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1st International Conference on Software, Systems and Information Technology, SSITCON 2024

作者： Singh, Neelam Upreti, Manisha Dwivedi, Ayushi Rawat, Vandana Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350352931

A species is biologically basic classification and taxonomic rank of an organism. Among the many common names for the mammals that make up the suborder Simiiformes (sometimes known as the simians), 'monkey' is among the most common. The traditional classification of all members of the group currently known as simians as monkeys. Different classifiers are used to categorize the various kinds of monkeys based on their photos. Assigning a name or class to an image is the work of image classification. In the research paper, different image classifiers of Keras are used for image classification in monkey species. A dataset of 10 distinct monkey species was used to test out many different classifiers. Keras comes preloaded with a variety of Convolutional Neural Networks, including VGG16, VGG19, ResNet50, ResNet-50 V2, ResNeXt101, Inception V3, and Xception. We compared the performance of other classifiers and discovered that ResNeXt-101 achieved the best accuracy (99.98%). © 2024 IEEE.

关键词： Invertebrates

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Reliable and Stable Routing in VANET Using Multi-Objective Function Aided K-Means Clustering with Heuristic Algorithm 1

Reliable and Stable Routing in VANET Using Multi-Objective F...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Das, Nabanita Barooah, Maushumi Das, Anupam Assam Engineering College Jalukbari Department of Computer Application Assam Guwahati781013 India The Assam Royal Global University Betkuchi Department of Computer Science and Engineering Guwahati781035 India

ISBN: (纸本)9798350307795

The expansion of Vehicular Ad-hoc Networks (VANET) has brought a heightened focus on service delivery within the realm of the smart transit systems. Nevertheless, the mobility attributes inherent to vehicular networks often result in frequent route disruptions, particularly when transmitting data. This circumstance holds true in both advanced and emerging nations, where substantial time is wasted due to congested traffic conditions. These consequences are substantial, encompassing heightened stress levels for drivers, reduced efficiency for diverse personal and commercial vehicles, and escalated emissions of harmful gases, especially those that contribute to air pollution, affecting the well-being of residents in densely populated regions. Clustering stands as a formidable approach for establishing a stable topological arrangement. Therefore, an efficient reliable routing scheme is developed in VANET with the help of a clustering algorithm. The node clustering is initially performed with the help of the K-means Clustering algorithm (KMC) in VANET. An Advanced Pelican Optimization Algorithm (APOA) is utilized for choosing a shorter and more stable route between all available routes to make efficient routing in VANET. The multi-objective functions like delay, hop count, energy, and the ratio of packet delivery are considered for the efficient routing in VANET to make energy-efficient transmission. The effectiveness of the proposed KMC-based optimal routing in VANET is validated with the conventional routing schemes in accordance with some of the metrics to determine efficacy. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Research on Top-Down Collaborative Product Design Method

Research on Top-Down Collaborative Product Design Method

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2024 China Automation Congress, CAC 2024

作者： Ren, Qiang Ren, HaiLei Li, Ling Zhang, Shaokai An, Baoran Ma, Tao Institute of Computer Application China Academy of Engineering Physics MianYang China Institute of Fluid Physics China Academy of Engineering Physics MianYang China

ISBN: (纸本)9798350368604

This paper introduces a top-down collaborative design methodology rooted in the concept of initiating product development from a holistic perspective, encompassing overall scheme planning, layout design, and subsystem refinement, down to detailed component engineering. This approach transcends software platform constraints and harmoniously integrates with the R&D workflows prevalent across industries, especially in large-scale, interdisciplinary product development endeavors. The core of our proposal revolves around a skeleton model-based framework that encompasses the establishment of a collaborative platform, outlining a top-down collaborative design strategy, and facilitating parallel design efforts across departments. This ensures that product conception stems from a unified vision, with seamless intent communication and efficient multi-disciplinary labor division, ultimately culminating in a comprehensive digital prototype equipped with models suitable for advanced applications like finite element analysis and motion simulation, fostering innovation and efficiency in the entire design lifecycle. © 2024 IEEE.

关键词： Musculoskeletal system

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Classification of Potato Leaf Disease using Deep Learning 3

Classification of Potato Leaf Disease using Deep Learning

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Singh, Neelam Sharma, Deeksha Dev, Saurav Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India

ISBN: (纸本)9798350354171

Deep learning is one of the subdomains of Artificial Intelligence. With the advantages of automatic learning and feature extraction, in recent years, it has become a hot topic in academic and industrial circles. Meanwhile, this has emerged as one of the hotspots within agricultural plant protection research, including plant disease recognition and pest range assessment. Deep learning in plant disease recognition can avoid the shortcomings of artificial selection of features of the disease spot, making the feature extraction of plant diseases more objective, promoting research efficiency, and increasing technological transformation speed. The goal of this work is to analyze potato leaf diseases using CNN in order to determine the model's optimal performance using a large dataset (10,000 photos) of potato leaves in the analysis. The most effective model will support farmers in making decisions that will keep potato production from declining. This investigation revealed that the model worked better when forty epochs were applied to the dataset of 10,000 photos. Additionally, three different epochs were applied and achieved 100% accuracy for 30 and 50 as achieved 99.97% and 99.98% respectively. By offering recommendations for how to categorise Potato Leaf Disease with the best result, the study made a substantial contribution to the agricultural industry and farmers. While working with big volumes of data, researchers should be concerned about the system because running code takes longer time. To focus this, a dataset including 2152 photos was gathered from Kaggle, with an additional 7848 images sourced from various platforms such as Google and raw data from Potato Field. To determine which epochs performed the best, a range of epochs were applied and results shows that 40 epochs led to 100% accuracy. © 2024 IEEE.

关键词： artificial intelligence deep learning potato disease

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Anti-Collusion Mechanism for Audio Signals Based on Pre-Adjustment 3

Anti-Collusion Mechanism for Audio Signals Based on Pre-Adju...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Shekhawat, Jyoti Khurana, Rashmi Vivekananda Global University Department of Computer Science Engineering Jaipur India Chandigarh School of Business Master of Computer Application Department of Computer Science Engineering Jhanjeri India

ISBN: (纸本)9798350399264

One of the most well-known and difficult attacks for audio transmissions that gravely infringes on copyright is collusion. The mixture attack by collaboration and other assaults, is too sophisticated for conventional defenses to handle. In this study, we propose by removing the perceived quality of a pre-adaptation process (PAP)-based strategy to collusion signals by removing the incentive for traitors to carry out covert attacks. The PAP signal is then produced by modifying the DCT coefficients in accordance with an adjustment matrix (AM) that has already been designed. The vigor of some frequency groups in the colluding signal will be lost or lowered when numerous PAP signals are averaged to produce it, which significantly lowers the colluded signal's perceptual quality. The suggested technique is resistant against hybrid as well as collusion assaults. Additionally, it is safe because the receiver won't receive access to the PAP secret keys. The suggested copyright protection technique may be further enhanced in terms of effectiveness by being integrated with other state-of-the-art watermarking algorithms. The proposed mechanism is superior, as demonstrated by theoretical analysis and experimental findings. © 2023 IEEE.

关键词： Copyrights

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

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1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Unveiling the Dynamics of Online Culinary Convenience: A Systematic Exploration 2

Unveiling the Dynamics of Online Culinary Convenience: A Sys...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Bhatt, Chandradeep Bisht, Anurag Sharma, Sanjay Gupta, Shefali Singh, Teekam Graphic Era Hill University Cse Department Dehradun India Graphic Era Hill University Computer Science and Engineering Dehradun India Sgrr Univeristy Computer Application Department Dehradun India Chitkara University Institute of Engineering and Tech Chitkara University Punjab India Graphic Era Deemed to Be University Computer Science and Engineering Dehradun India Sgrrits Computer Application Department Dehradun India

ISBN: (纸本)9798350306415

The industry for online food delivery has grown quickly, changing how customers access and use food services. Finding foods that are suitable for their diets and leanings among the dozen selections offered to customers and users is frequently difficult. For this study, existing research on customer behavior in online food ordering systems is reviewed in order to identify any gaps where intelligent advocacy systems can be very helpful. The proposed work focuses on utilizing sophisticated machine learning algorithms and approaches to create personalized food advice that takes into consideration user preferences, previous order history, and contextual data. The findings of the empirical study offer priceless insights into the efficiency of intelligent suggestion systems and their influence on user engagement. The results provide guidance for future advancement in the sector and help to understand consumer preferences as well as the difficulties that online meal ordering platforms have had to overcome. My suggested system includes an online food ordering option that makes it simple for clients. My system makes it simple to purchase food online from restaurants and use the mess service. This study looks into how the online food ordering landscape is changing and aims to offer opinions on how intelligent advising systems could improve user experience. The ultimate goal of this is to help companies devise plans to enhance customer service and spur expansion in the online food ordering sector. © 2024 IEEE.

关键词： DOM E-Commerce JWT machine learning

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Identification of Age, Plumage, and Sex of Bird Species Using Pre-Trained Deep Convolutional Neural Network 2

Identification of Age, Plumage, and Sex of Bird Species Usin...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Banerjee, Sourik Gupta, Richa Pandian, J. Arun Saranya, S. Mariappan, L. Thanga Vellore Institute of Technology Department of Computer Application Vellore India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Tiruchirappalli India

ISBN: (纸本)9798350306415

The research aims to identify the age, plumage, and sex of bird species using standard Pre-trained Deep Convolutional Neural Networks (Pre-DCNNs). The proposed work involves collecting various bird images, which are then used for training and validating the pre-trained DCNNs. The dataset contains 200 images of four bird species. The dataset was split into twelve labeled classes of images based on age, plumage, and sex. The standard image manipulation techniques such as cropping, flipping, mirroring, rotating, shearing, and skewing were used as image augmentation techniques to increase the size of the proposed dataset. The proposed data is preprocessed, including feature extraction and data cleaning, to ensure the performance of the Pre-Trained DCNNs. The Pre-Trained DCNNs were trained on 90% of the dataset to recognize patterns in bird images. The accuracy of the pre-trained DCNNs was evaluated using metrics such as classification accuracy. The research acknowledged the advantages and limitations of the standard Pre-Trained DCNNs on the age, plumage, and sex of birds using the images. Also, it compares the performance of the Pre-Trained DCNNs on the proposed bird species image dataset. © 2024 IEEE.

关键词： Bird Plumage Identification Bird Species Identification Deep Convolutional Neural Networks Image Augmentation Pre-trained networks

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Study on Intelligent Decision Architecture of Online Data Acquisition and Analysis System 3

Study on Intelligent Decision Architecture of Online Data Ac...

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3rd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2024

作者： Yu, Chenglong Liu, Daoyuan Yan, Hongpo Li, Qiang Gong, Maozhen Beijing Institute of Computer Technology and Application Beijing China Jincheng Nanjing Engineering Institute of Aircraft System Nanjing China

ISBN: (纸本)9798331529222

To better improve the production capacity and control efficiency of discrete manufacturing workshops, a decision system architecture based on online acquisition and data analysis is proposed, and its architecture and operation process are described. Furthermore, the key supporting function such as manufacturing data detection and acquisition based on internet of things, data analysis for production control, production optimization decision based on data analysis were further studied. Ultimately, a prototype system was built, as well as its functionality was validated by the past actual data. It has good reference value to provides technical support and software foundation for subsequent research and performance verification. ©2024 IEEE.

关键词： Smart manufacturing

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Correction to: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India (International Journal of Environmental Science and Technology, (2024), 10.1007/s13762-024-06266-4)

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International Journal of Environmental Science and Technology 2025年 1-2页

作者： Kavathekar, V. Tripathy, A.K. Chettri, S.K. Bhanage, V. Computer Science and Engineering Computer Application Assam Don Bosco University Assam India Computer Engineering Don Bosco Institute of Technology Mumbai India PHIS Centre The IDEC Institute Hiroshima University IDEC Bldg. 1-5-1 Kagamiyama Higashi-Hiroshima739-8529 Japan

The original version of this Article contained an error in the representation of elevation values depicted in the Fig. 1. Incorrect version of Fig. 1 Geographical location of the Mumbai metropolitan region contributes to achieving the region’s sustainable development goals. (Figure presented.) Readers should consider following Figure as Fig. 1. Correct version of Fig. 1 Geographical location of the Mumbai metropolitan region (Figure presented.) © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and Science and Research Branch, Islamic Azad University 2025.

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