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Prediction Of Typhoid Using Machine Learning and ANN Prior To Clinical Test

Prediction Of Typhoid Using Machine Learning and ANN Prior T...

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International Conference on computer communication and Informatics (ICCCI)

作者： Md. Atik Bhuiyan Sharaf Shahariare Rad Fatema Tuj Johora Abdullah Islam Md Ismail Hossain Aliza Ahmed Khan Dept. of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Dept. of Communication and Environment Rhine-Waal University of Applied Sciences Kamp-Lintfort Germany

One of the most prevalent illnesses, typhoid causes a large number of fatalities each year, primarily in Africa. A quick and accurate diagnosis is essential in the medical sector. Self-medication, delayed diagnosis, a lack of medical expertise, and inadequate healthcare facilities all contribute to the high incidence of typhoid fever mortality. Machine learning as well as deep learning has worked wonders for extrapolative analysis in the health industry, and as a result, more health industries are utilizing machine learning techniques. This is the earliest evaluation where a typhoid fever prediction model is being developed which predicts prior to a clinical trial. In this paper, deep learning and machine learning have been employed to develop the model. Ten algorithms have been utilized here, and the XGBoost classifier is the best performer with 97.87% accuracy.

关键词： Deep learning Industries Machine learning algorithms Computational modeling Medical services Predictive models Prediction algorithms

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Speaker's Emotions Recognition Module Based on the GoogleLeNet Neural Network

Speaker's Emotions Recognition Module Based on the GoogleLeN...

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2022 International Conference on Smart Information systems and Technologies, SIST 2022

作者： Tereikovskyi, Ihor Tereikovska, Liudmyla Kryvoruchko, Olena Tereikovskyi, Oleh Tyshchenko, Dmytro Franchuk, Tamara National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of System Programming And Specialized Computer Systems Kyiv Ukraine Kyiv National University Of Construction And Architecture Dept. Of Information Technology For Design And Applied Mathematics Kyiv Ukraine Kyiv National University Of Trade And Economics Dept. Of Software Engineering And Cyber Security Kyiv Ukraine National Technical University Of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Dept. Of Department Of Computer Engineering Kyiv Ukraine

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

The article is devoted to the development of means for recognition of the emotions of the speaker, based on the neural network analysis of fixed fragments of the voice signal. The possibility of improving recognition tools through the use of a convolutional neural network of the GoogLeNet type is shown. The expediency of training the network on the examples of the TESS database is determined. A procedure for representing a voice signal in the form of a square grayscale image has been developed. Based on the use of the specified database, the modification of the GoogLeNet network was carried out. The architecture and software implementation of the recognition module have been developed. With the help of computer experiments, it has been established that after 150 epochs of training on a fairly limited training sample, the modified GoogLeNet makes it possible to achieve an accuracy of recognition of the speaker's emotions of approximately 0.97 on test examples. The obtained accuracy is comparable to the accuracy of the best modern systems of this purpose and confirms the promise of using neural network models like GoogLeNet for emotion recognition based on voice signal analysis. The need for further research aimed at reducing the resource intensity of the neural network model is shown. © 2022 IEEE.

关键词： Emotion Recognition

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Leg Movement Controlled Robotic Leg for Menace Preclusion

Leg Movement Controlled Robotic Leg for Menace Preclusion

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SmartBlock4Africa, IEEE

作者： Mensah Sitti Anita Antwiwaa Archibald Danquah-Amoah Kannan Govindan Ezekiel Mensah Martey Dept of Computer Science and Engineering University of Mines and Technology Tarkwa Ghana Dept. of Electronics and Communication Engineering All Nations University College Koforidua Ghana Dept. of Medical Laboratory Accra Technical University Accra Ghana Dept. of Orthotics and Prosthetics University of Health and Allied Sciences Ho Ghana

ISBN: (数字)9798350376838

ISBN: (纸本)9798350376845

Movement of autonomous humanoid is a key area, as we gradually accept robots. This research proposes an “Artificial Human Leg Model” which can precisely follow the real movements of human leg by means of image processing thereby avoiding sensor noise and damage and providing more adjustable response. Webcams are used to capture the leg images followed by Morphological image processing to extract information from human leg movement. The four degrees of freedom implemented in the proposed method are rotation of base, leg like motions, knee like motions, and steps motions. The robot leg is controlled successfully using human leg movement, where the system can travel a distance, manoeuvre in an uneven surface and move the robot from one place to other with precision. The user can control the leg entirely based on his/her intentions to accomplish the required task. The proposed real-time method of implementing human leg movement also ensures immediate control and performance. Distant control of the robotic leg does not only ensure complete safety to human, but also allows flexibility in controlling distant objects. The overall goal of the proposed system is to guarantee safety, flexibility and accuracy in movements when performing sensitive tasks in day-to-day life activities thus offering suitability for menace preclusion.

关键词： Legged locomotion Webcams Image processing Process control Surface morphology Robot sensing systems Real-time systems Safety Reliability Robots

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Study of an OPTO-ASIC for a Functionally Safe Speed Sensor Using the Spatial Frequency Filter Method

Study of an OPTO-ASIC for a Functionally Safe Speed Sensor U...

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IEEE Design and Diagnostics of Electronic Circuits and systems (DDECS)

作者： Frank Wasinski Werner Bonath Ubbo Ricklefs Michael Schwarz Josef Börcsök Dept. of Electrical Engineering and Information Technology THM University of Applied Sciences Giessen Germany ICAS Institute for Computer Architecture and System Programming University of Kassel Kassel Germany

ISBN: (数字)9798331528010

ISBN: (纸本)9798331528027

For decades, speed sensors based on the spatial frequency filter method have successfully proven themselves in a wide range of applications. This makes the sensor concept particularly suitable for the area of functional safety. New filter variants extend the previous measuring range to very low speeds up to standstill detection. By combining the sensor with a safety system on chip for recording and analysing measured values, a functionally safe measuring chain can be established. In order to demonstrate the variability and performance of the new filters, an optical application-specific integrated circuit was designed, which forms the basis for further extensive tests. The core components are a large number of freely configurable photodiode channels, which were realised via a redundant structure, as well as two different transimpedance amplifier concepts. In the further course of the project, the ASIC will be analysed with regard to its desired performance parameters and its robustness.

关键词： Optical filters Semiconductor device measurement Optical design Stimulated emission Safety Topology system-on-chip Optical sensors Optical amplifiers Velocity measurement

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Optimal Scheduling and Sizing of Energy Storage system using Hybrid Algorithm for Electric Vehicles

Optimal Scheduling and Sizing of Energy Storage System using...

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2022 International Virtual Conference on Power engineering Computing and Control: Developments in Electric Vehicles and Energy Sector for Sustainable Future, PECCON 2022

作者： Dhivya, S. Arul, R. Maheswari, S. Kanimozhi, R. Karthik, N. School of Electrical Engineering Vellore Institute of Technology Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil nadu Chennai India FEAT Annamalai University Dept. of Electronics and Communication Engineering Tamil nadu Chidambaram India Hindustan Institute of Technology and Science Dept. of Electrical and Electronics Engineering Tamil nadu Chennai India

ISBN: (数字)9781665485920

ISBN: (纸本)9781665485920

The rapid exhaustion of fossils shaded concern about environmental problems which enforce researchers to embed this renewable distribution generation into power system networks. Electric vehicles are increasing threat to reliability of power grid by overloaded equipment and disturbed stability in voltage. However, it is important that electric vehicles must provide support to grids through vehicle to grid optimized V2G scheduling to minimize load variances. The purpose of this study is to employ a hybrid approach to improve energy storage system scheduling and sizing for electric vehicles. The hybrid version of Flower Pollination and golden section search is used. This research considers peak shaving, valley filling and priority charging mode of Energy Storage system for effective load management. Results indicated that optimized and scheduled energy storage system can fulfill the proper load allocation at grid. © 2022 IEEE.

关键词： Energy storage

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Implementation of the Optical Lock through Visible Light communication using AES

Implementation of the Optical Lock through Visible Light Com...

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Advances in Signal Processing, Power, communication, and Computing (ASPCC), IEEE International Conference on

作者： K.P. Swain Sumant Kumar Mohapatra Puspanjali Mallik R. K. Dash Rakesh Kumar Lenka Department of ETC Trident Academy of Technology Bhubaneswar India Dept. of CSE Shailabala Women’s College Cuttack India Dept. of Electrical Electronics and Communication Engineering Galgotia University Dept. of Computer Science Central University of Odisha Sunabeda Koraput

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study presents the development and implementation of a novel optical lock using visible light communication (VLC), leveraging a custom-built mobile application and flash LED for secure information transmission. The system utilizes an Android application with a user-friendly interface to input and encrypt messages using the AES-128 bit algorithm and Base64 encoding. The encrypted message is transmitted via a smartphone’s flash LED as an OOK signal, received by a specially designed PCB composed of a photodiode and a servo motor. At the heart of the receiving mechanism is a Raspberry Pi, which is responsible for decrypting the message and executing a comparison check with a pre-stored message to determine system unlocking. This innovative approach combines the convenience of mobile technology with advanced encryption, offering a robust solution in optical locking systems.

关键词： Heart Signal processing algorithms Information processing Light emitting diodes Mobile applications Encryption Photodiodes Optical signal processing Servomotors Visible light communication

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Channel Allocation in 5G Networks using Ant Colony Optimization Algorithm

Channel Allocation in 5G Networks using Ant Colony Optimizat...

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International Youth Conference on Radio Electronics, Electrical and Power engineering (REEPE)

作者： Areej Hussein Mohammad Anbar Mohammed Molhem Atheer Yousif Oudeh Mohammad Nassr Habib Fardoun Dept. of Communication Technology Engineering University of Tartous Tartous Syria Dept. of Computer Engineering Technology Al-Ayen University Thi-Qar Iraq Dept. of Computer Science College of Education for pure Science University of Thi-Qar Scientific Research Center Al-Ayen University Thi-Qar Iraq Canadian University Dubai UAE

Efficient channel allocation is crucial for enabling concurrent calls and data sessions within a cellular network in general. When channels are inefficiently allocated, network congestion may occur leading to call blocking due to a lack of available channels. In this research, an Ant Colony Optimization (ACO) algorithm-based model has been proposed to tackle channel allocation problem in 5G cellular networks. The proposed model has been simulated using Python. The experimental study focused on minimizing Call Block Probability (CBP) by optimizing channel allocation as the number of network users increase.

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Talk to your data: Enhancing Business Intelligence and Inventory Management with LLM-Driven Semantic Parsing and Text-to-SQL for Database Querying

Talk to your data: Enhancing Business Intelligence and Inven...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Jerry Zhu Saad Ahmed Bazaz Srimonti Dutta Bhavaraju Anuraag Imran Haider Srijita Bandopadhyay Faculty of Mathematics University of Waterloo Ontario Canada Dept. of Computer Science National University of Computer and Emerging Sciences Islamabad Pakistan School of Agro and Rural Technology Indian Institute of Technology Guwahati Guwahati India Dept. of Electronics and Communication Engineering Vellore Institute of Technology India Dept. of Industrial and Systems Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. of Electronics and Communication Engineering University of Engineering and Management Kolkata India

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

This paper delves into the potential of Large Language Models (LLMs) in revolutionizing business intelligence and inventory management through semantic parsing and text-to-SQL methodologies. It assesses various LLM models, such as DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, elucidating their capabilities and contributions. Two critical analyses are presented here. The first compares cutting-edge LLM models using cosine similarity and cost efficiency metrics. The second analysis enhances GPT’s precision through prompt engineerings, like few-shot techniques, and explores frameworks like DIN-SQL, NSQL, and DSP. DIN-SQL substantially boosts accuracy, and NSQL demonstrates potential in specific scenarios. This research underscores the transformative potential of LLM-driven models in business intelligence and inventory management. DIN-SQL, in particular, emerges as a game-changer with the potential to reshape inventory management practices. GPT showcases its versatility through fine-tuning for tasks beyond conventional programming, while CoPilot offers a cost-effective alternative. This study emphasizes the importance of cost-effectiveness in real-world applications, with LLaMa and CoPilot being practical choices. NSQL, with its budget-friendly and semi-accurate solution, holds promise for semantic parsing in growing companies. These insights are a foundation for further innovation, promising unmatched efficiency and competitiveness across industries in the evolving Artificial intelligence landscape.

关键词： Industries Structured Query Language Technological innovation Costs Large language models Semantics Inventory management

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Investigating the Advantages of AI-driven Predictive Analysis in Healthcare

Investigating the Advantages of AI-driven Predictive Analysi...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Murtuza Ali Mohammed Vazeer Ali Mohammed Ashween Ganesh T. Kiruthiga Dept. of Computer & Information System Lewis University Illinois USA Research and Development Fresenius Medical Care North America Concord CA Dept. of ECE Vetri Vinayaha College of Engineering and Technology Trichy India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

AI-pushed predictive evaluation can revolutionize healthcare. With the usage of superior predictive models and machine algorithms, AI can discover styles and appropriately expect consequences for clinical treatments and remedies. It may assist clinicians in making more informed decisions and ensuring certain affected persons' safety. In this summary, we assess the benefits of imposing AI-driven predictive analysis in healthcare AI-driven predictive evaluation permits the usage of state-of-the-art fashions that can correctly predict medical outcomes. It allows doctors and healthcare carriers to limit risks and discover ability problems earlier than they grow to be intricate. By way of leveraging available information, such as scientific records, genomic information, and more, AI can accurately generate insights and guidelines for remedy and prognosis. Moreover, AI-pushed predictive evaluation is incredibly capable of figuring out destructive drug reactions and different interactions among drugs, in addition to providing perception into drug interactions among illnesses and other conditions. AI-driven predictive evaluation has numerous packages in healthcare and may be used to pick out capacity mistakes before they occur. AI can analyze a large variety of records and generate reports on the consequences. It will assist healthcare providers in making the proper decisions in relation to imparting top-quality care for sufferers. Moreover, AI can be used to expand customized treatment plans, which are custom-designed to every patient’s unique state of affairs. It may be extraordinary.

关键词： Drugs Medical services Predictive models Prediction algorithms Market research Safety Artificial intelligence Predictive analytics Prognostics and health management Medical diagnostic imaging

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Neural Network Coding in Data Compression systems in communication Channels 6

Neural Network Coding in Data Compression Systems in Communi...

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6th International Scientific Conference on Information, Control, and communication Technologies, ICCT 2022

作者： Berezkin, Aleksandr Kukunin, Dmitry Kirichek, Ruslan State University of Telecommunications Bonch-Bruevich Saint-Petersburg Dept. of Software Engineering and Computer Science St. Petersburg Russia Bonch-Bruevich Saint-Petersburg State University of Telecommunications Dept. of Communication Networks and Data Transmission St. Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

The article describes the use of a neural network implementation of BCH codes encoding and decoding devices for error correction in on-the-fly data compression systems. The user payload vector is encoded in the BCH code with additional redundancy, which provides linear data separability and better convergence when training the autoencoder. On the receiving side, the decoding device operates in the error correction mode, which makes it possible not to impose strict requirements on the accuracy of data recovery when training the autoencoder. Examples of such devices are described in the article. © 2022 IEEE.

关键词： Data compression

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