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Predictive Insights Beyond Boundaries: Integrating Clinical Data for Hepatitis C Forecasting Using Machine Learning and Deep Learning

Predictive Insights Beyond Boundaries: Integrating Clinical ...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Fatema Tuj Jenin Al-Amain Khandaker Mohammad Mohi Uddin Faizul Ahmed Robin Md. Ferdous Bin Hafiz Dept. of Computer Science and Engineering Southeast University Dhaka Bangladesh Dept. of Information and Electrical Engineering Hamburg University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A person's well-being and wellness are seriously hampered by the chronic diseases' lasting implications and recurrent signs. A startling 58 million people worldwide suffer from an incurable chronic form of hepatitis C, disease is brought about by a virus called hepatitis C (HCV). Additionally, it results in 290,000 deaths every year from cirrhosis and liver cancer, both of which linked to HCV. The goal of this venture is to reliably identify and anticipate hepatitis C utilizing Machine Learning (ML) and deep Learning (DL) techniques. It does this by utilizing ML and DL algorithms' capacity to scan big datasets and spot patterns and associations that help with diagnosis. Predictive models were created utilizing blood values from 615 patients, including both hepatitis patients and healthy blood donors, using a variety of approaches comprising SVM, Logistic regression (LR), KNN, LSTM and others. The dataset underwent extensive preprocessing, which included managing missing values, scaling, and diminished dimensionality using Principal Component Analysis (PCA). After a variety of ML along with the SVM classifier showed the best result, giving an accuracy concerning 98.33%, recall concerning 94.17%, precision concerning 93.99%, and F1-score concerning 94.16%.

关键词： Support vector machines Deep learning Accuracy Liver diseases Predictive models Reliability Long short term memory Viruses (medical) Medical diagnostic imaging Principal component analysis

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Facial Biometrics-Driven Feature Encoding and Deep Learning Integration for Secure Password Retrieval

Facial Biometrics-Driven Feature Encoding and Deep Learning ...

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Disruptive Technologies (ICDT), International Conference on

作者： Akash Immanuel Solomon A Ashwin M Swaminathan K Dept. of Computer Science St. Joseph's College of Engineering Chennai India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

The proposed system includes face recognition-based systems designed for secure identity verification and decentralized data storage. The working is supposed to be done in three phases: data capture, training of the machine learning model, and decentralized data storage. Collection in the first phase will include face images of individuals and government-issued identification details, such as Aadhar and PAN. A machine learning model will be trained in the second phase for improving the accuracy in the face recognition process. The third phase ensures secure storage of the recognized faces and associated personal data through a decentralized approach similar to blockchain technology. Security, privacy, and resilience against tampering are further developed in this design. The proposed system will reliably meet the application requirements that involve strong authentication processes and protection of data, therefore making the sectors using these systems include government services and secured personal data management.

关键词： Training Privacy Face recognition Government Memory Passwords Reliability Protection Secure storage Resilience

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Sensing Together: Cooperative Task Adaptation and Scheduling for IoT-Nets Using Renewable Energy

Sensing Together: Cooperative Task Adaptation and Scheduling...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Elizabeth Liri K. K. Ramakrishnan Koushik Kar Flavio Esposito Dept. of Computer Science and Engineering University of California Riverside Riverside CA USA Computer Science Dept. Saint Louis University St. Louis MO USA Electrical Computer and Systems Engineering Dept. Rensselaer Polytechnic Institute Troy NY USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

IoT devices used in various applications, such as monitoring agricultural soil moisture, or urban air quality assessment, are typically battery-operated and energy-constrained. We develop a lightweight and distributed cooperative sensing scheme that provides energy-efficient sensing of an area by reducing spatio-temporal overlaps in the coverage using a multi-sensor IoT network. Our “Sensing Together” solution includes two algorithms: Distributed Task Adaptation (DTA) and Distributed Block Scheduler (DBS), which coordinate the sensing operations of the IoT network through information shared using a distributed “token passing” protocol. DTA adapts the sensing rates from their “raw” values (optimized for each IoT device independently) to minimize spatial redundancy in coverage, while ensuring that a desired coverage threshold is met at all points in the covered area. DBS then schedules task execution times across all IoT devices in a distributed manner to minimize temporal overlap. On-device evaluation shows a small token size and execution times of less than 0.6s on average while simulations show average energy savings of 5% per IoT device under various weather conditions. Moreover, when devices had more significant coverage overlaps, energy savings exceeded 30% thanks to cooperative sensing. In simulations of larger networks, energy savings range on average between 3.34% and 38.53%, depending on weather conditions. Our solutions consistently demonstrate near-optimal performance under various scenarios, showcasing their capability to efficiently reduce temporal overlap during sensing task scheduling.

关键词： Performance evaluation Schedules Renewable energy sources Simulation Redundancy Soil moisture Smart systems Sensors Internet of Things Meteorology

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Enhancing Women's Safety Through Customized Smartphone Sensor Data: A Human Activity Recognition System 9

Enhancing Women's Safety Through Customized Smartphone Senso...

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9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Islam, Yamina Nowrin, Sanjida Faqih, Majed Ibrahim Sultana, Fozia Bahadur, Erfanul Hoque Masum, Abdul Kadar International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram Bangladesh Signal Corps Institute Director of Research and Development Saudi Arabia Hiperdyne Artificial Intelligence Engineer Tokyo Japan Daffodil International University Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9798350319651

Human activity recognition (HAR) research using smartphone sensor technology has changed people's daily lives and opened the door for numerous exciting applications due to its extensive applications in addressing real-world humancentric issues. These studies, however, mostly concentrated on a woman's daily activities and behaviours when she is attacked, such as running, walking, sleeping, eating, drinking, lying, straight punching, front kicking, and so forth. The article offers a method to foresee human behaviour using a deep learning model and evaluates the efficiency of the approach using real-world data in response to this. We offer an LSTM structure for detecting the intensity of HAR in order to track activity patterns and build a neural network with binary classification skills to evaluate the situation of women. The proposed model uses the dataset to identify human activity, which records 30 volunteers' body movements while they engage in 18 physical activities. Feature engineering is used to extract additional variables from the data in addition to that. Our proposed model achieves a 90.89% accuracy. © 2023 IEEE.

关键词： Smartphones

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Sentiment Analysis in Greek Clinical Conversations: A Comparative Study of BERT, VADER, and Lexicon Approaches

Sentiment Analysis in Greek Clinical Conversations: A Compar...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chatzimina, Maria Evangelia Oikonomou, Nikolaos Papadaki, Helen Tsiknakis, Manolis Pontikoglou, Charalampos Institute of Computer Science Crete Heraklion Greece Hellenic Mediterranean University Dept. of Electrical and Computer Engineering Crete Heraklion Greece School of Medicine University of Crete Department of Hematology Crete Heraklion Greece

ISBN: (纸本)9798350337488

Sentiment analysis, a subfield of Natural Language Processing (NLP), focuses on the study of emotions expressed in text data. While extensively used in major languages such as English, its application in Greek remains underexplored. We investigate sentiment analysis in Greek clinical dialogues, focusing on hematologic malignancies. These dialogues offer valuable insights into patient experiences, healthcare provider approaches in palliative care, and underscore the critical role of sentiment analysis in accurately assessing patients' emotional status for effective care. Our study compares three methodologies - Greek Lexicon, VADER, and BERT - while addressing the limited Greek resources in healthcare-related NLP. Key findings indicate that BERT outperformed other methods by achieving well-balanced precision and recall in assessing sentiment. The Lexicon-Based approach encountered challenges in identifying both negative and positive sentiments, while VADER showcased robust results. Our study involves data collection, data annotation, model training, and performance evaluation. We aim to address the research gap and the unique challenges of sentiment analysis in Greek, especially in context of hematologic malignancies, where NLP and sentiment analysis resources are limited. © 2023 IEEE.

关键词： clinical dialogues hematologic malignancies natural language processing palliative care sentiment analysis

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Distributed IRSs Mitigate Spatial Wideband & Beam Split Effects

Distributed IRSs Mitigate Spatial Wideband & Beam Split Effe...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： L. Yashvanth Chandra R. Murthy Bhaskar D. Rao Dept. of Electrical Communication Engineering Indian Institute of Science Bangalore India Dept. of Electrical and Computer Engineering University of California San Diego USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We address the problem of the beam split (B-SP) caused by the spatial wideband (SW) effect in intelligent reflecting surface (IRS) aided wideband systems. The SW effect arises when the signal delay across the IRS aperture is comparable to the system sampling time. This leads to a B-SP, wherein the IRS phase shifters fail to form a beam at the desired user equipment (UE) over the complete bandwidth (BW) allotted to that UE, in turn reducing the system throughput. This paper proposes a distributed IRS design that aids in naturally combating the SW/BSP effects by parallelizing the spatial delays across the multiple IRSs. Specifically, we propose to split a single large IRS into multiple smaller IRSs and distribute them over the geographical area. Then, by specifying the maximum permissible number of elements at each IRS, we ensure that the B-SP remains within a specified tolerance level. Finally, we derive the sum-rate and show that the proposed solution yields a nonzero rate over the whole BW of operation and circumvents the SW/B-SP effects. We verify our findings numerically and illustrate the low complexity of the proposed method compared to state-of-the-art techniques.

关键词： Array signal processing Phase shifters Apertures Throughput Acoustics Delays Complexity theory Acoustic beams Speech processing Wideband

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TotCareBot: A Comprehensive Approach Towards Enhancing Early Childhood Education through Robotics

TotCareBot: A Comprehensive Approach Towards Enhancing Early...

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International Conference on Circuits, Communication, Control and Computing

作者： Manjula R Chougala Sushma B Akshata S Bhayyar Abhay Bhandarkar P N Pavithra Dept. of Computer Science and Engineering Ramaiah Institute of Technology Bangalore India Department of Computer Science Engineering Ramaiah Institute of Technology Bengaluru India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Robots have emerged as versatile tools with significant potential to enhance teaching and learning environments, including classrooms, laboratories, play homes, crèches, and even at home. Their engaging nature can captivate young children, boosting motivation and interest. This paper proposes a methodology for an Educational Robot (ER) designed for play homes. The ER is capable of moving around the classroom, monitoring children, analyzing their emotions, responding appropriately, and alerting the teacher when necessary. The proposed system aims to leverage the positive impact of expressive behaviors in robots, such as storytelling and pampering. Our findings indicate that expressive robot narration is as effective as that of a human displaying similar behaviors and even surpasses a static, inexpressive human narrator. Furthermore, the robot excels in conveying educational content, although its limited emotional expression slightly reduces its effectiveness in narrating emotionally rich stories. This research underscores the potential of ERs to create dynamic and interactive learning experiences for young children, fostering both their cognitive and emotional development.

关键词： Educational robots Education Laboratories Circuits Boosting Behavioral sciences Robots Erbium Monitoring

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Ensemble Approaches for Offensive Language Detection in Kannada

Ensemble Approaches for Offensive Language Detection in Kann...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Monish M N Kavitha Sooda Dept. of Computer Science & Engineering B.M.S. College of Engineering Bengaluru India

In the realm of natural language processing, sentiment analysis remains a focal point due to its pivotal role in discerning and analyzing public sentiments. While past research has been dedicated to enhancing the performance of sentiment analysis models, this study delves into the potential benefits of ensemble techniques, with a special emphasis on Genetic Algorithm (GA) optimization, The main thrust of this research was to investigate whether merging multiple models and subsequently optimizing their weights via GA could elevate performance. Evaluations were thoughtfully conducted, focusing on the weighted precision, recall, and F1-scores of individual models. Additionally, the study compared the ensemble methods, both with and without GA optimization, The ensemble method, even without the GA optimization, secured an impressive F1-score of 0.73. Upon integrating the GA optimization, there was a gentle improvement in the ensemble’s performance, achieving an F1-score of 0.75, In summing up, this study manifests a palpable enhancement in sentiment analysis outcomes when shifting from standalone models to a GA-optimized ensemble mechanism. The results, epitomized by an F1-score peaking at 0.75, underline the intrinsic merits of harnessing GA for weight optimization in ensemble constructs.

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Smart ANPR: A Checkpoint-Based Toll and Parking Management System Using Hybrid OCR and Blockchain

Smart ANPR: A Checkpoint-Based Toll and Parking Management S...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Samarth M N Sampriti Gopisetti Chaitra M Dept. of Computer Science and Engineering BNM Institute of Technology Affiliated to VTU Bangalore India

ISBN: (数字)9798331515911

ISBN: (纸本)9798331515928

Automated Number Plate Recognition is one of the most important applications of computer vision, employed in the enforcement of traffic law, vehicular management, security, and even at toll collection. This paper proposed an advanced system for ANPR that can move away from the traditional booth and establish a checkpoint. The strategically located cameras with checkpointing will automatically capture the plates of passing vehicles, permitting road tax and parking fees to be deducted without making contact. A hybrid approach that combines the capabilities of OpenCV, Tesseract OCR, and deep-learningbased OCR models helps tackle such challenges as stable images from live video feeds and highly accurate text recognition. To this end, since the system integrates high-resolution frame stabilization using OpenCV to ensure crystal clear captures, it further employs the use of YOLOv8 to boost the precision of real-time detection. The blockchain technology is further implemented to ensure that transactions are tamper proof, therefore ensuring secure and transparent billing systems. This makes the ANPR solution deployable on edge devices, which opens real-time processing capabilities for high-speed and high-volume environments, thereby offering a scalable and efficient approach for intelligent transportation and smart city applications.

关键词： computer vision Technological innovation Accuracy Smart cities Text recognition Transportation Real-time systems Blockchains Security License plate recognition

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Fake Product Identification System

Fake Product Identification System

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Emerging Technology (INCET), International Conference for

作者： B. Rajalakshmi Jayashree Anushka Dharan Jainam Jain Shivswami Chavan Dept. of CSE New Horizon College of Engineering Bengaluru India Dept. of Computer Science and Engineering New Horizon College of Engineering Bengaluru India

ISBN: (数字)9798350361155

ISBN: (纸本)9798350361162

The largest issue facing the retail industry today is product counterfeiting. Products that are counterfeit are just poor copies of authentic brands. Various techniques have been implemented to fight product counterfeiting, including RFID tags, machine learning, artificial intelligence, QR code-base systems, and many more. However, these techniques have drawbacks as well. For example, a QR code can be used to distinguish a real product from a fake one, artificial intelligence and machine learning require a lot of processing power to perform tasks, and numerous other techniques have been adopted but no workable solution has yet been created. With the aid of blockchain technology, we have attempted to enhance the detection of counterfeit goods in this *** approach involves using a QR code to store the product supply chain at each stage of the product's transaction with a new party. One of the key benefits of blockchain is that once data is recorded in the system, it cannot be changed by anybody, making our data more secure and protected from outside parties. This makes blockchain useful for storing product supply chains since it creates a decentralized system.

关键词： Reviews Supply chains QR codes Machine learning Blockchains History Security

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