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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Dubey, Krati Esnaashariyeh, Ali Nahak, Kirti Hodade, Dipali Nagnath Shrivastava, Kapil Jorvekar, Ganesh Manipal University Jaipur Department of Information Technology Jaipur India Institute of Management and Entrepreneurship Development Pune India Kalinga University Dept of Computer Science & Engineering Chhattisgarh Raipur India Shree Ramchandra College of Engineering Department of Engineering and Applied Science Maharashtra Pune412216 India Department of Computer Engineering and Applications GLA University Mathura India Sanjivani K.B.P. Polytechnic Computer Technology Kopargaon India

ISBN: (纸本)9798350330915

To improve patient care and operational effectiveness, this study investigates the implementation of big data along with artificial intelligence (AI) into medical operations. Utilizing a deductive technique and an interpretive approach, this study collects secondary data from reliable sources using a descriptive design. While AI applications concentrate on clinical decision support, tailored treatment strategies, and predictive analytics, Big Data integration includes health records, medical imaging, genome-wide data, and patient-generated information. Private data, interconnection, biases in AI systems, and resource limitations are some of the difficulties. Transparency, justice, and patient autonomy are all important ethical considerations. Strong data security protocols, continuous attempts to mitigate prejudice, interdisciplinary training, and open governance frameworks are among the suggestions offered. Future research should focus on interoperability standards © 2023 IEEE.

关键词： Big data

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AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations

AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogene...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Bomgni, Alain Bertrand Ceskoutsé, Ribot Fleury Téné Njingang, Kevin Jordan Njike Bouetou, Thomas Bouetou Gadhamshetty, Venkataramana Etienne Gnimpieba, Z. Oglala Lakota College KyleSD United States University of Yaounde 1 Ecole Nationale Supérieure Polytechnique Yaounde Cameroon University of Dschang Dept. of Computer Science Dschang Cameroon South Dakota School of Mines and Technology Dept. of Civil and Environmental Engineering Rapid CitySD United States University of South Dakota Dept. of Biomedical Engineering VermillionSD United States

ISBN: (纸本)9798350386226

The synthesis of small molecules is a crucial task across multiple scientific domains, including drug discovery, materials science, and sustainable chemistry. As AI and Machine Learning (ML) continue to advance, these technologies offer transformative potential in molecular synthesis, enhancing efficiency and expanding synthetic possibilities. In particular, small molecule synthesis has profound implications for creating novel therapeutic agents, high-performance materials, and greener chemical processes, but a major challenge remains in designing efficient synthetic routes for target molecules. Retrosynthesis, the process of mapping out synthetic pathways by working backwards from a target molecule, represents a vital step;however, traditional retrosynthesis methods often struggle to predict complex or novel transformations. To address this limitation, we introduce AIM-HKR: AI-Driven Molecular Retrosynthesis Using Heterogeneous Knowledge Representations, a model that leverages graph neural network techniques to enhance retrosynthetic predictions. AIM-HKR integrates information from heterogeneous knowledge graphs, capturing the intricate relationships and analogical reasoning required for retrosynthesis. This model generates type-specific embeddings that reflect both network topology and semantic connections across different entity types, such as molecules, reactions, and functional groups. AIM-HKR's unique capacity to leverage heterogeneous graphs allows it to propose synthetic pathways that extend beyond established precedents, enabling predictions of chemically feasible but previously uncharted transformations. We believe AIM-HKR has the potential to significantly advance molecular synthesis through AI-driven retrosynthesis, establishing a new paradigm in AI-assisted chemistry. © 2024 IEEE.

关键词： Graph neural networks

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Assessment of IT Governance Maturity Level for Yemeni Telecom Sector Using COBIT 2019 Framework 1

Assessment of IT Governance Maturity Level for Yemeni Teleco...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Ghallab, Abdullatif Alswari, Tahani Alhazbi, Saleh Al-Fuhaidi, Belal Hadwan, Mohammed Al-Fahaidy, Farouk Al-Dowail, Mohammed Maqtary, Naseebah Department of Computer Science University of Science and Technology Sana’a Yemen Department of IT Open University Malaysia Sana’a Yemen Dept. of Computer Science & Eng. Qatar University Doha Qatar Department of IT Qassim University Buraydah Saudi Arabia Department of Electrical Engineering Ibb University Ibb Yemen Department of Information Systems University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Information technology (IT) plays a crucial role in business operations most organizations rely on IT resources for gaining competitive advantages and creating innovative and continuous values. This increased the importance of enterprise IT governance (EGIT) and developed the recent version of the control objectives for information and related technology (COBIT), COBIT 2019 framework. It identifies the seven components of an effective EGIT system: processes, organizational structures, information flows and items, people, skills and competencies, policies and procedures, culture, ethics and behavior, services, infrastructure, and applications. The EGIT maturity level (ITGML) assessment helps to align the EGIT with strategic objectives. This study aims to assess the ITGML of the Yemeni telecom sector using COBIT 2019 framework. A quantitative method was applied, with a 73.6% response rate (148 out of 201 questionnaires collected). The study revealed that the current ITGML is 3.48, reflecting a defined maturity level. The findings suggested that IT governance practices based on COBIT 2019 are well documented and standardized within the Yemeni telecom sector. © 2024 IEEE.

关键词： Telecommunication industry

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

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AIML-applications for engineering & Technology (ICAET), International Conference on

作者： Pratima Patil Deepa S Deshpande Dept. of Computer Science and Engineering Jawaharlal Nehru Engineering College MGM University Chhatrapati Sambhajinagar India

ISBN: (数字)9798350355611

ISBN: (纸本)9798350355628

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time.

关键词： Deep learning YOLO Temperature sensors Accuracy Forehead Nose Face detection History Long short term memory Faces

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Visual Exploratory Data Analysis of the Covid-19 Pandemic in Nigeria: Two Years after the Outbreak 5

Visual Exploratory Data Analysis of the Covid-19 Pandemic in...

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5th International Conference on Information Technology for Education and Development, ITED 2022

作者： Orji, Ugochukwu. E. Ukwandu, Elochukwu Obianuju, Ezugwu. A. Ezema, Modesta. E. Ugwuishiwu, Chikaodili. H. Egbugha, Malachi. C. University of Nigeria Dept. of Computer Science Nsukka Enugu Nigeria Cardiff School of Technologies Cardiff Metropolitan University Dept. of Applied Computing and Engineering United Kingdom Ahmadu Bello University Dept of Computer Engineering Zaria Nigeria

ISBN: (纸本)9781509064229

The outbreak of the coronavirus disease in Nigeria and all over the world in 2019/2020 caused havoc on the world's economy and put a strain on global healthcare facilities and personnel. It also threw up many opportunities to improve processes using artificial intelligence techniques like big data analytics and business intelligence. The need to speedily make decisions that could have far-reaching effects is prompting the boom in data analytics which is achieved via exploratory data analysis (EDA) to see trends, patterns, and relationships in the data. Today, big data analytics is revolutionizing processes and helping improve productivity and decision-making capabilities in all aspects of life. The large amount of heterogeneous and, in most cases, opaque data now available has made it possible for researchers and businesses of all sizes to effectively deploy data analytics to gain action-oriented insights into various problems in real time. In this paper, we deployed Microsoft Excel and Python to perform EDA of the covid-19 pandemic data in Nigeria and presented our results via visualizations and a dashboard using Tableau. The dataset is from the Nigeria Centre for Disease Control (NCDC) recorded between February 28th, 2020, and July 19th, 2022. This paper aims to follow the data and visually show the trends over the past 2 years and also show the powerful capabilities of these data analytics tools and techniques. Furthermore, our findings contribute to the current literature on Covid-19 research by showcasing how the virus has progressed in Nigeria over time and the insights thus far. © 2022 IEEE.

关键词： Data Analytics

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Advanced Image Ciphering: 1-D Group Cellular Automata and S-Box Strategies

Advanced Image Ciphering: 1-D Group Cellular Automata and S-...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Dept. of Computational Sciences Brainware University Kolkata India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

This work explores advanced methodologies for encrypting images by utilizing one-dimensional Group Cellular Automata (1-D GCA) and S-Box techniques, with a focus on their pivotal role in ensuring the security of digital images. The proposed approach amalgamates Cellular Automata and S-Box mechanisms within a framework of symmetric-key cryptography to augment the strength of encryption. Through the generation of pseudorandom sequences using 1-D GCA and the utilization of S-Box for substitution, the method accomplishes efficient pixel diffusion and confusion. The assessment criteria demonstrate noteworthy enhancements: Number of Pixels Change Rate (NPCR) at 99.9864%, Unified Average Changing Intensity (UACI) at 33.6130%, Peak Signal-to-Noise Ratio (PSNR) at 9.2336 dB, and Mean Squared Error (MSE) at 0.1193. These findings underscore the robustness of the algorithm against statistical and differential attacks. The study justifies the promise of integrating GCA and S-Box strategies to reinforce cryptographic systems, proposing future avenues for enhancing computational efficiency and broadening applicability across diverse digital contexts.

关键词： PSNR Digital images Instruments Automata Information processing Robustness Encryption S Box Resilience Random sequences

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Comparative Study of Cancer Classification by Analysis of RNA-seq Gene Expression Levels 13

Comparative Study of Cancer Classification by Analysis of RN...

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13th International Conference on Computing Communication and Networking Technologies, ICCCNT 2022

作者： Amruth, A. Ramanan, R. Paul, Rhea Jayan, Sarada Thakur, Amrita Nidhin Prabhakar, T.V. Amrita School of Computing Dept. of Computer Science and Engineering India Dept. of Mathematics Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Dept. of Chemistry Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Cancer classification is in the spotlight of research in the medical domain;genetic inheritance plays a significant role in causing this condition. Certain similarities in DNA sequence can be prevalent in individuals who have cancer. The foremost intention of this paper is to put forth efficient cancer classification techniques that provide steady and substantial accuracy. An unconventional factor that is RNA-seq gene expression levels are considered here as opposed to familiar factors such as physical features or results of various imagery techniques. The classification is conducted by analyzing RNA-seq Gene Expression levels measured by a powerful sequencing system called the Illumina Hiseq platform. A comparative study of the application of various machine learning algorithms - Decision Tree, Random Forest, SVM, KNN, Naïve Bayes, Multinomial Regression is conducted in this paper. This method significantly reduces the computational complexity as against deep neural network approach which is used with various imagery techniques. Experimental observations suggest that SVM provides higher accuracy, cross-validation score, nearly ideal AUC-ROC curves, and better performance concerning the time required to fit the model and subsequently predict the cancer type. © 2022 IEEE.

关键词： Principal component analysis

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Predicting Customer Purchase Intent Using Logistic Regression: A Data-Driven Approach

Predicting Customer Purchase Intent Using Logistic Regressio...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： C. Rajarajachozhan G. Karthika A. Rajakrishnammal M. Mano P. Nithish Kumar E. Jayahari Dept. of Electronics and Communication Engineering Saveetha Engineering College Chennai Tamil Nadu Dept. of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu Dept. of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Predicting customer purchase intent is crucial for modern businesses in order to improve engagement, refine marketing strategies, and increase revenue. Conventional rule-based systems encounter difficulties with dynamic and extensive customer data, limiting their flexibility and precision. This project employs a machine learning methodology utilizing logistic regression to forecast customer intent based on demographic, transactional, and behavioral data. The proposed model analyses input features including age, browsing history, and previous purchases, employing sophisticated data preprocessing methods such as One-Hot Encoding and feature scaling to guarantee data integrity. Through hyperparameter tuning and the assessment of critical metrics including accuracy, precision, recall, and Fl-score, the system provides dependable and actionable insights. Real-time predictive capabilities enable businesses to customize customer interactions, facilitating targeted marketing and enhancing customer experience. This project underscores the capacity of predictive analytics to tackle real-world issues, facilitating data-driven decision-making and sustainable business development.

关键词： Logistic regression Adaptation models Accuracy Pipelines Companies Predictive models Real-time systems Data models Predictive analytics Business

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Real-Time Seed Detection and Germination Analysis in Precision Agriculture: A Fusion Model With U-Net and CNN on Jetson Nano

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2023年第2期1卷 145-155页

作者： Donapati, Ramesh Reddy Cheruku, Ramalingaswamy Kodali, Prakash National Institute of Technology Warangal Department of Electronics and Communication Engineering Warangal506004 India Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering & Technology Dept. of Ece Hyderabad500090 India National Institute of Technology Warangal Department of Computer Science and Engineering Warangal506004 India

Precision agriculture involves the strategic utilization of resources, precise application of inputs, and continuous monitoring of crop health with the aim of enhancing productivity and sustainability in the field of agriculture. However, seed quality is difficult since natural differences among seed batches may affect germination rates, vigor, and crop performance. Hence, in this article, a novel fusion model for seed detection and germination is proposed. The proposed model combines the U-Net and CNN architectures for seed segmentation and classification, respectively. By harnessing U-Net's capabilities in image segmentation and CNN's strengths in classification, the proposed approach enables effective seed germination analysis. In addition, the model is specifically optimized for real-time processing and applications by implementing it on the NVIDIA Jetson Nano embedded GPU platform. The proposed fusion model achieved 0.91 pixel accuracy, 0.84 intersection over union, and 0.90 precision. The proposed model demonstrated excellent predictive ability when compared with the ResNet50, Inception, and LeNet. In addition, the proposed model requires less number of trainable parameters after LeNet. Further, the proposed model tested in real time and achieved 0.26 ms latency. © 2023 IEEE.

关键词： Crops

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Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity and Lightweight Models with Knowledge Distillation 17

Resource-Aware Monkeypox Diagnosis: Leveraging High-Capacity...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Raha, Avi Deb Gain, Mrityunjoy Saha, Sudip Kumar Rahman, Md Shohanur Adhikary, Apurba Debnath, Rameswar Bairagi, Anupam Kumar Biswas, Sujit Khulna University Khulna Bangladesh AI Innovation Lab Khulna Bangladesh United International University Dhaka Bangladesh KUET Dept. of Mechatronics Engineering Khulna Bangladesh Noakhali Science and Technology University Dept. of ICE Noakhali Bangladesh City University of London Computer Science Department EC1V 0HB United Kingdom

ISBN: (纸本)9798331531195

Monkeypox, a novel zoonotic disease akin to smallpox, necessitates prompt and accurate diagnosis for effective treatment. Conventional diagnostic techniques, such as Polymerase Chain Reaction (PCR), offer high precision but require specialized equipment and trained personnel, rendering them impractical in resource-limited settings. Existing deep learning approaches for monkeypox diagnosis have predominantly relied on single, resource-intensive models, prioritizing accuracy over deployment feasibility across diverse computing platforms. In this study, we present a resource-conscious model deployment strategy that balances diagnostic accuracy with computational efficiency, enabling precise diagnosis in both resource-rich environments, such as hospitals, and resource-constrained contexts. We utilize a pretrained ConvNeXt-B model, trained on the extensive ImageNet-22K dataset, for deployment in resource-abundant scenarios, and a SqueezeNet model, optimized for resourcelimited devices using the ImageNet-1K dataset. To enhance the performance of the lightweight SqueezeNet model without increasing computational complexity, we apply Knowledge Distillation. Experimental results demonstrate that the ConvNeXt-B model achieves an accuracy of 95.75%, which is 3.47% higher than the previous studies. Similarly, the Knowledge Distillation-enhanced SqueezeNet model attains an accuracy of 91.89%, representing a 2.32% improvement over the baseline. This dualmodel approach ensures that accurate monkeypox diagnostics are accessible across a wide range of computational environments, thereby supporting more effective outbreak management and contributing to improved public health outcomes. © 2025 IEEE.

关键词： Distillation equipment

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