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3rd International Conference on Innovative Sustainable Computational Technologies, CISCT 2023

作者： Roy, Vikas Kumar Roy, Ganpati Kumar Thakur, Vasu Baliyan, Nikhil Goyal, Nupur Chauhan, Yash Ruprecht Karl University of Heidelberg Department of Data and Computer Science Germany Sharda University Department of Computer Science and Engineering Greater Noida India RIT Roorkee Department of Computer Science and Engineering Uttarakhand Roorkee India Graphic Era Deemed to Be University Department of Mathematics Dehradun India Graphic Era Deemed to Be University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350303360

To carry out vigorous and effective fruit recognition in farming is difficult since there are a number of variations. Lately, various deep-learning approaches presented an outstanding result in numerous visual-guided agricultural usage. These approaches need labelling on training data, which is a tedious work. This study presented a customized five layers CNN architecture and various pretrained deep learning algorithms i.e., VGG-16, DenseNet-121 and InceptionV3are used to recognise the fruit and their calories for helping farmers, patients and peoples to estimate the fruit calories for their health and in farming. The model is trained and validated on integrated datasets which is downloaded from Kaggle and IEEE dataport having 10 classes of different fruits. After extensive experiments, highest accuracy obtained was 99.60% by five layers of CNN and proposed deep learning model has been deployed on Amazon AWS EC2 instance using WinSCP, PuTTY and PuTTYgen. Flask (Python Framework), HTML, CSS and JavaScript is used for website development and connected to its parent website which help users to recognize the fruit and their calories. © 2023 IEEE.

关键词： Fruits

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Advanced machine learning models for Depression level categorization using DSM 5 and personality traits 2

Advanced machine learning models for Depression level catego...

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2nd International Conference on Machine Learning and data engineering, ICMLDE 2023

作者： Krishna, Rahul Teja, Ravi Neelima, N. Peddi, Nikhita Department of Data Science Engineering Cullen College of Engineering University of Houston TX77004 United States Department of Computer Science Indiana State University Indiana47809 United States Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetam Bengaluru India

The COVID-19 pandemic has led to an increase in mental health problems, such as depression. Depression is a major cause of disability and can affect anyone. It is important to seek early detection and treatment for depression, as it can have a significant impact on a person's life. This research paper explores the complex and multifaceted nature of human personality using the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) criteria and Eysenck's personality traits questionnaire. The paper then outlines the methodology employed, involving the collection of data through surveys consisting of 66 questions divided into six forms. DSM-5 criteria are applied to assess depressive episodes, while Eysenck's questionnaire evaluates personality traits. The paper's approach involves two stages: predicting personality traits and estimating stress levels. Multiple machine learning models are utilized for these predictions. The results of this study highlight the effectiveness of the Support Vector Machine (SVM) classifier, which consistently outperforms other models, achieving impressive accuracy in predicting both personality traits, such as extroversion and neuroticism, and stress levels. Notably, SVM demonstrates its prowess with an accuracy of 91.43% in predicting stress levels. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Deep Learning in Analyzing Carbon Flux Patterns for Environmental Health: Remote Sensing Insights for Climate Mitigation Strategies

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 337-351页

作者： Subramani, Hariharan Harish, M. Neela S, Selva kumar Radhakrishnan, P. Madupu, L. N. K. Sai Sandeep, Kasturi Sai Nimma, Divya Department of CSE Panimalar Engineering College Chennai India Department of Biomedical Engineering Easwari Engineering College Chennai India Department of Computer Science and Engineering B.M.S College of Engineering Bull Temple Road Bengaluru India Department of ECE Tagore Engineering College Chennai India Department of Civil Engineering R.V.R. & J.C. College of Engineering Andhra Pradesh Chowdavaram Guntur India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist Andhra Pradesh India University of Southern Mississippi Data Analyst in UMMC Hattiesburg United States

The integration of deep learning with remote sensing techniques has emerged as a transformative approach in analyzing carbon flux patterns to mitigate climate change impacts on environmental health. This study introduces a Spatiotemporal Attention Network (SATN) that leverages satellite-derived data and ground-based observations to predict and understand carbon flux variations. Key features such as vegetation indices, soil moisture, and land surface temperature are prioritized, contributing over 60% to the model’s predictive accuracy. The proposed SATN achieved a remarkable R² score of 0.95, outperforming traditional models like ConvLSTM (0.87) and Transformers (0.90). With a low RMSE of 0.045 and MAE of 0.031, the SATN demonstrated robust accuracy and efficiency, achieving a balanced F1-score of 0.91. The results highlight spatial hotspots like the Amazon Rainforest and temporal trends indicating seasonal vegetation growth and human-induced emissions as critical contributors to carbon flux dynamics. By identifying regions and periods of high carbon activity, this study provides actionable insights for reforestation efforts, urban emissions control, and climate resilience planning. The SATN framework sets a new benchmark for integrating machine learning with environmental science, paving the way for innovative climate mitigation strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Carbon flux Carbon sequestration Climate change Deep learning Environmental monitoring Remote sensing Satellite data Spatiotemporal Attention Network Vegetation indices

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Piccolo: Large-Scale Graph Processing with Fine-Grained in-Memory Scatter-Gather 31

Piccolo: Large-Scale Graph Processing with Fine-Grained in-M...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Seoul National University Department of Electrical and Computer Engineering Korea Republic of Old Dominion Univeristy School of Data Science United States Samsung Electronics Korea Republic of

ISBN: (纸本)9798331506476

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28 × and a geometric mean speedup of 1.62 ×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025 IEEE.

关键词： Memory architecture

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Non-adaptive Quantitative Group Testing via Plotkin-Type Constructions

Non-adaptive Quantitative Group Testing via Plotkin-Type Con...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Soleymani, Mahdi Mahdavifar, Hessam Javidi, Tara University of California San Diego Halicioglu Data Science Institute United States University of Michigan The Department of Electrical Engineering and Computer Science Ann Arbor United States University of California San Diego Halicioglu Data Science Institute The Department of Electrical and Computer Engineering United States

ISBN: (纸本)9781665475549

In this paper, we study the quantitative group testing problem, also known as the heavy hitter detection problem and the coin weighing problem, in a non-adaptive setting. In this problem, the aim is to recover k defective items from a group of n items with the smallest possible number of quantitative/additive tests, where each such test returns the number of defective items participating in the test. In the non-adaptive setting, that we study in this paper, all tests are designed at once and can be, in principle, run in parallel. We establish a novel construction method for designing non-adaptive test matrices with a nested structure inspired by the Plotkin concatenation in the coding theory literature. Our proposed algorithm identifies k defective items among the collection of n items with high probability using k(1 + o(1)) log (n/k) non-adaptive tests in the sub-linear regime of k = o(n). Furthermore, our analysis demonstrates that the probability of decoding failure approaches zero exponentially in k as k, n ? 8. Our approach outperforms existing state-of-the-art methods for designing non-adaptive test schemes with efficient decoders for the quantitative group testing problem in terms of the required number of measurements. © 2023 IEEE.

关键词： Decoding

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A Novel Energy Efficient Sensor data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Manicka Raja, M. Manoj Kumar, S. Kiruba, B. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350319088

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied. © 2023 IEEE.

关键词： Internet of things

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Enhanced Model for Mango Detection and Quality Classification Using Optimized Feature Extraction Techniques

Enhanced Model for Mango Detection and Quality Classificatio...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Aryan, Adla Mohammed, Abdul Aleem Chabra, Manish Rasheed, Syed Saarib Adnan, Mohammed Raoof, Mohammed Abdul Vardhaman College of Engineering Department of Artificial Intelligence & Machine Learning Telangana Hyderabad501218 India Muffakham Jah College of Engineering and Technology Department of Computer Science and Engineering Hyderabad500034 India Methodist College of Engineering and Technology Department of Artificial Intelligence & Data Science Telangana Hyderabad500001 India

ISBN: (纸本)9798331529833

This paper introduces an automated grading system for mangoes, enhancing efficiency and accuracy compared to human-based methods. The system uses the Lion Assisted Firefly Algorithm (LA-FF) to extract the best features from multiple highlights, enhancing grading efficiency and accuracy. The LA-FF algorithm is then used to fine-tune the convolutional layers of a deep CNN based on the specific requirements of mango grading. The system integrates the latest algorithms, automation, and adaptation to create an even more effective and precise grading system suitable for rural agricultural contexts. The LA-FF algorithm is used to extract the best features from multiple highlights, resulting in a more accurate and efficient grading process. © 2025 IEEE.

关键词： Fruits

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An Empirical Analysis of Load Balancing Issues in Fog Computing Environment 2

An Empirical Analysis of Load Balancing Issues in Fog Comput...

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2nd International Conference on data Science and Information System, ICDSIS 2024

作者： Kumar, K. R. Prasanna Siddesh, G.M. Ananda Babu, J. M S Ramaiah Institute Of Technology Department Of Computer Science & Engineering Bangalore India M S Ramaiah Institute Of Technology Department Of Artificial Intelligence & Data Science Bangalore India Malnad College Of Engineering Department Of Information Science And Engineering Hassan India

ISBN: (纸本)9798350381665

Fog Computing (FC) is a distributed paradigm that complements cloud computing in terms of service delivery. By extending storage and computation of the network edge, fog systems enable location awareness and mobility support, while also resolving the issue of service computing for delay-sensitive applications. A crucial component of fog networks is load balancing, which prevents fog nodes from becoming under or overloaded. In recent years, only a limited number of studies have been conducted on load-balancing methods in fog networks. This paper provides a thorough assessment of the load-balancing techniques used in fog computing, covering approximate, accurate, fundamental, and hybrid methods. This paper also examines load balancing metrics in fog networks, including all benefits and drawbacks associated with the selected load balancing algorithms. The evaluation methods considered for each study are investigated. Additionally, the primary unresolved issues and potential developments for these systems are discussed. © 2024 IEEE.

关键词： Virtualization

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Optimized Transfer Learning Model for Lung Cancer Stage Classification Using Computed Tomography Images 5

Optimized Transfer Learning Model for Lung Cancer Stage Clas...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Deepika, R. Shanmugam, P. Moorthi, K. Kumar, P. K. Manoj Swarna, S.L. Ramnath, K. B v Raju Institute of Technology Department of Artificial Intelligence and Data Science Telangana Narsapur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Sri Sairam Engineering College Department of Electronics and Communication Engineering Chennai India School of Computational Intelligence Joy University Tirunelveli India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India K.L.N College of Information Technology Department of Computer Science and Engineering Tamilnadu Madurai India

ISBN: (纸本)9798331518097

Lung cancer has become one of the primary causes of death worldwide due to cancer, which indicates the necessity of early diagnosis. In this regard, the present work proposes an optimized model of transfer learning based on characterizing different stages of lung cancer by using computed tomography images acquired from the IQ-OTH/NCCD lung cancer database downloadable from Kaggle. It makes use of a two-step process: first, a U-Net model is used on the patient's CT images to reconstruct anatomical features and reduce noise;second, a ShuffleNet architecture is used to predict the patient's CT stage using the reconstructed image. In addition, to enhance classification, PSO was incorporated into the model so that it can optimize the hyperparameters. The accuracy of the model was at 97.85 percent, and it suggests that the model can become a very useful tool for clinicians to bring about earliest identification and staging for lung cancer. © 2024 IEEE.

关键词： Lung cancer

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3D Mesh ONoC: Design of low Insertion Loss and Non-blocking Optical Router and Efficient Routing Algorithm 14

3D Mesh ONoC: Design of low Insertion Loss and Non-blocking ...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Asadinia, Sanaz Yaghoubi, Elham Mehrabi, Mahdi Esfahan Branch Islamic Azad University Department of Computer Engineering Esfahan Iran Najafabad Branch Islamic Azad University Faculty of Computer Engineering Big Data Research Center Najafabad Iran Shiraz Branch Islamic Azad University Department of Computer Engineering Shiraz Iran

ISBN: (纸本)9798350349412

In recent years, the 3D Network on Chips (3DNoCs) is an optimal solution to use multi-core processors and overcome the bandwidth limit of electrical connections. The use of optical communication technology for 3DNoCs in multi-core architecture has been very promising and effective in the performance of these networks. In the architecture of 3D Optical Network on Chip (3D ONoC)4 and 5 port routers are applied at the corners and edges of the network and 6 and 7 port routers are used to transmit data. The structure of optical routers in 3D ONoCs affects the transmission efficiency of the entire network, so that providing optical routers with minimal loss is focused by the researchers in this *** this paper, a non-blocking 6 router are presented based on the micro-ring resonator to reduce the loss in terms of the number of optical switching elements. Moreover, in this paper, a new routing algorithm is proposed to find the best route in terms of loss for the 3DONoC, which is implemented on the proposed 6 port optical router. The proposed algorithm can choose the best route between the source and destination processing cores according to the architecture of the proposed 6 port optical router and the use of turning models. This optimal route has the least loss compared to other routes between the selected source and destination. To evaluate the optical routers and the proposed routing algorithm on 3DONoC network, the parameter of loss are considered so that the simulation results show the improvement of network performance on the 3DONoC compared to existing networks. © 2023 IEEE.

关键词： Network-on-chip

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