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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Tomar, Vivek Kumar, Nitin Deshmukh, Maroti Singh, Maheep University Computer Science & Engineering Department Dehradun India Computer Science & Engineering Department Chandigarh India National Institute of Technology Computer Science & Engineering Department UttarakhandGarhwal Srinagar India National Institute of Technology Uttarakhand Doon University Computer Science Department Dehradun India

ISBN: (纸本)9798331540821

Single-sample face and ear recognition is a challenging sub-problem in biometric recognition, as only one sample is available for training. This article presents a method to recognise the subject from a single face or ear image with two-level image augmentation, followed by using a pre-trained deep learning model for feature extraction and classification. First, sampling is used for generating additional samples, and then mirroring and rotation are used. Pre-trained VGG16 model is used for parameter estimation and classification. The proposed method is tested on three face and two ear databases. The proposed method achieves classification accuracy of 84.83 %, 77.08 %, and 88.52 % on ORL, Yale, and FeedTum databases and 66.69% and 78.29% on AMI and IITD databases, respectively. The image quality of the generated samples is also found to be in an acceptable range. The proposed method can be helpful and effective when smaller databases and fewer training images are available. © 2024 IEEE.

关键词： Image quality

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CKELM: Unified Approach to Parking Space Classification for All Weather Types 12th

CKELM: Unified Approach to Parking Space Classification for...

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12th International Conference on Big Data and Artificial Intelligence, BDA 2024

作者： Navpreet Roul, Rajendra Kumar Rani, Rinkle Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

ISBN: (纸本)9783031818202

Recently, surging urbanization and the subsequent increase in vehicular movement have amplified the popularity of parking management systems. A crucial hurdle in effective parking management is the optimal utilization of available parking spaces. Although deep learning techniques have showcased their prowess in accurately identifying parking spaces, they sometimes falter when partial obstructions and diverse lighting conditions are present. This challenge is further exacerbated during foggy or hazy weather, resulting in a decline in their performance. This research article introduces a novel approach that leverages the benefits of CNN and Kernel Extreme Learning Machine (KELM). The approach not only enhances accuracy but also significantly reduces training time. The key innovation lies in replacing the fully connected layer of CNN with KELM, which circumvents the backpropagation process and reduces the training time. A purpose-built data set tailored to classify parking spaces in hazy conditions and PKLot is employed for the validation. Within the proposed approach, images from the hazy dataset undergo preprocessing using Light-DehazeNet (LD-Net) to counteract the effects of haze. Subsequently, CNN is trained on this preprocessed dataset to extract features and generate a feature vector. This feature vector is input into the KELM to classify parking spaces as occupied or vacant. Compared with standalone CNN, KELM, machine learning classifiers, and similar existing models, the proposed model showcases superior performance and better computational efficiency. This study demonstrates that the integration of CNN with KELM offers a promising solution for parking space classification in challenging weather conditions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Parametrized Optimization Based on an Investigation of Musical Similarities Using SPARK and Hadoop

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SN computer Science 2024年第1期5卷 78页

作者： Chaudhary, Savita Karthik, V. Shankar, R. Taranum, Ayesha Naresh, E. Department of Computer Science and Engineering Sir M Visvesvaraya Institute of Technology Bengaluru Bengaluru India Department of Information Science and Engineering Ramaiah Institute of Tech B’lore India Department of Computer Science and Engineering BMS Institute of Technology and Engineering Bengaluru India Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The big data processing framework Spark is used to power a parameterizable recommender system that can make recommendations for music based on a user’s individual tastes and take into account a variety of musical tonal qualities. The system as it is presently built is completely scalable, which means that additional songs can be contributed to the data, the cluster size could be increased, and new types of audio information, in addition to more cutting-edge similarity evaluations, may be included. Another issue discussed in this research paper is the parallel collection of required audio characteristics on a computer cluster. Song recommendations for a dataset including more than 114,000 songs may be created on a Spark cluster with 16 nodes in under 12 s by integrating eight distinct audio feature types and similarity assessments. After the features have been retrieved, they are sent to the Spark-based recommender system to be processed. The calculated distance was displayed, examined, and graphically depicted. By computing the distance depending on the melody, rhythmic, and timbral components of the music, the final software controls song suggestion. © 2023, The Author(s).

关键词： Big data Hadoop Music information retrieval Optimization Spark

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DeepRice: A deep learning and deep feature based classification of Rice leaf disease subtypes

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Artificial Intelligence in Agriculture 2024年第1期11卷 34-49页

作者： P.Isaac Ritharson Kumudha Raimond X.Anitha Mary Jennifer Eunice Robert Andrew J Department of CSE Karunya Institute of Technology and Sciences CoimbatoreTamilnaduIndia Department of Robotics Engineering Karunya Institute of Technology and SciencesCoimbatoreIndia Department of Mechatronics Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India Department of Computer Science and Engineering Manipal Institute of TechnologyManipal Academy of Higher EducationManipalKarnataka 576104India

Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf ***,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical *** the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and *** study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the *** classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown *** meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of *** datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks *** performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception *** effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art ***,the layer wise feature extraction is also visualized as the interpretable AI.

关键词： Rice leaf disease Deep learning CNN Crop yield Agriculture Food security

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Cooperative Channel and Optimized Route Selection in Adhoc Network

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Intelligent Automation & Soft Computing 2023年第5期36卷 1547-1560页

作者： D.Manohari M.S.Kavitha K.Periyakaruppan B.Chellapraba Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119TamilnaduIndia Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035TamilnaduIndia Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107TamilnaduIndia Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia

Over the last decade,mobile Adhoc networks have expanded dramati-cally in popularity,and their impact on the communication sector on a variety of levels is *** uses have expanded in lockstep with its *** to its instability in usage and the fact that numerous nodes communicate data concur-rently,adequate channel and forwarder selection is *** this proposed design for a Cognitive Radio Cognitive Network(CRCN),we gain the conﬁdence of each forwarding node by contacting one-hop and second level nodes,obtaining reports from them,and selecting the forwarder appropriately with the use of an optimization *** that point,we concentrate our efforts on their channel,selection,and lastly,the transmission of data packets via the designated *** simulation work is validated in this section using the MATLAB ***,steps show how the node acts as a conﬁdent forwarder and shares the channel in a compatible method to communicate,allowing for more packet bits to be transmitted by conveniently picking the channel between *** cal-culate the conﬁdence of the node at the start of the network by combining the reliability report for theﬁrst hop and the reliability report for the secondary *** then refer to the same node as the conﬁdent node in order to operate as a *** a result,we witness an increase in the leftover energy in the *** percentage of data packets delivered has also increased.

关键词： Adhoc Network conﬁdent forwarder one-hop optimized route selection secondary report channel selection

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Smart Waste Management 15

Smart Waste Management

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kanade, Dnyaneshwar More, Aniket Binnar, Nilesh Narnaware, Paridhi Narote, Atharv Patil, Niraj Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Department of Computer Engineering Pune India

ISBN: (纸本)9798331300579

It is the responsibility of every individual to ensure the cleanliness and sustainability of the environment. We have created a waste management model that utilizes various sensors such as soil moisture sensors, ultrasonic sensors, and inductive proximity sensors, along with a conveyor belt, to automate the process of waste segregation. The ultrasonic sensor is used to detect dry waste based on its distance from the sensor, while the soil moisture sensor is employed to identify wet waste by measuring the moisture content in the waste. The inductive proximity sensor plays a crucial role in identifying metal waste by detecting any metallic objects present in the waste stream. The entire system is designed to streamline the waste separation process, thereby minimizing the need for manual sorting and enhancing overall efficiency. By integrating these sensors with the conveyor belt mechanism, the system enables real-time waste detection and segregation, thereby optimizing waste management operations. The proposed intelligent waste management system offers numerous advantages, including enhanced precision in waste categorization, reduced human errors, increased efficiency in waste collection, and the potential for recycling and resource recovery. © Grenze Scientific Society, 2024.

关键词： Soil moisture

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Enabling technology Integrated Learning for Autistic Children Using Augmented Reality Based Cognitive Rehabilitation

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SN computer Science 2024年第1期5卷 151页

作者： Abdullah, A. Sheik Karthikeyan, J. Gomathi, V. Parkavi, R. Rajarajeswari, P. School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering National Engineering College Kovilpatti India Department of Information Technology Thiagarajar College of Engineering Madurai India

Augmented reality (AR)-based rehabilitation is an excellent teaching tool (ICT tool) for simulating daily activities for autistic children. Children with autism may become more mentally capable with this type of AR-based rehabilitation. It is intended to observe the children's performance in terms of concentration, attention, and identification. The observation has been done through placards as a target image to display the 3D objects on a mobile phone or tablet. In this project, observations are made for 21 autism children in the age group of 7–14, out of whom 17 are boys and 5 are girls. Those 21 children are given practice identifying 15 different objects in an augmented reality environment. Their performance was initially evaluated using conventional instructional techniques. The majority of the kids were having more difficulty identifying things during that observation. Then, with an Augmented Reality environment, the identical observation has been made once more. Using a mobile device or tablet, the 3D objects from the provided placard photos are produced in an augmented reality environment with animation and voice in the languages of English and Tamil. Children with autism are able to recognize and also grasp the behaviors of those objects while viewing them in 3D. Their efforts are measured using a two-point scale (0, 1, 2). The pre-assessment and post-assessment reports for the above observations are tabulated. All the observations are made in the presence of the special education teacher (therapist). However, the children observed in this project fall into three different categories: mild, moderate, and severe. In the Mild category, statistical significance is evident with p values of 0.002 in pre-assessment and 0.014 in post-assessment. Likewise, in the Moderate category, where p values are 0.023 in pre-assessment and 0.033 in post-assessment, significance is observed, as all p values fall below the chosen significance level of 0.05. This leads to rejecti

关键词： Augmented reality (AR) Autism ICT tools Observation Rehabilitation Teaching methodology

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Intrusion Detection Using Federated Learning for Computing

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computer Systems Science & engineering 2023年第5期45卷 1295-1308页

作者： R.S.Aashmi T.Jaya Department of Computer Science and Engineering CSI Institute of TechnologyThovalaiTamilnaduIndia Department of Electronic Communications and Engineering CSI Institute of TechnologyThovalaiTamilnaduIndia

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle *** novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a *** learning is a collaborative machine learning approach without centralized training *** proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown *** main objective is to learn overall behavior of an intruder while performing attacks to the assumed target ***,the updated system model is send to the centralized server in jungle computing,to detect their *** learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious *** our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing *** execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

关键词： Jungle computing high performance computation federated learning false positive rate intrusion detection system(IDS)

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Investigations on optimizing performance of the distributed computing in heterogeneous environment using machine learning technique for large scale data set

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Materials Today: Proceedings 2023年 80卷 2976-2982页

作者： Pandey, Rajeev Silakari, Sanjay Department of Computer Science & Engineering University Institute of Technology RGPV Madhya Pradesh Bhopal India

Enforcing admired machine learning approaches to huge data enhanced novel issues for researchers. Conventional libraries could not suitably fulfil the requirement of complex model with wide variety of data and system parameters. Therefore new methodologies are required performing the computation on more than one machine over distributed environment. Some distributed frameworks on huge data such as MapReduce and TensorFlow have been deployed to solve various machine learning problems in heterogeneous distributed environment. The objective of this paper is providing a wide variety of useful information about platforms, approaches, problems, datasets, and optimization approaches in distributed systems. So researchers have been used the beneficial information to develop new approaches for efficient machine learning. This paper also covers the various formats of data like structured, semi structured and unstructured big data. A brief review of previous works is also represented in text and tabular format to provide motivation to the researchers for developing new paradigm of distributed computing environment. © 2021

关键词： Data mining

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Performance Analysis of Denoising Filters for Ultrasound Fetal Head Images

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SN computer Science 2025年第2期6卷 1-13页

作者： Prasad, Kauleshwar Patnaik, Pawan Kumar Agrawal, Anupam Department of Computer Science and Engineering Bhilai Institute of Technology Chhattisgarh Durg India Department of Electrical and Electronics Engineering Bhilai Institute of Technology Chhattisgarh Durg India

Deep learning has significantly advanced image processing in the medical domain, including the analysis of ultrasound (US) fetal images for prediction of fetal growth retardation. Before analysis of fetal images there is need of correction since they are usually adulterated by noise during acquisition of image. In the recent years, there are various deep learning based denoising methods in which U-Net have attracted the attention of many researchers. This paper describes the comparative study of different filters such as mean filter, gaussian filter, anisotropic diffusion, non-local means (NLM) filter and U-Net based on their performance on denoising fetal images prior to the prediction of fetal growth retardation (FGR). In this article peak signal to noise ratio (PSNR), mean square error (MSE), similarity index measure (SSIM), peak signal to interference ratio (PSIM) and multi scale structural similarity index measure (MSIM) values are analyzed for the performance of these filters. This paper reveals that U-Net performs better in image quality evaluation when compared to other filtering algorithms, based on testing and analytical findings. We have also discussed the significance of MSE, PSNR, SSIM, PSIM, MSIM and U-Net in this article. This article also covers the advantages, disadvantages, and characteristics of different filtering methods, including U-Net. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Denoising techniques Fetal growth retardation U-Net

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