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

作者： Chitresha Jain Payal Chaudhari Dept. of Computer Science and Engineering Pandit Deendayal Enerygy University Gandhinagar India

Blockchain has shown tremendous growth in the past few years because of its decentralized and immutable architecture, which makes transactions transparent, making it a feasible choice for authentication and security purposes. Federated learning is a collaborative model training method that can benefit from Blockchain Technology. It is vulnerable to a single point of failure while performing aggregation in a centralized server architecture. Blockchain-aided Federated Learning can be proposed for enhancing the security and privacy of user data together with removing a single aggregator. It also supports a mechanism of giving rewards to users in the form of cryptocurrency to encourage users to follow the protocol. This paper introduces a framework named Blockchain-Aided privacy preserving framework for Federated Learning (BPPF) that helps in model training on the user side with added security and privacy.

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Preparing and Annotating a Set of Relevant Contextual Words from the Text Corpus and the Online Newspaper Archives in Bengali

Preparing and Annotating a Set of Relevant Contextual Words ...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Alok Ranjan Pal Prithwish Adhikari Abhishek Dutta Antara Pal Diganta Saha Dept. of Computer Science and Engg. College of Engg. and Mgmt. Kolaghat India Dept. of Computer Science and Engg. Atharva College of Engineering Mumbai India Dept. of Computer Science and Engg. Jadavpur University Kolkata India

Context analysis is one of the obvious tasks in nearly all computational linguistics based works. All of these works pursue this task upto different extents using different techniques. Most of the techniques include the linguistic features, like - N-grams, content words, function words, noun phrase and verb phrase, theme extraction with relevance scoring, facets etc. There are lots of machine learning and deep learning based tools available for this task. The performances of these tools are variable according to different situations. But, the close observations cite that the performance of these systems mainly depends on the availability of the contextual words at that particular experiment. Except for a good collection of contextual words, no system can produce an effective result by using only some complex mathematical calculations. This scenario is the motivation behind this work. In this work, an attempt has been made to construct a structured set of most relevant contextual words according to the classification of text domains, mentioned in the Technology Development for Indian Languages (TDIL) corpus. As it is a group work, therefore different categories of people have been involved in this work for preparing, annotating, verifying and validating the data sets.

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Tomato Shelf Life Prediction using IoT and Machine Learning

Tomato Shelf Life Prediction using IoT and Machine Learning

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2024 International Conference on Innovations in science, engineering and Technology, ICISET 2024

作者： Naim, Nazmul Arafin Islam, Raisul Saifuddin, Mohammed Hoque, Md. Jiabul Hasan, Mohammad Nadib Khandaker, Mohammad Moneruzzaman International Islamic University Chittagong Dept. of Computer and Communication Engineering Kumira Chittagong4318 Bangladesh School of Agriculture Science & Biotechnology Sultan Zainal Abidin Besut Campus Besut Terengganu22200 Malaysia

ISBN: (纸本)9798350355499

Predicting tomato shelf life is crucial for optimizing supply chains and reducing waste. This study utilizes IoT and machine learning to analyze postharvest characteristics of tomatoes in four ripening stages under controlled conditions until the rotten phase. Key findings include significant reductions in weight and dimensions, increased red and decreased green color components indicating ripening, and longer shelf life for unripe tomatoes. Green 1 tomatoes had the most extended shelf life of 18 days. p H trends showed higher levels during ripening (pH 3.5) compared to the green stage (p H 4.5). To predict the shelf life, various machine learning models were applied and the Random Forest Regression was the most accurate model, exhibiting the lowest errors and highest predictive reliability. © 2024 IEEE.

关键词： Shelf life

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NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading

arXiv

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arXiv 2023年

作者： Wei-Ning, Chiu Hsiang-Hui, Liu Han-Jay, Shu Data dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Infrastructure dept. NoxLab Computer Science dept. NTHU Hsinchu Taiwan Data dept. NoxLab Electrical Engineering and Computer Science dept. NTHU Hsinchu Taiwan

We introduce NoxTrader, a sophisticated system designed for portfolio construction and trading execution with the primary objective of achieving profitable outcomes in the stock market, specifically aiming to generate moderate to long-term profits. The underlying learning process of NoxTrader is rooted in the assimilation of valuable insights derived from historical trading data, particularly focusing on time-series analysis due to the nature of the dataset employed. In our approach, we utilize price and volume data of US stock market for feature engineering to generate effective features, including Return Momentum, Week Price Momentum, and Month Price Momentum. We choose the Long Short-Term Memory (LSTM) model to capture continuous price trends and implement dynamic model updates during the trading execution process, enabling the model to continuously adapt to the current market trends. Notably, we have developed a comprehensive trading backtesting system — NoxTrader, which allows us to manage portfolios based on predictive scores and utilize custom evaluation metrics to conduct a thorough assessment of our trading performance. Our rigorous feature engineering and careful selection of prediction targets enable us to generate prediction data with an impressive correlation range between 0.65 and 0.75. Finally, we monitor the dispersion of our prediction data and perform a comparative analysis against actual market data. Through the use of filtering techniques, we improved the initial -60% investment return to 325%. © 2023, CC BY-NC-SA.

关键词： Momentum

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A Comprehensive Review of Routing Protocols in Vehicular Ad Hoc Networks (VANETs): Challenges, Solutions and Future Directions

A Comprehensive Review of Routing Protocols in Vehicular Ad ...

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IEEE UP Section Conference on Electrical computer and Electronics (UPCON)

作者： Sonika Bhardwaj Ramesh Saha Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

VANETs play a pivotal role in the growth/development of the intelligent transportation systems (ITS) by allowing seamless communication between vehicles and infrastructure. This paper presents a comprehensive review of various routing protocols used in VANETs, focusing on topology-based, position-based, hybrid, and machine learning (ML) enhanced protocols. The analysis evaluates these protocols across key performance metrics such as scalability, latency, packet delivery ratio (PDR), overhead, and security. Hybrid and context-aware protocols provide greater adaptability but come with more complexity than standard protocols, which struggle with scalability and security in dynamic contexts. The growing combination of ML, blockchain, and federated learning into VANET routing establishes potential for enhanced performance and security. Furthermore, the paper also highlights the current limitations of an existing protocols and recognizes the directions for the research, the more emphasizing on the need for scalable, secure, and real-time adaptable solutions for next-generation VANETs.

关键词： Federated learning Reviews Scalability Vehicular ad hoc networks Routing Routing protocols Real-time systems Blockchains Security Vehicle dynamics

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SynthBA: Reliable Brain Age Estimation Across Multiple MRI Sequences and Resolutions 3

SynthBA: Reliable Brain Age Estimation Across Multiple MRI S...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Puglisi, Lemuel Rondinella, Alessia De Meo, Linda Guarnera, Francesco Battiato, Sebastiano Ravi, Daniele University of Catania Dept. of Math and Computer Science Catania Italy University College London Department of Neuroinflammation London United Kingdom School of Physics Engineering and Cs University of Hertfordshire Hatfield United Kingdom

ISBN: (纸本)9798350378009

Brain age is a critical measure that reflects the biological ageing process of the brain. The gap between brain age and chronological age, referred to as brain PAD (Predicted Age Difference), has been utilized to investigate neurodegenerative conditions. Brain age can be predicted using MRIs and machine learning techniques. However, existing methods are often sensitive to acquisition-related variabilities, such as differences in acquisition protocols, scanners, MRI sequences, and resolutions, significantly limiting their application in highly heterogeneous clinical settings. In this study, we introduce Synthetic Brain Age (SynthBA), a robust deep-learning model designed for predicting brain age. SynthBA utilizes an advanced domain randomization technique, ensuring effective operation across a wide array of acquisition-related variabilities. To assess the effectiveness and robustness of SynthBA, we evaluate its predictive capabilities on internal and external datasets, encompassing various MRI sequences and resolutions, and compare it with state-of-the-art techniques. Additionally, we calculate the brain PAD in a large cohort of subjects with Alzheimer's Disease (AD), demonstrating a significant correlation with AD-related measures of cognitive dysfunction. SynthBA holds the potential to facilitate the broader adoption of brain age prediction in clinical settings, where re-training or fine-tuning is often unfeasible. The SynthBA source code and pre-trained models are publicly available at https://***/LemuelPuglisi/SynthBA. © 2024 IEEE.

关键词： Deep learning

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Design and Fabrication of an IoT based Air Purifier using HEPA Filter

Design and Fabrication of an IoT based Air Purifier using HE...

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Annual Information Technology, Electromechanical engineering and Microelectronics Conference (IEMECON)

作者： Arpita Choudhary Lakhan Saini Afaq Ahmad Hriday Banerjee Firoj Gazi dept. of Computer Science University of Engineering and Management Jaipur Jaipur India dept. of Computer Science UEM Jaipur Jaipur India dept. of Computer Science University of Alberta Edmonton Canada

Air pollution has become one of the major problems for human health across the globe. Indoor air pollution poses more risk than outdoor air pollution, since the human body is more exposed to the inside. It refers to the contamination of indoor air that causes harmful health problems. To purify the polluted air, the air purifier is a main necessity. In this Internet of Things (IoT) project, we have created an air purifier that purifies the indoor air by passing it through the High Efficiency Particulate Air (HEPA) filter and Ultraviolet light. The microcontroller used is the NodeMCU based on the ESP8266 WiFi enabled chip. Two MQ135 air quality sensors used to monitor the change and calculate the efficiency of the purified air. IoT customization eases the manual work of changing the purifier manually every time by providing a platform that operates on the mobile phone via Wi-Fi where the user can operate it accordingly. It provides an equivalent grade of services at a very moderate cost that is affordable even for the average person and can provide real time analytics. The proposed system is capable for providing hands-on practical operations, which enables more exciting possibilities in IoT based automation systems.

关键词： Air cleaners Microwave measurement Air pollution Information filters Sensors Timing Internet of Things

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Classification of Epileptic Seizure Using 2L- LSTM Architecture

Classification of Epileptic Seizure Using 2L- LSTM Architect...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Preeti Malik Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Epilepsy is a long-term neurological problem that makes it hard for people to function their daily lives normally because their seizures can happen at any time. People who have epileptic seizures often hurt their brains, which can lead to memory loss, mental decline, and other problems. Because of this, it is important to find epileptic seizures quickly and get medical help for them. This paper presents the optimized two-layer long short-term memory model (2L-LSTM), and it can automatically expand retrospectively and for each patient. The program automatically creates distinctive characteristics that help identify seizure patients as either epileptic or not epileptic in real time. The proposed two layer Long Short-Term Memory (2L-LSTM) classifier classifies Epileptic or non-Epileptic signals with up to $\mathbf{9 8 \%}$ accuracy. It also gives the sensitivity, specificity and F1_score of $\mathbf{9 5 \%}$, $\mathbf{9 4 \%}$ and $\mathbf{9 6 \%}$ respectively. The Simulation results shows that proposed 2L-LSTM model perform far better as compared to 1L-LSTM.

关键词： Deep learning Technological innovation Sensitivity Accuracy Computational modeling Simulation Epilepsy

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On the Effect of Redundant Caching Policy on Multimedia Streaming in D2D Underlay Network

On the Effect of Redundant Caching Policy on Multimedia Stre...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Suvadip Batabyal Dept. of Computer Science & Engineering NIT Durgapur India

Device-to-device (D2D) communication underlay cellular network improves spectral efficiency by reusing cellular resources over short low-powered links between the devices. These D2D links can be used to fetch cached contents and therefore improve the application experiences, especially for multimedia contents. In this article, we investigate the effect of caching policies on the offloading probability (also known as the hit-rate), and hence the buffering time, for video streaming applications. Designing a caching policy to maximize the hit-rate depends on several factors such as device storage capacity, instantaneous number of devices available to act as a D2D transmitter, resource allocation policy, etc. We first model the D2D links as a simple birth-death process to realize the dynamicity of the D2D links, and derive the steady-state distribution of the number of D2D links. We show that the hit-rate can be improved by increasing the caching redundancy (number of copies of a given content to be cached). Finally, through simulation we show that there exists an optimal group size and redundancy, beyond which the hit-rate do not improve.

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Performance Monitoring and Controlling of Multicore Shared-Memory Parallel Processing Systems 3

Performance Monitoring and Controlling of Multicore Shared-M...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Haji, Lailan M Zeebaree, Subhi R. M. Ageed, Zainab Salih Ahmed, Omar M. Sadeeq, Mohammed A. M. Shukur, Hanan M. University of Zakho Faculty of Science Computer Science Dep. Duhok Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq Zakho Technical Institute Duhok Polytechnic University Information Technology Dept. Duhok Iraq Technical College of Administration Duhok Polytechnic University ITM Dept. Duhok Iraq Al-Kitab University Computer Engineering Dept. Kirkuk Iraq

ISBN: (纸本)9798350334968

The field of research known as 'parallel processing' examines architectural and algorithmic methods for increasing the efficiency and other desirable qualities (such as cost-effectiveness and reliability) of computers by exploiting many forms of concurrency. The development of multi-core CPUs has opened the door to novel strategies for boosting system performance via parallelism. Increasing the number of concurrent threads greatly affects the time cost. While increased parallelism might reduce calculation time, the associated synchronization costs would likely be prohibitive for most applications. In this article, we'll go through the first half of the process, which involves gathering requirements and designing a prototype;the second half, which involves monitoring and managing a program that can run on a wide range of multicore system architectures, including those with (2, 4, and 8 CPUs). A shared-memory parallel processing method was proposed and built to show off the system's full potential. The implications of multi-core systems on overall program execution time and parallel processing in shared-memory systems provide the backbone of this investigation. © 2022 IEEE.

关键词： Cost effectiveness

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