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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Rathod, Dharmendrasinh Patel, Kashyap Goswami, Akashgiri Jashvantgiri Degadwala, Sheshang Vyas, Dhairya Computer Science and Engineering Chandubhai S Patel Institute of Technology Charusat University India Department of Computer Engineering DEPSTAR Charotar University of Science and Technology India Silver Oak College of Computer Application Silver Oak Univercity Gota Gujarat Ahmedabad India Computer Engineering Department Sigma Institute of Engineering Gujarat Vadodara India The Maharaja Sayajirao University of Baroda Gujarat Vadodara India

ISBN: (纸本)9798350398496

This research investigates the use of machine learning techniques for sentiment analysis of drug re views, which is a crucial task for extracting valuable information from the vast amount of unstructured data available online. Drug reviews provide patients' opinions on various aspects of drugs, including their efficacy, side effects, and overall experience. The study explores the performance of different machine learning algorithms for sentiment analysis of drug reviews. Additionally, it examines the effectiveness of various feature engineering techniques, such as bag-of-words, n-grams, and word embeddings, in capturing the subtleties of drug reviews. The study evaluates the performance of these techniques on a publicly available dataset of drug reviews and compares the results based on accuracy, precision, recall, and F1-score. The experiments demonstrate that the appropriate use of machine learning algorithms and feature engineering techniques can accurately capture the sentiment of drug reviews, achieving high accuracy and F1-score. This study's findings can provide valuable insights for healthcare professionals and researchers to analyze patient opinions about drugs, identify potential side effects, and enhance drug development and regulatory processes. © 2023 IEEE.

关键词： Embeddings

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Efficient net-based deep learning: A deep-fake image detection

Efficient net-based deep learning: A deep-fake image detecti...

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Communication, Control and Intelligent Systems (CCIS)

作者： Rohan Singh Dilip Kumar Sharma Praphula Kumar Jain Department of Computer Science and Engineering GLA University Mathura Department of Computer Engineering and Application GLA University Mathura Department of Computer Science and Engineering MITS Gwalior MP

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

With the substantial volume of social media data generated daily and the concurrent emergence of powerful multimedia modification techniques, detecting manipulations in photographs and videos has become an important task. This study examines the strength of a deep learning-based efficient net model for detecting manipulation in multimedia images. This study examines the publicly accessible benchmark dataset DFDC to prove its efficiency. We also highlighted the real-world challenges and emerging trends to combat deep-fakes. The research experiment resulted in and confirmed the efficiency of Efficient-Net as it performed well in both the training and testing stages, exhibiting more accuracy and reduced loss. The test accuracy varies between 50 and 95, with epochs spanning from 0 to 10.

关键词： Training Deep learning Accuracy Social networking (online) Image edge detection Streaming media Market research Feature extraction Spatial databases Intelligent systems

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Deep Learning Driven X-ray Classification for Pneumonia Detection Using CNN

Deep Learning Driven X-ray Classification for Pneumonia Dete...

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Communication, Control and Intelligent Systems (CCIS)

作者： Sarvagya Saxena Siddhartha Khandelwal Sneha Chaudhary Priyanka Gupta Computer Science Engineering GLA University Vrindavan India Computer Science Engineering GLA University Mathura India Computer Engineering & Application GLA University Mathura India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Major deaths (55% or 1.5 million) of LRI (Lower Respiratory Infections) are caused due to Pneumococcal pneumonia. LRI is the 5th highest cause of demise as mentioned by the Global Burden of Disease Study 2015. Health organization suffers from a lack of Physicians. A Physician handles so many cases which leads to misdiagnoses. To resolve all these issues we came up with an easy-to-handle, and implemented in health organizations, the CNN model. The main aim of this model is to help the healthcare professionals by providing them with a tool which will help them detect Pneumococcal pneumonia. With the help of this tool, the work strain of a doctor can be reduced. This model is developed without any pre-trained model and for better accuracy, an immense dataset is used. All the X-ray images were pre processed before being introduced to the neural network. Different layers have been used to make the model more precise and make it adaptable for X-ray images under different conditions. The aim is to combine deep learning concepts with healthcare to make a positive and meaningful impact in the healthcare industry.

关键词： Deep learning Adaptation models Pneumonia Accuracy Computational modeling Neural networks Medical services Organizations X-ray imaging Strain

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Analyzing the Efficiency of Key Management Techniques Used in Information Security Models for Networked applications 5th

Analyzing the Efficiency of Key Management Techniques Used i...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Beemkumar, N. Singh, Vaishali Shree, Ritu Batra, Raman Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

This study aims to investigate the performance of numerous key manipulation strategies utilized in modern statistics protection models. Especially, this painting examines the use of public key infrastructures (PKI), symmetric key infrastructures (SKI), and identity-based totally encryption (IBE) for securing application conversation in networked environments. By way of analyzing the strengths and weaknesses of each gadget, potential improvements are diagnosed. Moreover, a comparison among the unique fashions is done to pinpoint feasible weaknesses and endorse feasible mitigation strategies. The research results will enable the improvement of greater comfortable and green key management structures for networked programs, which can offer effective protection against malicious assaults. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Infrastructures Pinpoint Protection Symmetric Weaknesses

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Domain Transfer Learning for Medical Image Segmentation Tasks 5th

Domain Transfer Learning for Medical Image Segmentation Task...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Kharbas, Vikas Kumar Srivastava, Pulkit Bargavi, Manju Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Mechanical Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Domain transfer learning is a powerful device for clinical image Segmentation duties. It involves transferring information from a source domain to a goal area so that related tasks on those domains can be advantageous from every difference. This era allows improving and robust clinical photo segmentation models by exploiting the prevailing area information and facts from a source area. This approach has been used to enhance the performance of sure segmentation responsibilities in medical imaging together with the segmentation of pathologies from imaging, segmentation of anatomic structures, and system getting to know-based total segmentation. The most critical domain version strategies encompass domain-adversarial neural networks, generative fashions, and meta-getting to know. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Mental Health Prediction using Machine Learning

Mental Health Prediction using Machine Learning

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Govindaraj M V Asha H Jayanth Harshitha D G Harshitha V Patil N. Mithili Devi Master of Computer Applications New Horizon College of Engineering Bengaluru India Master of Computer Application New Horizon College Of Engineering Bengaluru India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Major problems in the 21st century are mental health care problems. It can be considered as one of its important evils between men, and its source is in the largeness of awareness among the people. Using this paper, we would like to make a person realize that he is suffering from some kind of mental illness for instance, depression, anxiety, PTSD, insomnia, etc., by simply making the people aware of their symptoms using Machine learning. A sample of various ages, conditions, professions, sex, lifestyles taken from a group of respondents and collected with the help of a survey form containing questions which are often used by psychologists to understand the problem of their patient in detail has been applied machine learning algorithms on.

关键词： Surveys Technological innovation Machine learning algorithms Anxiety disorders Machine learning Mental health Predictive models Depression Socioeconomics Forecasting

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IoT based Truck Wheel Temperature Monitoring System

IoT based Truck Wheel Temperature Monitoring System

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Zaid, Mohd Ahmad, Nadeem Aljamal, Mohammed Patel, Sarosh Khan, Asif Dept. of Computer Science and Engineering Integral University Lucknow India Dept. of Electronics and Communication Engg. Goel Institute of Technology and Managment Lucknow India School of Engineering University of Bridgeport United States Integral Robotics Lab Dept. of Computer Application Integral University Lucknow India

ISBN: (纸本)9798350374933

The main objective of this paper is to design an IoT based truck wheel temperature monitoring system using Arduino board with the aim to overcome endanger and raise the safety measures for truck and goods. By designing and implementing a temperature-detecting system, we can detect the temperature of the wheels trucks. By the use of such detecting device we can prevent the major problem associated with wheel’s temperature and may avoid the major accidents. This monitoring system senses the temperature data of the wheel and transmits through the Wi-Fi module. In addition, it upgrades Thingspeak channel that can collect the data and it can easily be accessed by the transport agency and the truck driver. This can further be analysed using any computer system or mobile with internet connectivity anywhere across the globe to take real-time decision. The core function of the proposed detecting system is to monitor the real- time value of truck wheel temperature and to inform the concerned people. © 2024 IEEE.

关键词： Wi-Fi

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Process parameters optimization in CNC turning of aluminum 7075 alloy using TOPSIS method coupled with Taguchi philosophy

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Materials Today: Proceedings 2022年 56卷 989-994页

作者： Kanaujia, Neetu Rahul Behera, Jitendra Kumar Kumar Mohapatra, Sambit Behera, Asit Jha, Pushkar Kishore Joshi, Kamal Chandra Routara, Bharat School of Computer Application KIIT Deemed to be University Bhubaneswar751024 India School of Mechanical Engineering KIIT Deemed to be University Bhubaneswar751024 India Sarang Dhenkanal759146 India

Aluminum 7075 is a very useful metal as it is used extensively in various fields of engineering like aerospace, constructions, electronics industry etc. As modern industry demand is very high accuracy of products in shorter period of time;so CNC machines are perfect solution for it. CNMG120408 uncoated carbide inserts used for machining of aluminum 7075. So in this research work, we studied the outcome of different parameters (i.e. cutting velocity (Vc), feed rate (Vf), depth of cut (Dc) and coolant pressure (Pc)) on the responses like material removal rate (MRR), surface roughness (Ra) and tool tip temperature (Tavg). Our primary objective is to find out such a parametric settings in that MRR will be maximum and Ra and Tavg is will be minimum. Here we used L16 orthogonal array and the results were optimized by TOPSIS method integrated with Taguchi philosophy. © 2022

关键词： Electronics industry

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Smart Firefighting Early Warning and Evacuation System Based on Edge Computing with Led Dynamic Indicator Devices 6

Smart Firefighting Early Warning and Evacuation System Based...

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6th International Conference on Communications, Information System and computer engineering, CISCE 2024

作者： Yu, Liang Lai, Yangbing Mo, Jiayu Zeng, Yuze Tan, Guoshen Xu, Haonan College of Computer Guangdong University of Science and Technology Dongguan China AIoT Edge Computing Engineering Technology Research Center of Dongguan City Guangdong AIoT Application Innovation Joint Laboratory Dongguan China

ISBN: (纸本)9798350353174

The current standard practice for building fire safety relies heavily on regular lighting, emergency lighting, and smoke sensors. The management and control centers for these systems are typically separate and independent, resulting in a bulky and expensive local control system that is difficult to use and manage. Additionally, the traditional fixed instructions in fire protection systems do not allow for adaptive planning of optimal escape routes based on the location of the fire, leading to delays in evacuation and ultimately causing significant casualties. This paper presents the design of a smart fire warning and evacuation system based on edge computing utilizing LED dynamic indication devices. The system integrates LED dot-matrix technology to create a smart indicator and emergency light with dynamic adaptive indication capabilities. Additionally, it includes temperature, humidity, and smoke sensors to monitor the building's environmental conditions. Through Mesh wireless networking, time-series sensing data is aggregated to the edge gateway, where a miniature machine learning model trained by the Edge Impluse (EI) platform performs edge computing for predicting and reasoning about fire situations. The data is then uploaded to a cloud server and forwarded to mobile users via Cat.1 communication protocol for real-time monitoring. The system also incorporates an improved ant colony algorithm for automatic execution in the cloud and allows manual commands from mobile terminals to control adaptive and dynamic indication of indicator lights and emergency lights, guiding building personnel to the safest evacuation path. System testing demonstrates a high accuracy rate of 95% for fire prediction and inference, with an average inference time of less than 1 ms. This system provides a novel approach to adaptive building fire prediction and evacuation, enhancing safety measures for orderly and secure evacuations. © 2024 IEEE.

关键词： Ant colony optimization

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Development of an Electric Automation Control Model Using Artificial Intelligence 2

Development of an Electric Automation Control Model Using Ar...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Srikanth, V. Aswini, P. Asha, V. Pithamber, Khobragade Sobti, Rajeev Salman, Z. School of Sciences and Computer Studies Bengaluru India Hyderabad India New Horizon College of Engineering Department of Master of Computer Application Bangalore India Mlr Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (纸本)9798350364699

The design of an AI-based electrical computerization control system is implemented to efficiently address the challenges encountered by contemporary electrical engineering. A model of an AI-based control system for electrical automation is presented. To achieve optimal control settings, the control strategy makes use of an artificial intelligence system. Even in the presence of a twenty per cent load intervention and 2.1 Hz wavelength interference, the research demonstrates that the organisation has significant anti-interference capabilities, as shown by an acceptable fail rate of 0.02 for each at the system management level. Consequently, using an AI algorithm in automated electrification control may lead to a significant improvement in control reaction time, cost savings, and efficient construction. © 2024 IEEE.

关键词： AI Artificial Intelligence Control System Cost Savings Electrical engineering

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