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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Muhammad Shameem, P. Jemsheer Ahmed, P. Nisa Parvin, K.P. Safa, M.T. Sahana, V.P. Hima Musthafa, T.H. Mea Engineering College Department of Computer Science & Engineering Malappuram India

ISBN: (数字)9798350381689

ISBN: (纸本)9798350381689

Leukemia, a life-threatening blood cancer, de-mands early and accurate diagnosis for effective treatment. This project aims to bring out the potential of dual deep learning techniques to enhance acute lymphoblastic leukemia detection. Our approach combines the capabilities of Convolutional Neural Networks (CNNs). We also propose a mobile application that integrates these dual deep learning models to provide a user-friendly and accessible solution for acute lymphoblastic leukemia detection. High-resolution medical images, such as peripheral blood smears undergo thorough examination using advanced CNN architectures. Sequential data, encompassing temporal information like cell morphology evolution, genetic markers, and patient histories, are systematically analyzed. The dual deep learning model is trained on an extensive and diverse leukemia dataset, encompassing various subtypes and disease stages. Fine-tuning and transfer learning techniques are employed to optimize model performance. Once trained, the system is assured to efficiently evaluate new patient data,providing rapid and highly accurate leukemia diagnosis. The app is trained by using a comprehensive dataset of annotated blood cell images. The app will be designed with a user-friendly interface, enabling medical professionals to easily upload and analyze blood samples. The system's output will provide rapid and accurate leukemia detection, reducing the time and subjectivity associated with manual diagnosis. In summary our project aims to advance acute lymphoblastic leukemia detection by combining two or more powerful deep learning methods to work together effectively. This system holds the promise of assisting healthcare practitioners in making more informed decisions, ultimately leading to improved patient outcomes and more effective management of this life-threatening disease © 2024 IEEE.

关键词： Diseases

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Brain Tumor Diagnosis Using Deep Learning: A Literature Review 2

Brain Tumor Diagnosis Using Deep Learning: A Literature Revi...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Bhuvaneswari, S. Thomas, Joel Nithish, S. Prithvi, S. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350328202

Brain tumor diagnosis is a critical task in the field of medical imaging, with the potential to significantly impact patient outcomes and treatment planning. The use of deep learning has been more well-known within the last ten years as a potential method for improving and automating the detection and categorization of brain tumors. The goal of this literature review is to present a thorough overview of the application of deep learning to the diagnosis of brain tumors. © 2024 IEEE.

关键词： Medical imaging

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DeepQ Based Automated Irrigation Systems Using Deep Belief WSN

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Intelligent Automation & Soft Computing 2023年第3期35卷 3415-3427页

作者： E.Gokulakannan Department of Computer Science and Engineering MRK Institute of TechnologyCuddalore608301TamilnaduIndia

Deep learning is the subset of artiﬁcial intelligence and it is used for effective decision *** Sensor based automated irrigation system is proposed to monitor and cultivate *** system consists of Distributed wire-less sensor environment to handle the moisture of the soil and temperature *** is automated process and useful for minimizing the usage of resources such as water level,quality of the soil,fertilizer values and controlling the whole *** mobile app based smart control system is designed using deep belief *** system has multiple sensors placed in agriculturalﬁeld and collect the *** collected transmitted to cloud server and deep learning process is applied for making *** residue analysis method is proposed for analyzing auto-mated and sensor captured ***,we used 512×512×3 layers deep belief network and 10000 trained data and 2500 test data are taken for *** is automated process once data is collected deep belief network is *** performance is compared with existing results and our process method has 94%of accuracy ***,our system has low cost and energy consumption also suitable for all kind of agriculturalﬁelds.

关键词： Wireless sensor network deepq residue automation deep belief network tensorﬂow

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A heterogeneous soft-hard fusion framework on fog based private SaS model for smart monitoring of public restrooms

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 8957-8984页

作者： Gore, Rajasi Banerjea, Shashwati Tyagi, Neeraj Computer Science and Engineering Department MNNIT Allahabad Uttar Pradesh Prayagraj211004 India

Usage of public restrooms, provided by the Indian government under "Clean India Mission", is limited due to negligence in manual supervision. This paper proposes an Internet of Things-based smart application for sanitation to keep check on geographically distributed restrooms autonomously through soft-hard sensors. Due to a substantial increase in the number of sensing devices in recent years, the application aims to re-utilize data from hard sensors co-located near the restroom locations. A private Sensing as a Service (SaS) paradigm is proposed on the fog node that provides sensor data as a service at the network edge to reduce new sensor deployments. The data from re-utilized sensors is vendor-specific, and, therefore, have heterogeneous protocols and file formats, unknown to the application vendor. A soft-hard fusion framework is proposed to handle data heterogeneity of re-utilized data and perform time-series fusion of hard-sensor data (vendor-specific or application-specific) with uncertain soft sensor data at the fog node for having complete and accurate information about the toilet. The proposed framework takes approximately 0.145 s to resolve heterogeneity, handle soft uncertainty and perform fusion with low resource consumption. Moreover, it has a good system as well as network performance with increased classification accuracy in predicting the cleaning requirement of every toilet. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fog

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Transforming Human-Machine Interaction: Generative AI Virtual Asst

Transforming Human-Machine Interaction: Generative AI Virtua...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Babu, Gotru Jeevan Safrinfathima, S. Reddy, K. Deekshith Sen, Sanjay Kumar Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350359688

By creating a sophisticated virtual assistant (VA), this study investigates how generative AI might revolutionise human-machine interaction (HMI). Our suggested solution overcomes conventional constraints by utilising cutting-edge generative models, which allow the VA to produce contextually relevant responses and adjust to a variety of user inputs. The project's goals include increasing user engagement, broadening the Virtual Assistant's scope of work, and maximising the effectiveness of human-machine interactions overall. The paper explores the technical aspects of putting generative AI into practice, emphasising its real-time understanding of user intent, context, and sentiment. We tackle issues including potential biases, ethical concerns, and the requirement for ongoing learning to guarantee the responsible and efficient use of generative AI in virtual assistant applications. By means of this study, we hope to add to the current conversation on the direction of HMI by illuminating the revolutionary potential of Generative AI in producing more perceptive, flexible, and human-like virtual assistants. © 2024 IEEE.

关键词： Chatbots

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Attention Contours: VR in ADHD Assessment 10

Attention Contours: VR in ADHD Assessment

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajarajeswari, P. Jothikumar, Karthikeyan Gomathi, V. National Engineering College Department of Computer Science and Engineering Kovilpatti India

ISBN: (纸本)9798350353068

Virtual reality (VR) technology offers potential opportunities for the assessment and intervention of Attention-Deficit Hyperactivity Disorder (ADHD). This research article presents a study evaluating the effectiveness and usability of a VR-based attention assessment tool designed to measure attention profiles across diverse populations in diverse settings. The study aims to evaluate the effectiveness and usability of the tool in healthcare settings through user trials involving participants with varying levels of VR experience. The user experiences, such as comfort levels, the incidence of motion sickness, ease of controls, immersion, sense of presence, and overall satisfaction, are evaluated in terms of VR features. Results indicated positive feedback from participants on the developed tool. These findings highlight the potential of the tool as a valuable tool for attention assessment in healthcare settings. This study accentuates the need for VR technology in healthcare to enhance attention disorder assessment and management, opening avenues for future research and implementation. © 2024 IEEE.

关键词： Virtual reality

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Fired_from_NLP at AraFinNLP 2024: Dual-Phase-BERT - A Fine-Tuned Transformer-Based Model for Multi-Dialect Intent Detection in The Financial Domain for The Arabic Language 2

Fired_from_NLP at AraFinNLP 2024: Dual-Phase-BERT - A Fine-T...

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2nd Arabic Natural Language Processing Conference, ArabicNLP 2024

作者： Chowdhury, Md. Sajid Alam Chowdhury, Mostak Mahmud Shanto, Anik Mahmud Murad, Hasan Das, Udoy Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (纸本)9798891761322

In the financial industry, identifying user intent from text inputs is crucial for various tasks such as automated trading, sentiment analysis, and customer support. One important component of natural language processing (NLP) is intent detection, which is significant to the finance sector. Limited studies have been conducted in the field of finance using languages with limited resources like Arabic, despite notable works being done in high-resource languages like English. To advance Arabic NLP in the financial domain, the organizer of AraFinNLP 2024 has arranged a shared task for detecting banking intents from the queries in various Arabic dialects, introducing a novel dataset named ArBanking77 which includes a collection of banking queries categorized into 77 distinct intents classes. To accomplish this task, we have presented a hierarchical approach called Dual-Phase-BERT in which the detection of dialects is carried out first, followed by the detection of banking intents. Using the provided ArBanking77 dataset, we have trained and evaluated several conventional machine learning, and deep learning models along with some cutting-edge transformer-based models. Among these models, our proposed Dual-Phase-BERT model has ranked 7th out of all competitors, scoring 0.801 on the scale of F1-score on the test set. ©2024 Association for Computational Linguistics.

关键词： Deep learning

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Voice assistance to blind people by detection of surrounding objects, activities, and humans

Voice assistance to blind people by detection of surrounding...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Kambham, Suhas Preetham Varman, Raahul Ramya, Tadepalli Sri Valli Eniyan, M.K. Tangavel, Senthil Kaumar Somasundaram, K. Shanmugam, Selvanayaki Kolandapalayam Department of Computer Science and Engineering Amrita School of Computing Coimbatore India Department of Mathematics and Computer Science Ashland University United States

ISBN: (纸本)9798331510299

Various systems were developed to assist those with vision impairments and enhance their standard of life. Regrettably, the majority of these systems have restricted functionality. We decided to build a model that detects all possible surrounding things and guides them through voice description. Our main objective is to detect the obstacles in the person's pathway and warn them using voice description [1]. The model can assist blind people from the surrounding obstacles which exist in their pathway. The primary goal of the model is to identify the nearby obstacles - vehicles, animals, persons, objects, etc, in the person's pathway and guide or warn them using voice description. Our proposed system integrates advanced real-time obstacle detection and voice-guided navigation using a novel combination of DepthNet module with convolutional LSTM layers, enhancing spatial context awareness compared to traditional models. This approach, along with fine-tuning on diverse datasets such as MS COCO and BDD100K, ensures robust performance in varied environments and conditions. The real-time guidance capability is further improved by the integration of optical flow algorithms, which accurately estimate motion in dynamic scenes. This comprehensive solution significantly enhances the assistive technology for those with visual impairments. © 2024 IEEE.

关键词： Obstacle detectors

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Cognitive Vision Companion: An AI-Enhanced Support System for the Visually Impaired 2

Cognitive Vision Companion: An AI-Enhanced Support System fo...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Gowthami, M. Shreya, P. Sripriya, T. Yazhini, B. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387933

In a world where a major portion of the populace suffer with daily challenges of blindness, this pioneering project introduces a comprehensive smart support system designed to aid the needs of people who are visually impaired. This advanced system is ingeniously integrated into a pair of headphones, combining various state-of-the-art technologies such as cameras, ultrasonic sensors, and cutting-edge machine learning algorithms. Its primary goals encompass recognizing objects, accurately estimating distances between objects and users, deciphering captured signs and indications, and offering real-time location-based navigation and directions. This multifunctional blind assistance device is designed to empower users by enhancing their spatial awareness and providing them the tools they need to navigate travel securely and on their own. It represents a remarkable leap forward in refining the overall quality of life and mobility for the blind people. © 2024 IEEE.

关键词： Ultrasonic cameras

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Pneumonia Detection Using Anterior Chest X-Ray Images

Pneumonia Detection Using Anterior Chest X-Ray Images

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Vallisha, M. Khot, Vishal Nagarathna, N. BMS College of Engineering Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372502

Pneumonia is a common lung infection in developing and underdeveloped countries. Analysis of anterior chest x-ray is the most commonly used method to diagnose lung infections. Existing methods provide reasonable accuracies at the price of very high model sizes. Such a high memory requirement is contrary to the primary use case of such models, which is for a preliminary check from a place with access to less resources. We propose a lightweight neural network model to detect pneumonia in anterior chest x-ray images. The proposed model is trained to detect pneumonia in chest x-ray images utilizing contrast enhancement and progressive resizing. © 2024 IEEE.

关键词： computer vision

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