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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Kumar, M. Ravi Jeya, I. Jasmine Selvakumari Athira, S. Hindusthan College of Engineering and Technology Department of Computer Science And Engineering Coimbatore India Vit Bhopal University School of Computing Science and Engineering Sehore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331529246

Over the years, Cloud computing is becoming increasingly popular due to the continually changing technology. The primary goal of the cloud computing network is to offer consumers pay-per-use usage of on-demand processing and storage resources, enabling low-cost, efficient infrastructure utilization for smaller businesses. However, because of the feature of cloud resource sharing, data security and privacy create issues, and prevent smart systems from continuously transferring produced data to the cloud computing resources. Many encryption methods were presented to address data security concerns. This article presents the processes of several data protection and encryption systems currently in use and critically analyzes them based on common factors. The purpose of this survey is to compile data encryption methods now in use, often discussed in the survey of smart systems for data security when applied to the cloud platforms. © 2025 IEEE.

关键词： Cloud security

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Braille–Latin conversion using memristive bidirectional associative memory neural network

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Journal of Ambient Intelligence and Humanized computing 2023年第9期14卷 12511-12534页

作者： Vaidyaraman, Jayasri Thyagarajan, Abitha K. Shruthi, S. Ravi, V. School of Electronics Engineering Vellore Institute of Technology Chennai India Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States

Artificial neural networks (ANNs) are finding increasing use as tools to model and solve problems in almost every discipline in today’s world. The successful implementation of ANNs in software—particularly in the fields of deep learning and machine learning—has spiked an interest in designing hardware architectures that are custom-made to implement ANNs. Several categories of ANNs exist. The two-layer bidirectional associative memory (BAM) is a particular class of hetero-associative memory networks that is extremely efficient and exhibits good performance for storing and retrieving pattern pairs. The memristor is a novel hardware element that is well-suited to modelling neural synapses because it exhibits tunable resistance. In this work, in order to create a device that can perform Braille–Latin conversion, we have implemented a circuit realization of a BAM neural network. The implemented hardware BAM uses a memristor crossbar array for modelling neural synapses and a neuron circuit comprising an I-to-V converter (resistor), voltage comparator, D flip-flop, and inverter. The efficiency of the implemented hardware BAM was tested initially using 2 × 2 and 3 × 3 patterns. Upon successfully verifying the ability of the implemented BAM to store and retrieve simple pattern pairs, it was trained for a pattern-recognition application, namely mapping Braille alphabets to their Latin counterparts and vice versa. The performance of the implemented BAM network is robust even on the introduction of noise. The application can recognize the input patterns with accuracies of 100% in either direction when tested with up to 30% noise. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Neural networks

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SMARTGOV: Strategic Management of Assets for Resilient and Transparent Government Operations 9

SMARTGOV: Strategic Management of Assets for Resilient and T...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Chauhan, Naman Atri, Pranshul Tomar, Riya Kumar, Aman Chauhan, Chhavi Kumar, Kaitala Charan Sophiya, E. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India School of Computer Science University of Petroleum and Energy Studies Dehradun India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350350845

In this age, we introduce an application that aims to revolutionize the management of government buildings. With a commitment, to supporting Digital India initiative, proposed project addresses the changing needs of governance and public service delivery. This application serves a purpose;it provides authorities with an easy-To-use platform for efficient building management while also empowering the public with real-Time information and a streamlined channel for expressing concerns. We leverage technologies like GPS camera Lite, Google Maps, Automatic fire detection using CNN with a mAP of 98.61% and Cloud Firestore to seamlessly integrate our application with Android Studio and Kotlin ensuring an optimal user experience. Our motivation behind this initiative stems from the need to contribute to our nation's progress by promoting governance and aligning with India's aspirations for Digital India. By fostering communication, transparency and accountability our application becomes a tool, in building an empowered and prosperous India. Proposed Work outlines the features, technological framework and societal benefits of our solution that resonates with both government requirements and public aspirations. © 2024 IEEE.

关键词： Cameras

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A novel medical image data protection scheme for smart healthcare system

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 821-836页

作者： Mujeeb Ur Rehman Arslan Shafique Muhammad Shahbaz Khan Maha Driss Wadii Boulila Yazeed Yasin Ghadi Suresh Babu Changalasetty Majed Alhaisoni Jawad Ahmad School of Computer Science and Informatics Cyber Technology InstituteDe Montfort UniversityLeicesterUK Department of Biomedical Engineering University of GlasgowGlasgowUK School of Computing Engineering and the Built EnvironmentEdinburgh Napier UniversityEdinburghUK Robotics and Internet‐of‐Things Laboratory Prince Sultan UniversityRiyadhSaudi Arabia Department of Computer Science Al Ain UniversityAl AinUnited Arab Emirates Department of Computer Engineering College of Computer Science King Khalid UniversityAbhaSaudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the ***,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning *** healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical *** smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance *** security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of ***,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission *** address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos *** results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,*** validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

关键词： data analysis medical image processing security

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Enhancing Named Entity Recognition with a BERT-DQN Hybrid Model 15

Enhancing Named Entity Recognition with a BERT-DQN Hybrid Mo...

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

作者： Bharathi Mohan, G. Shyam Ganesh, K. Lavanya, G. Elakkiya, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India Bits Pilani Department of Computer Science and Engineering Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798350370249

Named Entity Recognition (NER) is a fundamental task in natural language processing (NLP) that involves identifying and classifying proper nouns into predefined categories such as names of people, organizations, locations, etc. This paper proposes an advanced NER model by integrating Bidirectional Encoder Representations from Transformers (BERT) with Conditional Random Fields (CRF), further enhanced with Deep Q-Network (DQN) reinforcement learning within the CRF layer. Utilizing the CoNLL 2003 dataset, our model aims to leverage the powerful feature extraction capabilities of BERT along with the structured prediction advantage of CRF. The DQN integration aims to optimize the sequence tagging decisions dynamically, addressing limitations found in traditional NER models. Our results demonstrate significant improvements in precision, recall, and F1 score, highlighting the efficacy of incorporating reinforcement learning into the BERT-CRF architecture for NER tasks. © 2024 IEEE.

关键词： BERT Conditional Random Field DQN Named Entity Recognition precision recall

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Fusing Superpixel Graph Propagation and Positional Convolutions for Small Object Detection in Computed Tomography Scan

Fusing Superpixel Graph Propagation and Positional Convoluti...

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2024 International Conference on Microelectronics, ICM 2024

作者： Modak, Sudipta Abdel-Raheem, Esam Rueda, Luis University of Windsor Department of Electrical and Computer Engineering Canada School of Computer Science University of Windsor WindsorON Canada

ISBN: (纸本)9798350379396

Small object detection in radiological images has been a key challenge in the field of medical diagnosis for the last decade. Radiological modalities such as computed tomography scan imaging are often used to evaluate the condition of a patient. These modalities can capture anomalies from various parts of the body which are then analyzed by radiologists to identify tumors, stones, nodules, etc. However, it becomes a strenuous task for any radiologist to accurately identify tiny abnormalities contained within the radiological images. In this paper, a two-step lightweight computer-aided anomaly detection method is proposed that is suitable for small object detection in computed tomography scan images. The proposed method leverages the power of uniform superpixel generation, graph propagation, and positional convolutions to detect anomalies in computed tomography scan images with great accuracy. Furthermore, experiments conducted on imaging datasets from two distinct organs of the body that is the lungs and the kidneys, show the effectiveness of the proposed method in terms of small object detection in medical imaging. © 2024 IEEE.

关键词： computerized tomography

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Image Caption Generation using Contrastive Language Image Pretraining 15

Image Caption Generation using Contrastive Language Image Pr...

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

作者： Bharathi Mohan, G. Harigaran, R. Sri Varshan, P. Srimani, Repakula Prasanna Kumar, R. Elakkiya, R. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai India BITS Pilani Department of Computer Science and Engineering Dubai Campus Dubai United Arab Emirates

ISBN: (纸本)9798350370249

Image captioning is a problem within the boundary of both natural language processing and computer vision. A challenging task is making well-written sentences. It needs both understanding language rules and meaning. Describing picture contents with accurate sentences impacts individuals with low vision to understand images better. In this study, we have introduced a novel approach of using Contrastive Language-Image Pre-training(CLIP) encodings as image features for an Lstmbased textual decoder model trained on the coco dataset 2017. On training with textual context CLIP model has rich image semantic features. For datasets with data in large-scale and diverse features, CLIP provided meaningful captions without any additional attention mechanism or pre-training because of joint embedding learning. To evaluate the model we have generated captions for random unseen images. The model performed well with the unseen images generating meaningful captions related to the image. © 2024 IEEE.

关键词： Semantics

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A Data-Driven Approach based on Dynamic Mode Decomposition for Efficient Encoding of Dynamic Light Fields

A Data-Driven Approach based on Dynamic Mode Decomposition f...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ravishankar, Joshitha Khaidem, Sally Sharma, Mansi Indian Institute of Technology Madras Department of Electrical Engineering India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350302493

Dynamic light fields provide a richer, more realistic 3D representation of a moving scene. However, this leads to higher data rates since excess storage and transmission requirements are needed. We propose a novel approach to efficiently represent and encode dynamic light field data for display applications based on dynamic mode decomposition (DMD). Acquired images are firstly obtained through optimized coded aperture patterns for each temporal frame/camera viewpoint of a dynamic light field. The underlying spatial, angular, and temporal correlations are effectively exploited by a data-driven DMD on these acquired images arranged as time snapshots. Next, High Efficiency Video Coding (HEVC) removes redundancies in light field data, including intra-frame and inter-frame redundancies, while maintaining high reconstruction quality. The proposed scheme is the first of its kind to treat light field videos as mathematical dynamical systems, leverage on dynamic modes of acquired images, and gain flexible coding at various bitrates. Experimental results demonstrate our scheme's superior compression efficiency and bitrate savings compared to the direct encoding of acquired images using HEVC codec. © 2023 IEEE.

关键词： Dynamic mode decomposition

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New Delay Doppler Communication Paradigm in 6G Era:A Survey of Orthogonal Time Frequency Space(OTFS)

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China Communications 2023年第6期20卷 1-25页

作者： Weijie Yuan Shuangyang Li Zhiqiang Wei Yuanhao Cui Jiamo Jiang Haijun Zhang Pingzhi Fan Department of Electronic and Electrical Engineering Southern University of Science and TechnologyShenzhenChina Faculty of Electrical Engineering and Computer Science Technical University of BerlinBerlinGermany School of Mathematics and Statistics Xi’an Jiaotong UniversityXi’anChina Department of Communication Engineering Beijing University of Posts and TelecommunicationsBeijingChina China Academy of Information and Communications Technology BeijingChina School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Information Science and Technology Southwest Jiaotong UniversityChengduChina

In the 6G era,Space-Air-Ground Integrated Network(SAGIN)are anticipated to deliver global coverage,necessitating support for a diverse array of emerging applications in high-mobility,hostile *** such conditions,conventional orthogonal frequency division multiplexing(OFDM)modulation,widely employed in cellular and Wi-Fi communication systems,experiences performance degradation due to significant Doppler *** overcome this obstacle,a novel twodimensional(2D)modulation approach,namely orthogonal time frequency space(OTFS),has emerged as a key enabler for future high-mobility use ***,OTFS modulates information within the delay-Doppler(DD)domain,as opposed to the timefrequency(TF)domain utilized by *** offers advantages such as Doppler and delay resilience,reduced signaling latency,a lower peak-to-average ratio(PAPR),and a reduced-complexity *** studies further indicate that the direct interplay between information and the physical world in the DD domain positions OTFS as a promising waveform for achieving integrated sensing and communications(ISAC).In this article,we present an in-depth review of OTFS technology in the context of the 6G era,encompassing fundamentals,recent advancements,and future *** objective is to provide a helpful resource for researchers engaged in the field of OTFS.

关键词： OTFS 6G delay-Doppler(DD)domain

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Adaptive Control Strategy for Primary Frequency Regulation of BESS in Wind Farm 9

Adaptive Control Strategy for Primary Frequency Regulation o...

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9th Asia Conference on Power and electrical engineering, ACPEE 2024

作者： Gu, Yichao Zhuang, Kai Zhang, Jimiao Xie, Jianfeng Chongqing University of Science and Technology School of Electrical Engineering Chongqing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350309638

This paper proposes a novel adaptive synthetic inertia (SI) control scheme for the battery energy storage system (BESS) in the wind farm. The proposed approach dynamically adjusts the amplitude of the direct current (DC) of the voltage source converter (VSC) in response to grid frequency disturbances. The unique feature of the proposed control is its ability to emulate droop and inertia in real time by adjusting parameter values according to the rate of change of frequency (RoCoF) and frequency deviation. Within the SI control, a first-order low-pass element is incorporated into the virtual inertia control to augment the system inertia. Its advantages and effectiveness are rigorously demonstrated on a modified IEEE benchmark system under load changes and wind speed fluctuations. © 2024 IEEE.

关键词： Battery storage

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