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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Varshini Devi, I. Oviya, I.R. Raja, Kalpana Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Medicine Yale University Section for Biomedical Informatics and Data Science New HavenCT United States

ISBN: (纸本)9798350344837

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios. © 2024 IEEE.

关键词： Emotion Recognition

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Explainable Artificial Intelligence to Enhance Data Trustworthiness in Crowd-Sensing Systems 19

Explainable Artificial Intelligence to Enhance Data Trustwor...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Afzal-Houshmand, Sam Papamartzivanos, Dimitrios Homayoun, Sajad Veliou, Entso Jensen, Christian D. Voulodimos, Athanasios Giannetsos, Thanassis Cyber Security Section Denmark Ubitech Ltd. Digital Security and Trusted Computing Group Greece University of West Attica Department of Informatics and Computer Engineering Athens Greece School of Electrical and Computer Engineering National Technical Univ. of Athens Greece

ISBN: (纸本)9798350346497

Around the world there has been an advancement of IoT edge devices, that in turn have enabled the collection of rich datasets as part of the Mobile Crowd Sensing (MCS) paradigm, which in practice is implemented in a variety of safety critical applications. In spite of the advantages of such datasets, there exists an inherent data trustworthiness challenge due to the interference of malevolent actors. In this context, there has been a great body of proposed solutions which capitalize on conventional machine algorithms for sifting through faulty data without any assumptions on the trustworthiness of the source. However, there is still a number of open issues, such as how to cope with strong colluding adversaries, while in parallel managing efficiently the sizable influx of user data. In this work we suggest that the usage of explainable artificial intelligence (XAI) can lead to even more efficient performance as we tackle the limitation of conventional black box models, by enabling the understanding and interpretation of a model's operation. Our approach enables the reasoning of the model's accuracy in the presence of adversaries and has the ability to shun out faulty or malicious data, thus, enhancing the model's adaptation process. To this end, we provide a prototype implementation coupled with a detailed performance evaluation under different scenarios of attacks, employing both real and synthetic datasets. Our results suggest that the use of XAI leads to improved performance compared to other existing schemes. © 2023 IEEE.

关键词： Time series analysis

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Omega-3 in the Subjective Betterment of Mood and Sleep Disturbances Induced by Hormone Therapy in Women with Breast Cancer: A Pilot, Randomized, Double-Blinded, Placebo-Controlled Clinical Trial

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Sleep and Vigilance 2024年第1期8卷 39-47页

作者： Nourozi, Behzad Naghsh, Erfan zadeh, Sahar Esmaeil Mehrzad, Valiollah Darakhshandeh, Ali Moghaddas, Azadeh Department of Clinical Pharmacy and Pharmacy Practice Faculty of Pharmacy Isfahan University of Medical Sciences Isfahan Iran Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada Department of Internal Medicine Oncology and Hematology Section Faculty of Medicine Isfahan University of Medical Sciences Isfahan Iran

Background: This study was performed to investigate the effect of omega-3 administration on the treatment of mood and sleep problem induced by menopausal complications due to hormone therapy in patients with breast cancer. Methods and Patients: A pilot, randomized, double-blinded and placebo-controlled clinical trial was designed in oncology-hematology outpatient’s clinic of Omid Hospital, Isfahan, Iran. First, 60 patients were screened by Hospital Anxiety and Depression Scale (HADS) for any non-identified baseline mood problems and then divided into either intervention group which received 2 g’ omega-3 daily for 4 weeks or placebo group which received identical supplement. Then, the patients were considered to assess by center for epidemiological studies—depression scale (CES-D), profile of mood states (POMS), and Pittsburgh sleep quality index (PSQI) questionnaires at the baseline and after 4-week follow-up. Results: The mean scores of CES-D (P = 0.002), POMS (P = 0.03), and PSQI (P = 0.04) were significantly lower in the intervention group than the control group. In the intervention group, mean scores of CES-D (P < 0.001), POMS (P < 0.001), and PSQI (P = 0.003) were significantly lower after intervention than pre-intervention. Mean changes in scores of CES-D (P = 0.01), POMS (P = 0.001), and PSQI (P = 0.02) were significantly higher in the intervention group than the control group. Conclusion: Our findings revealed that omega-3 supplementation have the potential to reduce mood problems complications induced by hormone therapy in breast cancer patients, and it could also improve sleep quality items in terms of subjective sleep quality, delay in falling asleep, sleep delay, and sleep disturbance. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2023.

关键词： Breast cancer Mood problem Omega-3 Sleep quality

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Tomato Disease Classification Using CNN 4th

Tomato Disease Classification Using CNN

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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Archanaa, N. Daniel, V. Vifert Jenuben Divya, S. Raja, Kalpana Oviya, I.R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Sciences Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New HavenCT United States

ISBN: (纸本)9789819736898

Plant disease detection using various machine learning techniques is a very popular research area. Many promising results have been obtained, but only a few specific applications can still make farmers’ lives easier. This research focuses on the important task of identifying tomato diseases at an early stage to prevent their spread and protect crop yields more broadly, emphasizing the idea that disease prevention will more important than treatment. Leveraging the capabilities of convolutional neural networks (CNN), we developed a model that diagnoses eight distinct tomato diseases using only their images. Diseases listed include bacterial spot, early blight, late blight, leaf mold, septoria leaf spot, two-spotted spider mite, target spot, and tomato yellow leaf curl virus. An integral part of our approach involves sophisticated image prepossessing and enhancement techniques, ensuring data quality and model robustness. Feature extraction plays a key role in transforming raw image data into recognizable patterns. The architecture of the CNN has been meticulously designed, consisting of multiple layers with a notable focus on the ReLU activation function and other relevant hyper-parameters. Our main dataset comes from PlantVillage, known for synthesizing rich and diverse plant images. After rigorous training and validation, the CNN model showed exceptional accuracy of 96.88% in tomato disease identification. However, challenges such as overfitting, image quality variability, and class imbalances were encountered but were resolved through strategic data augmentation, careful CNN overlay. In summary, our research presents a practical solution for farmers and agricultural entities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

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computers, Materials & Continua 2022年第7期72卷 227-242页

作者： Murad A.A.Almekhlafi Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a University61101Yemen Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be *** the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their ***,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’*** have designed MATLAB simulation and programming programs by Visual *** for a semi-realistic communication *** includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving *** we verified the correctness of the system’s ***,we begin by analyzing and studying multiple and branching cases to achieve the *** get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

关键词： Line of sight obstacle margin wireless networks communication link radio wave propagation

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Attribute Selection, Outliers Impact Study and Visualization within Breast Cancer Detection 13

Attribute Selection, Outliers Impact Study and Visualization...

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13th IEEE International Conference on Electronics and Information Technologies, ELIT 2023

作者： Chuiko, Gennady Dvornik, Olga Darnapuk, Yeugen Honcharov, Denis Krainyk, Yaroslav Yaremchuk, Olga Computer Engineering Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Ukraine Section Computer Engineering Department Petro Mohyla Black Sea National University Mykolayiv Ukraine Information computer center Petro Mohyla Black Sea National University Mykolayiv Ukraine Medical Institute Petro Mohyla Black Sea National University Mykolayiv Ukraine

ISBN: (纸本)9798350383096

Classification of mammography data into two types of breast tumors, benign or malignant, is an effective screening tool and the primary way of diagnosis and decision-making. This report aims to opt for the most relevant attributes of the well-known Wisconsin Breast Cancer Diagnostic Data Set to reduce its size at first. The reduction was performed initially by ranking attributes and finally by "decision tree" analysis. The clipped data set had only six attributes, against 31 in the initial one. The five most relevant attributes were the following: "perimeter_worst," "area_worst," "concave points_worst," "texture_mean," and "concave points_mean." If possible, It should be done without losing classification potency. Over and above, our extra goal was to find classifiers that provide acceptable performance while allowing visualization of the results in a way accessible to clinicians. Here, we mean various visualization tools in the Machine learning framework: "decision trees," association rules, attribute ranking, and so forth, to improve breast cancer diagnosis. © 2023 IEEE.

关键词： Decision trees

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Impact of diameter on D-shaped POF sensors for salt detection

Impact of diameter on D-shaped POF sensors for salt detectio...

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International Laser Technology and Optics Symposium 2024, iLATOS2024

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. bte Saidin, Norazlina Noor, Ahmad Shukri Muhammad Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala lumpur53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Instrumentation and Control Engineering Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia

This study investigates the relationship between the diameter of a D-shaped plastic optical fiber (POF) sensor and its optical response as a function of varying salt concentrations. A simple and efficient dry etching polishing technique was employed to create D-shaped POF sensors with a range of diameters. All the fabricated sensors were carefully developed to maintain a consistent length of 20 cm with a sensing region of 1cm. To evaluate the optical intensity response, each sensor, with its respective diameter, was examined with the salt concentration ranging from (0 to 5%) examination utilizing a spectrometer. The sensor response was characterized by quantifying the variations observed in the intensity of the optical signal propagating through the sensing region. The sensors having a diameter of 500 μm, 700 μm, and 900 μm had a sensitivity of 149 A.U/%salt, 66 A.U/%salt, and 11.2 A.U/%salt respectively. The sensitivity of the 500 μm diameter sensor increased 13 times approximately as compared to the sensor having diameter of 900 μm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Dry etching

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Novel intelligent predictive networks for analysis of chaos in stochastic differential SIS epidemic model with vaccination impact

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Mathematics and computers in Simulation 2024年 219卷 251-283页

作者： Anwar, Nabeela Ahmad, Iftikhar Kiani, Adiqa Kausar Shoaib, Muhammad Raja, Muhammad Asif Zahoor Department of Mathematics University of Narowal 51600 Pakistan Department of Mathematics University of Gujrat 50700 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Yuan Ze University AI Center Taoyuan320 Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Attock43600 Pakistan

In this paper, stochastic predictive computing networks are exploited to investigate the dynamics of the SIS with vaccination impact based epidemic model (SISV-EM) represented by nonlinear systems of stochastic differential equations (SDEs) by exploitation of artificial neural networks (ANNs) with the backpropagated Levenberg-Marquardt technique (BLMT) i.e., (ANNs-BLMT) to approximate the solution behavior. The stochastic nonlinear SISV-EM is governed with three classes: susceptible, infectious, and vaccinated populations. The referenced or target datasets for ANNs-BLMT are constructed by employing Euler-Maruyama (EM) scheme for solving stochastic differential systems in case of sufficiently various nonlinear SISV-EM scenarios by varying the percentage of vaccination for newly born, the coefficient of transmission, the natural mortality rates, the infectious rates of recovery, the rate at which vaccinated people lose their immunity, the rate of death caused by disease, the proportion of vaccinated against susceptible and the white noise in the environment. Based on arbitrary training, testing, and validation samples from the referenced dataset, the ANNs-BLMT provides an approximate solution for the stochastic nonlinear SISV-EM, with significant correlations to the referenced results. Exhaustive simulation-based results using error histograms, mean square errors, and regression analyses further demonstrate that the proposed ANNs-BLMT is efficient, consistent, and accurate for solving SISV-EM. © 2023 International Association for Mathematics and computers in Simulation (IMACS)

关键词： Mean square error

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