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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： M. Imrankhan M. Viji R. Sathishkumar T. Gnanaprakash R. Karthik Roshan G. Saravanan Dept. of CSE Manakula Vinayagar Institute of Technology Puducherry Dept. of Computer Science and Business Systems Sri Manakula Vinayagar Engineering College Puducherry

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

This can be one of the most magnificent advantages realized from applying blockchain technology in a forensic investigation context, namely, a transformative approach to address challenges embedded within legal and criminal justice systems. For example, smart contracts, being self-executing agreements with the automated inclusion of predefined rules, may even automate and secure the process of tracking evidence, managing chain of custody, and access control. This smart contract will ensure that forensic operations are transparent and of sound integrity. Every transaction and action performed on the blockchain is recorded there, instantaneously bringing down tampering to the lowest possibility and any chance for human mistakes. Regarding decentralized nature and cryptography-based security combined with immutability, blockchain is a technology known to have safeguards over sensitive data. Once recorded on the blockchain, the information thereupon becomes completely un-changeable, assuring the reliability and authenticity of evidence. This will be especially important in the management of an unforgeable chain of custody and in ensuring that forensic evidence remains unchanged and can be relied on with integrity throughout the court process.[6] Critical data can then be maintained tamper-proof for management and storage by developing Ethereum-based smart contracts on Remix IDE using Solidity.[15]Each transaction will be time-stamped and safely logged on the Ethereum blockchain, thus developing an unchallenged ledger that makes any investigation that much more trustworthy. In a nutshell, the application of blockchain in forensic activities builds greater control and efficiencies, reduces possible errors and conflicts, and provides transparency, integrity, and an unchangeable, permanent record for forensic processes. The invention thereby significantly upgrades the general integrity and confidence in legal and criminal justice inquiries.

关键词： Technological innovation Forensics Smart contracts Digital forensics Process control Market research Blockchains Reliability Protection Standards

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Experimental Study on Speech Synthesis Using Advanced Neural Network Models

Experimental Study on Speech Synthesis Using Advanced Neural...

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African Federation of Operational Research Societies (AFROS), International Conference of the

作者： Freha Mezzoudj Ahmed Slimani Medjahed Chareddine Narimane wafa Krolkral Dept. of Informatic systems engineering National Polytechnic School of Oran Oran Algeria Dept. of Computer science Univrsity of Adrar Adrar Algeria Dept. of Computer science Hassiba Benbouali Chlef university Chlef Algeria

ISBN: (数字)9798350386448

ISBN: (纸本)9798350386455

In this paper, the authors conduct an experimental work on neural networks Text-to-Speech (TTS), aiming to facilitate an appropriate understanding of current research and future tendencies in the field. Four cascading systems to generate an English Speech Synthesis model are used and compared. We focus on the key components in neural TTS, including text processing, acoustic-phonetics models, and speech generators - vocoders. This study is useful for industry practitioners and academic researchers interested in TTS. The experimental setup considers the following TTS models: Tacotron2 and Fastspeech2 acoustic models, both of which are used with MelGAN and Multiband MelGAN vocoders. The parameters' models are trained and tuned on the English LJSpeech dataset. Performance metrics used are WER objective and MOS subjective metrics. The mean opinion score (MOS) as subjective metrics is based on naturalness and intelligibility measures. The Results show that, in our case, the best performance is achieved using Fastspeech2 combining with Multi Band-Melgan model achieved/Comprehensibility of 4.25 and Naturalness of 4 which is close to performance of commercial systems.

关键词： Deep learning Industries Vocoders Neural networks Visual impairment Data preprocessing Generators Data models Acoustics Text to speech

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Advancements in Dental Diagnostics: YOLOv3-Powered Machine Learning for Automated Teeth Problem Detection

Advancements in Dental Diagnostics: YOLOv3-Powered Machine L...

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Information Technology (ACIT)

作者： Suhail Odeh Anas Samara Nicola Zreineh Seif Obeid Maen Ibrieghieth Mahmoud Obaid Software Engineering Dept. Bethlehem University Palestine Computer System Engineering Arab American University Jenin Palestine

This paper presents a rigorous investigation into the application of state-of-the-art machine learning techniques for the automated detection of dental issues, utilizing the YOLOv3 Algorithm, a cutting-edge one-stage object detection method. The study encompasses the meticulous annotation and utilization of a carefully curated dataset featuring 126 panoramic X-ray images, illustrating a diverse range of dental conditions. Among these conditions, the primary emphasis is placed on the precise detection of six specific dental problems. Employing advanced computer vision methodologies, the model demonstrates exceptional accuracy in the identification and precise localization of these targeted dental issues. The implications of these findings are profound for the field of dental healthcare, as the automated detection of dental problems holds the potential to significantly enhance the diagnostic and treatment planning capabilities of dental professionals. The results presented in this research represent a notable stride in the ongoing evolution of machine learning and underscore its capacity to revolutionize the landscape of dental diagnostics, ultimately contributing to the advancement of oral health management and patient care.

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Cloud-Based Framework for Data Exchange to Enhance Global Healthcare

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Procedia computer Science 2024年 241卷 570-575页

作者： Ammerha Naz Muhammad Ali Sehrish Munawar Cheema Ivan Miguel Pires Dept. of Computer Science University of Sialkot Sialkot Punjab Dept. of Software Engineering Superior University Lahore Pakistan Dept. of Computer Science University of Management and Technology Lahore Pakistan Instituto de Telecomunicações Escola Superior de Tecnologia e Gestão de Águeda Universidade de Aveiro Águeda Portugal

Healthcare is a global pillar, with a surge in the adoption of information technology, particularly in hospital information systems (HIS). However, global protocols are needed to meet the growing demand for data interchange, practical implementations for sharing healthcare data among facilities, and a pressing need for processing and storage infrastructure to handle the escalating volume of healthcare data. This study proposes a solution for efficient data transmission using electronic health records (EHR) and Platform-as-a-Service (PaaS) to leverage cloud computing resources. This framework's architecture boasts robustness and adaptability, providing all registered software programs access to data interchange services. Through a comprehensive examination of the framework's structure, the essay also explores the most effective data-sharing methods. It identifies the healthcare system's optimal EHR data model. According to multiple healthcare experts, the operational building of this framework is expected to catalyze the growth of healthcare institutions both nationally and within specific industries.

关键词： Healthcare Hospital information systems (HIS) Electronic health records (EHRs) Cloud computing. Data transmission

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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Advancements in Computing (ICAC), International Conference on

作者： K. C. Nirasha Jayasiri Sanjeevi Chandrasiri Shalini Rupasinghe K.O. Rivindu Karunanayake Abineshh Uthayachandran M.I. Fathima Zihara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns.

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Regression Analysis for Predicting Health Insurance

Regression Analysis for Predicting Health Insurance

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Communications, Information, Electronic and Energy systems (CIEES), International Conference on

作者： Ginka Marinova Maya Todorova Dept. of Computer systems and technologies Technical University of Varna Varna Bulgaria Dept. of Software and internet technology Technical University of Varna Varna Bulgaria

In the field of health care, the use of data for medical insurance is a current area of research. In this report, regression models created using machine learning methods and algorithms for health insurance prediction are the object of investigation. A correlation analysis was performed on the input data, and a strong dependence was found for the features BMI and smoker. A comparative analysis was made for twenty-four models constructed using Decision Trees (DT), Support Vector Machine (SVM), Boosted, and Bagged algorithms. To evaluate the model metrics were used the coefficient of determination (R-Squared), Root Mean Square Error (RMSE) and Time for training. From the obtained experimental results, it is found that the model for the BMI feature with the Bagged algorithm has an accuracy of 0.94. The mean squared error for features Smoker and Blood Pressure of models created with the Bagged algorithm is 0.06. Models built with the Support Vector Method (SVM) require more training time than the others do. Algorithms from machine learning and statistical analysis are used to create regression models that can be useful both for health care providers and to improve the services provided.

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Enhancing Cybersecurity: A Multilayered Approach to Phishing Website Detection Using Machine Learning

Enhancing Cybersecurity: A Multilayered Approach to Phishing...

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Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering (RMKMATE), International Conference on

作者： Lakshmi Narayanaa T Skandarsini R R S. Jhansi Ida Dept. of Computer Science and Business Systems RMK Engineering College Thiruvallur India

This paper employs machine learning techniques to combat the escalating threat of phishing attacks in the digital realm. The research builds a predictive model capable of differentiating between phishing and legitimate websites by examining a broad range of information retrieved from website URLs, including address bar-based properties, domain characteristics, and webpage content. To ascertain their effectiveness in this task, six well-known machine learning algorithms—Decision Tree, Random Forest, XGBoost, Deep Learning, Autoencoder Neural Network, and Support Vector Machines—are rigorously investigated. Notably, the Multilayer Perceptrons algorithm emerges as the standout performer, achieving an 86.4% accuracy in identifying phishing websites. This endeavor not only advances the field of cybersecurity but also empowers individuals to proactively safeguard themselves against the pervasive threat of phishing attacks in an increasingly interconnected digital landscape.

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Tuning the feedback controller gains is a simple way to improve autonomous driving performance

arXiv

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arXiv 2024年

作者： Liang, Wenyu Baldivieso, Pablo R. Drummond, Ross Shin, Donghwan Dept. of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Dept. of Computer Science University of Sheffield Sheffield United Kingdom

Typical autonomous driving systems are a combination of machine learning algorithms (often involving neural networks) and classical feedback controllers. Whilst significant progress has been made in recent years on the neural network side of these systems, only limited progress has been made on the feedback controller side. Often, the feedback control gains are simply passed from paper to paper with little re-tuning taking place, even though the changes to the neural networks can alter the vehicle’s closed loop dynamics. The aim of this paper is to highlight the limitations of this approach;it is shown that retuning the feedback controller can be a simple way to improve autonomous driving performance. To demonstrate this, the PID gains of the longitudinal controller in the TCP autonomous vehicle algorithm are tuned. This causes the driving score in CARLA to increase from 73.21 to 77.38, with the results averaged over 16 driving scenarios. Moreover, it was observed that the performance benefits were most apparent during challenging driving scenarios, such as during rain or night time, as the tuned controller led to a more assertive driving style. These results demonstrate the value of developing both the neural network and feedback control policies of autonomous driving systems simultaneously, as this can be a simple and methodical way to improve autonomous driving system performance and robustness. © 2024, CC BY.

关键词： Feedback control

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Error Bounds for Decoding Piecewise Constant Nanopore Signals in DNA Storage

Error Bounds for Decoding Piecewise Constant Nanopore Signal...

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IEEE International Conference on Communications (ICC)

作者： Adrian Vidal V. B. Wijekoon Emanuele Viterbo Dept. Electrical & Computer Systems Engineering Monash University Clayton VIC Australia

Nanopore sequencing enables reading strings of A,C,G,T nucleotides in DNA strands by pulling them into nanopores with the help of motor proteins. Due to the discrete stepping of motor proteins, the signals produced by a DNA sequence tend to be piecewise-constant expansions of some underlying real-valued sequence. In this paper, we assume that every k-nucleotide sequence corresponds to a real-valued codeword of length $k$ , and model the nanopore channel as a noisy duplication channel that stretches every sample of a codeword using a geometric distribution, and then adds Gaussian noise. We show that for this channel, a simpler variant of the dynamic time warping (DTW) algorithm performs maximum likelihood decoding. Next, we devise an $O(k^{2})$ - algorithm for bounding the pairwise error probability between two codewords of length $k$ . Finally, we use Scrappie to design codebooks with a storage efficiency of 1 bit per nucleotide and demonstrate using error simulations the accuracy of the calculated error bounds.

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CPR: Correlation-based Page Remapping

CPR: Correlation-based Page Remapping

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International SoC Design Conference, ISOCC

作者： Hojung Namkoong Jungrae Kim Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Dept. of Semiconductor Systems Engineering Sungkyunkwan University Suwon Korea

As the number of multimedia IPs in mobile devices increases, a high memory bandwidth becomes essential. To meet this demand, Application Processors (APs) employ multiple memory channels. However, employing fine-grained channel interleaving leads to frequent bank conflicts in DRAM, resulting in increased energy consumption and reduced battery life. This paper proposes a novel Virtual to Physical address mapping (VA-to-PA) scheme, called Correlation-based Page Remapping (CPR). This technique involves remapping pages based on the access correlations between them to enhance bank parallelism. By increasing the channel interleaving size to 1KB and applying CPR, we can reduce the DRAM energy consumption of activation (ACT) and precharge (PRE) operations by 15% without any decrease in DRAM performance.

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