineering MinneapolisMN United States

ISBN: (纸本)9798350351552

Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time. © 2024 IEEE.

关键词： Digital storage

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Prediction of Alzheimer's Disease using Deep Learning Algorithms 2

Prediction of Alzheimer's Disease using Deep Learning Algori...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Praveen, P. Srilatha, Koleti Sathvika, Masabathini Nishitha, Edulapuram Nikhil, Madduri Sr University Dept. of Computer Science and Artificial Intelligence Warangal India Sr Engineering College Dept. of Computer Science Engineering Warangal India

ISBN: (纸本)9781665456302

Throughout the world, at least five crore individuals are thought to have the disease of Alzheimer. This Alzheimer's disease is the most well-known and prevalent kind of dementia (AD). Since the modest but significant advancements in Alzheimer's disease (AD) treatment, the diagnostic emphasis has progressively switched to the precise identification of the disease's earliest stage. In numerous attempts at clinical classification, the difficulty of differentiating pre-clinical AD from changes associated with normal aging or developed AD has been recognized. Alzheimer's disease is a neurological and brain disorder that eventually makes it difficult to do even the most fundamental tasks. It gradually destroys memory and thinking skills. It is a neurological degenerative disorder that causes both brain cell shrinkage and degeneration. The most typical reason of dementia is a gradual loss of cognitive ability, behavioral, and social abilities, limiting their capacity to operate independently. It causes both brain cell shrinkage and degeneration. As people live longer, there are more concerns about aging. Damage to brain cells can be avoided with an early diagnosis of this disease. Early detection can considerably slow or stop the growth of this disease because there is no cure for it. To stop the progression of Alzheimer's disease, early detection is essential. As a result, experts can initiate preventive care as soon as possible. They call for prompt and precise Alzheimer's disease detection in its initial and most elusive stages. The only accurate approach for diagnosis is magnetic resonance imaging (MRI) brain imaging, although exams like the Mini-Mental State Examination (Fol stein 1975), or MMSE, are routinely used for earlier diagnosis. The main objective of this research work is to devise a technique for accurately identifying and staging diseases in magnetic resonance imaging (MRI). Deep learning excels at analyzing images and predicting diseases. A growing body o

关键词： Magnetic resonance imaging

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A Comparative Study of Deep Image Retrieval Models Leveraging Deep Features

A Comparative Study of Deep Image Retrieval Models Leveragin...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Ashish Varshney, Sudeep SET Sharda University Dept. of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350378092

This review investigates how deep learning methods can be utilized for efficient image retrieval based on content. Obtaining accurate images from vast digital collections poses significant challenges, motivating research in CBIR. The effectiveness of these methods varies depending on the dataset's type and size, with certain algorithms excelling with specific dataset characteristics. An extensive and well-structured review of successful image retrieval techniques is given in this paper. Our primary objective is to evaluate various deep learning models applied by researchers and compare their performance based on the outcome of evaluation matrices. These models encompass CNNs, DBNs, and other deep architectures tailored for image retrieval tasks. By synthesizing insights from this review, researchers can make informed decisions regarding model selection and potentially enhance retrieval performance by leveraging advanced deep learning features. The importance of deep features in image retrieval is the ability to capture complex visual patterns and semantic information that cannot be easily extracted by traditional handcrafted features. With the increasing volume of online images, Image retrieval using deep learning has become crucial for applications like object recognition, image retrieval, and image search engines. © 2024 IEEE.

关键词： Contrastive Learning

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Dirt Identification and Removal based on Non-Contact Infrared Temperature Sensor on Solar Panel 7

Dirt Identification and Removal based on Non-Contact Infrare...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Rupin, Rezmah Jemima Hasan, Imran Jahan, Rownak Haque Ontor, Md. Rakibul Islam, Md. Motaharul East West University Dept. of Computer Science Engineering Dhaka Bangladesh United International University Dept. of Computer Science Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341485

Temperature sensing is a technique to detect dirt on a solar-panel-surface that can influence its surface temperature and can reduce its performance, which necessitates monitoring to optimize solar panel output generation, reduce unnecessary costs, and perform efficient cleaning. Temperature sensing can achieve faster and more accurate detection of dirt without using any robotic cameras or manual observation. In this study, a method is proposed to detect dirt on a solar panel with the help of an MLX90614 sensor, a DHT22 sensor connecting with Arduino UNO components. DC motors and gear motors are being used for the removal of accumulated dirt. The temperature of the solar panel surface is measured by an MLX90614 sensor using infrared radiation, while the environment's humidity and temperature are measured by a DHT22 sensor. The system implies measuring the solar panel's temperature and comparing it to the ambient temperature. This system aims at improving previously proposed dirt detection methods using this approach and suggests a motor-driven dirt removal technique. © 2023 IEEE.

关键词： Solar panels

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Facial Emotional Recognition Using Convolutional Neural Network 2

Facial Emotional Recognition Using Convolutional Neural Netw...

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Raj, Deepak Wassay, Md. Abdul Chandigarh University Dept. of Computer Science Mohali India Chandigarh University Dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798350320923

The face of a person is his identity. Most of his emotions, issues can be derived from his face. Face is the window to the soul which was said by famous French doctor Duchenne de Boulogne. He used several techniques to support his theory like giving shocks of electric impulses to understand how a person reacts to muscular contractions. He also tried to induce some of the expressions of bizarre looking. The ultimate aim is to analyze how much muscles contribute to emotion. He successfully derived lots of human emotions which is hidden. After 200 years this field is still active as it requires more experiments to extract invisible truths of human truths. Lots of Emotion Recognition which is automatic have been seen in the field of Marketing and Advertising. It is also seen in the field of Medical, Law and order...etc. The main fundamental question arrives whether it is good to enter into our personal space. This a question which has lots of dimension. Those who are against this practice is claiming that this is a violation of human rights by which we can use the stored data to harm the society in future. Even though it is a major concern if we can rectify the above the scope in this area is phenomenal. Lots of researchers are still working on this area because of mainly one reason that is its wide application in the area of Medical science. It can be used by a doctor for diagnosing a person with certain psychological disorders, children with Autism, person with Parkinson's disease. It can be widely used to diagnose children with Autism so that particular child can be motivated at early stage leads to his success in future and thus provide a good citizen to the society. In our research we are using several datasets such as FER2013, CK+...etc. These datasets have been given to a model which includes Deep Convolutional Neural Network. Here input image is given to a model using camera. The particular image has to done preprocessing to extract fine features in the model. A

关键词： Emotion Recognition

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Machine Learning-based Decision Making for Charging/Discharging Cost Optimization of PREV in Smart City 1

Machine Learning-based Decision Making for Charging/Discharg...

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1st International Symposium on Parallel Computing and Distributed Systems, PCDS 2024

作者： Neelakantam, Gone Ecole Centrale School of Engineering Mahindra University Dept. Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350349658

Smart cities, equipped with smart grid infrastructure, establish advanced communication networks that facilitate interactions among multiple entities. It is well-known that smart grid technology ensures the reliable transmission of electricity. Additionally, Plug-in Hybrid Electric Vehicles (PHEVs) significantly contribute to the efficient utilization of energy in mobility-aware environments. The Intelligent Transportation System (ITS) enables Vehicle-to-Infrastructure (V2I) communication, which is crucial for providing transportation services and managing PHEV recharging at user-preferred locations. This capability is a key element of smart city infrastructure. To optimize the use of PHEV services in a smart city, addressing the cost minimization of charging and discharging is essential. Therefore, this paper proposes a decision tree machine learning-based algorithm within a fog computing platform aimed at minimizing charging and discharging costs. Performance evaluation demonstrates that the model outperforms existing algorithms in terms of accuracy. These results indicate that our model can accurately predict the costs associated with charging and discharging PHEVs in smart city environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Using ChatGPT in Undergraduate computer science and Software engineering Courses: A Students' Perspective 54

Using ChatGPT in Undergraduate Computer Science and Software...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Andersen-Kiel, Noah Linos, Panagiotis Butler University Computer Science and Software Engineering Dept. IndianapolisIN United States

ISBN: (纸本)9798350351507

This innovative practice full paper presents an empirical study aimed at evaluating the potential of ChatGPT, an advanced AI-driven chatbot, as a supplementary educational tool in undergraduate computer science and Software engineering (CSSE) courses. The study, initiated in the summer of 2023, focused on assessing ChatGPT's capabilities in generating accurate and complete computer code, identifying and rectifying code defects (bugs), and its scalability in handling larger programs. To achieve this, we conducted a series of experiments with ChatGPT. In one experiment, we introduced bugs into small programs from introductory CSSE courses. ChatGPT was tasked with detecting these defects and providing recommendations for fixing them. We evaluated ChatGPT's effectiveness in bug detection, the quality of its recommendations, and the completeness of the proposed solutions. We sought answers to questions such as whether ChatGPT found all injected defects, provided appropriate recommendations, and delivered high-quality solutions based on criteria like code completeness, size, complexity, and readability. In another experiment, ChatGPT was asked to generate code for assignments from previous CSSE courses, including Intro to computer science and Programming in C++, Intro to Python Programming, and Object-Oriented Programming and Data Structures using Java. We assessed the generated code's correctness and quality in comparison to student-written code. Similarly, in a third experiment, we evaluated ChatGPT's ability to generate larger programs using requirement specifications from an upper-division CSSE course on Agile Software engineering. Analyzing both qualitative and quantitative data from these experiments during the summer, we determined that ChatGPT showed promise as an educational tool. Consequently, we developed a plan to integrate ChatGPT into select CSSE courses for the fall semester of 2023. Specifically, ChatGPT was integrated into two of our introductory CSSE cou

关键词： Object oriented programming

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Soil Monitoring Using Machine Learning Algorithms

Soil Monitoring Using Machine Learning Algorithms

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agarwal, Jyoti Bhatt, Ishita Goyal, Sahaj Singh, Prabhdeep Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350394412

Soil monitoring is important aspect of agriculture to grow good quality food. It is crucial aspect for famers and the environmental scientists as well. If soil is monitored properly then chances of good quality crop increased which reduces farmer's tension and increase their income. This research paper is a step towards applying machine learning algorithms for soil monitoring. Decision Tree Regression, Random Forest Regression, Gradient Boosting Regressor and Stacking Regressor are such algorithms. Comparison analysis of these techniques are also done in this paper. This work will be helpful for farmers and land managers to accurately predict about the soil fertility which will further help them to take decision which crop should be grow as per the soil condition. © 2024 IEEE.

关键词： Adversarial machine learning

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The Integration of Semantic and Structural Knowledge in Knowledge Graph Entity Typing

The Integration of Semantic and Structural Knowledge in Know...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Li, Muzhi Hu, Minda King, Irwin Leung, Ho-Fung Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798891761148

The Knowledge Graph Entity Typing (KGET) task aims to predict missing type annotations for entities in knowledge graphs. Recent works only utilize the structural knowledge in the local neighborhood of entities, disregarding semantic knowledge in the textual representations of entities, relations, and types that are also crucial for type inference. Additionally, we observe that the interaction between semantic and structural knowledge can be utilized to address the false-negative problem. In this paper, we propose a novel Semantic and Structureaware KG Entity Typing (SSET) framework, which is composed of three modules. First, the Semantic Knowledge Encoding module encodes factual knowledge in the KG with a Masked Entity Typing task. Then, the Structural Knowledge Aggregation module aggregates knowledge from the multi-hop neighborhood of entities to infer missing types. Finally, the Unsupervised Type Re-ranking module utilizes the inference results from the two models above to generate type predictions that are robust to false-negative samples. Extensive experiments show that SSET significantly outperforms existing state-of-the-art methods. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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