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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Shiv Ashish Dhondiyal Pooja Luthra Bhupender Sharma Rajni Rajni Ishita Uniyal Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Chandigarh University Mohali Punjab Department of Computer Science and Application Coer University Roorkee Uttarakhand Department Of Computer Science & Engineering Guru Jambheshwar University of Science and Technology Hisar Haryana India Department of Electrical Engineering Graphic Era Hill University

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The number of newly diagnosed cases of breast cancer exceeds 2.3 million annually worldwide. There is a severe lack of valid prognostic and predictive indicators for the clinical treatment of breast cancer patients at the present time. In order to decrease the mortality rate and increase the survival time of breast cancer patients, early detection of this deadly disease is crucial. Improving breast cancer prognosis relies on the analysis and processing of mammography data, the gold standard for screening and early detection. In order to detect breast cancer in mammograms, the Fuzzy C-means algorithm is employed for image segmentation. Next, we train it to identify the characteristics of the segmented regions, and then we use an effective classifier to categorise the trained pictures into their respective mammography classes. This research used the Breast Cancer Wisconsin Diagnostic dataset and two ML algorithms: K-Nearest Neighbours (KNN) and Random Forest. After getting the results, we compared and evaluated the performance of these two classifiers. Potentially useful tools for both early diagnosis and treatment planning include breast cancer prediction models that account for a wide range of risk factors. Effective disease management involves the collection, storage, and administration of many data sets, as well as intelligent systems based on several aspects for the prediction of breast cancer.

关键词： Image segmentation Accuracy Machine learning algorithms Sensitivity Databases Nearest neighbor methods Prediction algorithms Breast cancer Classification algorithms Random forests

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Quick Aid: Mobile Application for Product Rental 3

Quick Aid: Mobile Application for Product Rental

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3rd International Conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Sivasangari, A. Pravin, V. Pranvesh, B. Anandhi, T. Gomathi, R.M. Ajitha, P. Sathyabama Institute of Science and Technology School of Computing Chennai India Sathyabama Institute of Science and Technology Dept of Cse Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Information Technology Chennai India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

This research study aims to design and develop Quick Aid app with an innovative idea of renting products in order to help people by supplying the essential/non-essential things. The primary objective is to create a product rental app, which enables the user to rent a product or require a service, so that the product/service can be used by the requestor. This system could also help in building a strong relationship among people and solve the problem of people not owning non-essential things readily. It is always difficult to find a product for rental or short term. Quick Aid app will make it easier to get products and services by utilizing the help of other users. Quick aid delivers simple yet innovative way to request items or services by using a simple online form from the user. The placed online request is then notified to every other user of the app, so that the interested people could help in fulfilling the request. © 2022 IEEE.

关键词： Product design

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Dengue Cases Prediction and Analysis using LSTM and Region-specific Data

Dengue Cases Prediction and Analysis using LSTM and Region-s...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Hafiz Noor Ud Deen Shafique Nauman Ali Muhammad Zunnurain Hussain Muhammad Zulkifl Hasan Muzzamil Mustafa Aqsa Khalid Rimsha Awan Farhan Ashraf Zohaib Ahmed Khan Arslan Javaid Department of Computer Engineering Information Technology University Lahore Punjab Pakistan Dept. of Computer Science Bahria University Lahore Campus Zunnurain Department of Computer Science Faculty of Information Technology University of Central Punjab Lahore Pakistan Department of Computer Science National College of Business Administration and Economics Lahore Pakistan Information Technology University Lahore Pakistan Dept. of Computer Science Bahria University Lahore Campus

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Dengue Fever presents a substantial global health challenge, necessitating the development of accurate predictive models for effective disease management. This research paper conducts a comprehensive analysis of a Long ShortTerm Memory (LSTM) neural network-based model for forecasting Dengue cases in the Philippines. The study includes data preprocessing, model development using TensorFlow and Keras, and thorough evaluation using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and Rsquared (R2). The dataset, “Dengue Cases in the Philippines,” provides a detailed historical record of Dengue fever cases and fatalities across various regions, enabling a thorough analysis of temporal and spatial dynamics. Data preprocessing involves label encoding, normalization, and careful data splitting to ensure accurate model training and testing. The LSTM neural network architecture is optimized through hyperparameter tuning, including learning rate, batch size, and epochs, to improve prediction accuracy. Model performance is rigorously evaluated, with an MAE of 913, indicating areas for further improvement. While the model shows promise, there is room for refinement. This study contributes to the field by exploring the feasibility of LSTM models for Dengue prediction, with insights that may inform future research in disease forecasting and public health strategies. Limitations, ethical considerations, and recommendations for further work are discussed.

关键词： Training Accuracy Data preprocessing Dengue fever Predictive models Data models Forecasting Long short term memory Viruses (medical) Tuning

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Design And Analysis Of Qos Based Routing Protocols For Dynamic Data Transmission In Wireless Adhoc Networks

Design And Analysis Of Qos Based Routing Protocols For Dynam...

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2022 OPJU International technology Conference on Emerging Technologies for Sustainable Development, OTCON 2022

作者： Boyana, Krishnaiah Gurrala, Venkateswara Rao Swamy, G.V. Git Gitam University Department of Computer Science and Engineering A.P. Visakhapatnam India Git Gandhi Institute of Technology and Management Dept of Electronics and Physics A.P. Visakhapatnam India

ISBN: (纸本)9781665492942

Mobility management is addressed in a novel way by recasting the problem as one of learning from existing system behavior and establishing at runtime new policies in response to varying needs. In order to capture the higher-level aims of both users and administrators, DPQOERP (Dynamic Position & Quorum Based opportunistic Energy Routing Protocol) is implemented. In this research, the OMER (optimized Mobility and Energy-based Routing) strategy to wireless network congestion control and traffic management is introduced. Using this method, networks can deploy IP (Internet Protocol) carriers and define the real world as something that is manageable from a network perspective. Using traffic data from the network, we can see that if traffic grows, dynamic data transfer throughput greatly increases. © 2023 IEEE.

关键词： Mobile ad hoc networks

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A Decision Making Model Where the Cell Exhibits Maximum Detection Probability: Statistical Signal Detection Theory and Molecular Experimental Data 57

A Decision Making Model Where the Cell Exhibits Maximum Dete...

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57th Annual Conference on Information sciences and Systems, CISS 2023

作者： Emadi, Ali Lipniacki, Tomasz Levchenko, Andre Abdi, Ali New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ United States Institute of Fundamental Technological Research Polish Academy of Sciences Warsaw Poland Yale University Yale Systems Biology Institute Dept. of Biomedical Eng. New HavenCT United States New Jersey Institute of Technology Dept. of Elect. Comput. Eng. Dept. of Biological Sciences NewarkNJ United States

ISBN: (纸本)9781665451819

Molecular noise and signaling abnormalities in biochemical signaling systems in cells affect signaling events and consequently may alter cellular decision making results. Since unexpected and altered cellular decisions may contribute to the development of many pathological conditions and diseases, it is of interest to develop proper models to characterize and measure molecular signal detection parameters and cellular decisions. In this paper and using the Neyman-Pearson signal detection theorem, we propose a signal detection model in which the cell maximizes its signal detection probability in the presence of noise. To evaluate the usefulness of the proposed model, we use measured molecular experimental data of the important TNF-NF-κB cell signaling system. Our results demonstrate that the proposed model provides biologically relevant findings. The introduced Neyman-Pearson-based molecular signal detection framework allows to systematically model and quantify the signal detection behavior and failure of molecular signaling systems, and compute their key decision making parameters such as detection and false alarm probabilities. With regard to the specific TNF-NF-κB system case study in this paper and given the high involvement of the transcription factor NF-κB in cell survival, programmed cell death, immune signaling and stress response, the developed signal detection framework can serve as a useful tool to model the associated cell decision making processes. © 2023 IEEE.

关键词： Signal detection

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COBRA: A Continual Learning Approach to Vision-Brain Understanding

arXiv

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arXiv 2024年

作者： Nguyen, Xuan-Bac Choudhary, Arabinda Kumar Sinha, Pawan Li, Xin Luu, Khoa Serna-Aguilera, Manuel Dept. of Electrical Engineering and Computer Science University of Arkansas AR United States Dept. of Radiology SUNY Upstate Medical University NY United States Dept. of Brain & Cognitive Sciences Massachusetts Institute of Technology MA United States Dept. of Computer Science SUNY Albany NY United States

Vision-Brain Understanding (VBU) aims to extract visual information perceived by humans from brain activity recorded through functional Magnetic Resonance Imaging (fMRI). Despite notable advancements in recent years, existing studies in VBU continue to face the challenge of catastrophic forgetting, where models lose knowledge from prior subjects as they adapt to new ones. Addressing continual learning in this field is, therefore, essential. This paper introduces a novel framework called Continual Learning for Vision-Brain (COBRA) to address continual learning in VBU. Our approach includes three novel modules: a Subject Commonality (SC) module, a Prompt-based Subject Specific (PSS) module, and a transformer-based module for fMRI, denoted as MRIFormer module. The SC module captures shared vision-brain patterns across subjects, preserving this knowledge as the model encounters new subjects, thereby reducing the impact of catastrophic forgetting. On the other hand, the PSS module learns unique vision-brain patterns specific to each subject. Finally, the MRIFormer module contains a transformer encoder and decoder that learns the fMRI features for VBU from common and specific patterns. In a continual learning setup, COBRA is trained in new PSS and MRIFormer modules for new subjects, leaving the modules of previous subjects unaffected. As a result, COBRA effectively addresses catastrophic forgetting and achieves state-of-the-art performance in both continual learning and vision-brain reconstruction tasks, surpassing previous methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Research Collaborator Recommendation System based on citations and Influential citations

Research Collaborator Recommendation System based on citatio...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Jagadishwari V Rohan James Rakshith Rohan Abraham Dept of Computer Science and Engineering CMR Institute of Technology Bengaluru India

Recommendation systems are tools that use various factors to suggest items or ideas to users. These systems often rely on networking and filtering, a technique that utilizes algorithms to analyze user reviews and provide personalized recommendations to users with similar preferences. In this work, a recommendation system to assist with the identification of research collaborators based on their areas of interest and published papers has been explored. With the use of the DBLP database, important attributes such as domain, and citation scores are considered to build a research collaboration system. Three different formulae are used to compute research scores (R_Scores) and the scholars are ranked based on these scores and recommendations are made on the basis of the ranks.

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Zhou, Renjie Jin, Zhenming Wan, Jian Zhang, Jilin Guo, Xingyu Liu, Qiang Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou China Hangzhou Dianzi University Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education Itmo Joint Institute Hangzhou China Zhejiang University of Science and Technology School of Information and Electronic Engineering Hangzhou China Zhejiang Police College Dept. of Computer and Information Technology Hangzhou China

ISBN: (纸本)9798350304855

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user's shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods. © 2023 IEEE.

关键词： Forecasting

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Parking management system

Parking management system

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2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2021

作者： Sathya, D. Roopa, S Nithya Sudha, V. Praveen, K. Arunkumar, A. Sanjeevan, G. Kumaraguru College of Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665428293

The main concern for vehicle owners often is to identify the proper place to park the vehicle at the earliest. In this work, a web application is created and tested, which can be used to find the availability of different parking slots in a parking lot. The web application controlled by the admin consists of various modules ranging from parking allotment to payment status. To implement this, we have used the Apache Tomcat server using the database SQL server. This system will verify the occupancy of parking slots, provide customers about the availability of parking slots in a short period and monitor occupied slots. The convenience of the person driving the vehicle is positively handled which further improves the effective time management with increased effectiveness which also paves way for less energy consumption and pollution. © 2021 IEEE.

关键词： Energy utilization

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A Deep Ensemble Learning Approach for Venomous & Non-Venomous Serpents and Insect Species Identification

A Deep Ensemble Learning Approach for Venomous & Non-Venomou...

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Innovation in technology (ASIANCON), Asian Conference on

作者： Buddhin Saroj Samarasinghe K.B.N. Lakmali Dept. of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka Department of Computer Science Informatics Institute of Technology Colombo 06 Sri Lanka

In recent years snake and insect attacks have become a huge problem worldwide. Most species have similar colors and shapes, which makes it hard to tell them apart using typical techniques. Similarly, identifying different species of bees and wasps can be challenging, especially for non-experts in the field. Therefore, developing a reliable and effective method for recognizing these animals is essential to reducing the issues caused by snake and insect attacks and supporting wildlife conservation efforts. The proposed method in this research utilizes deep ensemble learning and transfer learning techniques to identify snakes, wasps, and bees accurately. Deep ensemble learning involves combining several machine learning models to make better predictions, and transfer learning is a technique that allows pre-trained models to be re-used for new tasks, saving time and computational resources. The proposed method utilizes deep ensemble learning and uses three base transfer learning models, DenseNet201, MobileN etV2, and InceptionResN etV2, for classification. The study obtained a training accuracy of 93%. The research classified 11 types of snakes, four types of bees, and four types of wasps, including all 19 classes. Moreover, the author developed a mobile application for user interaction and utilized Flask API for the logic tier. The mobile application enables users to take pictures or upload images of a snake, wasp, or bee and identify the species accurately. This feature makes the proposed method more accessible to people who encounter these animals daily. Overall, this research represents a promising step towards developing an accurate and effective system for identifying snakes, wasps, and bees, with practical implications for human safety and biodiversity conservation.

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