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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Joshi, Aditya Deshmukh, Ashwini Auti, Abhijeet Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Trinity College of Engineering & Research Department of Mechanical Engineering Pune India

ISBN: (纸本)9798350319088

With the global population steadily gravitating towards urban living, the demand for innovative and sustainable waste management solutions is paramount. This paper presents a comprehensive framework for Smart Waste Management (SWM) designed to address the escalating challenges of waste disposal in urban areas. It integrates cutting-edge technologies, including IoT-enabled sensors and real-time data analytics, to transform traditional waste collection processes. Emphasizing the significance of efficient waste management, the paper explores existing approaches and initiatives worldwide, with a particular focus on Sweden's successful waste management practices. The proposed framework introduces a systematic approach to waste segregation at its source, deploying colour-coded bags and bins for effective categorization. A diverse array of sensors, including ultrasonic and infrared can be seamlessly integrated into the framework. These sensors, in conjunction with microcontrollers and cloud storage, form a robust Smart Garbage Bin (SGB) network. The data generated by the SGBs undergoes meticulous processing, contributing to optimal route selection and dynamic route optimization, the Vehicle Tracking System (VTS), a waste monitoring application, provides real-time monitoring of waste collection operations, enhancing transparency and operational control. The proposed framework aims to revolutionize waste management, offering an environmentally sustainable, cost-effective, and efficient solution for urban areas facing burgeoning waste challenges. © 2023 IEEE.

关键词： Smart city

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Comparative research on block design-based protocol and blowfish for secured key generation and data sharing on cloud

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Soft Computing 2024年第23期28卷 13701-13713页

作者： Hombal, Uma Rangapura Basavaraju, Dayananda Shinde, Ashwini S. Department of Computer Science and Engineering KSIT Bengaluru India Department of Computer Science and Engineering MSRIT Bengaluru India Department of CSE-AI Nutan College of Engineering and Research Pune India

Confidentiality of maintaining the Electronic Health Records of patients is a major concern to both the patient and Doctor. Sharing the data on cloud is one of the most efficient technology infrastructures with extensive potential application, but at the same time there is a threat of hacking the data. Here we implement the concept for sharing of electronic health record on cloud that permits multiple members of the group to easily share the group data. With the increase in size of healthcare data the time and space complexity increases. The main challenge here is data security. This research article emphasis on a secured method for sharing the data on cloud and generation of the key that is safe from unauthorized user. In this research article, we implement the soft computing approach for key agreement protocol using Block based design to support several users that can adapt it-self to upsurge the number of users in the cloud which depends on the assembly of the block design. This protocol generates a common conference key for all members. On comparing our results with some prevailing techniques, we accomplish that the results of our research have a better computational complexity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Personal computing

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Knowledge Graph Representation Learning based on Entity Semantic Distance Classification

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computer-Aided Design and Applications 2024年第S7期21卷 256-269页

作者： Zhang, Yi Cao, Wanhua Liu, Juntao Wang, Yuanbin Rao, Ziyun College of Computer Science and Technology Harbin Engineering University Harbin China Wuhan Digital Engineering Research Institute Wuhan China

In recent years, significant progress has been made in knowledge graph representation learning, which has shown promising results in knowledge computing applications such as relation extraction and knowledge reasoning. However, the unbalanced distribution of relations and entities in knowledge graphs leads to low training efficiency. To address this issue, this paper proposes a novel knowledge representation learning method based on entity distance classification. This method classifies entities based on their semantic distance on a specific relational plane, and employs different training strategies to increase the training opportunities for entities with low semantic distance differentiation. Moreover, the loss function is adjusted by introducing different residual weights, which allows for the allocation of different training opportunities to negative samples. The effectiveness of our approach is demonstrated by comparing it with mainstream knowledge representation models on various benchmark datasets. © 2024 U-turn Press LLC,.

关键词： Knowledge graph

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Real Time Monitoring of Bridge by Using Sensor Technology with Concentration on Deflection Identification 1

Real Time Monitoring of Bridge by Using Sensor Technology wi...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Kokane, Sayali. R. Jadhav, Pramod Bharati VIdyapeeth College of Engineering Department of Computer Engineering Pune India Bharati Vidyapeeth College of Engineering and Research Department of Computer Engineering Pune India

ISBN: (纸本)9798350355611

This research paper inscribes an alarming need for effective bridge health monitoring and controlling amid growing concerns over structural integrity and safety. India has been inherited by n number of rail bridges and roadway bridges over valleys, rivers, hills etc. since british era. Many bridges in urban India near rivers face deterioration over time despite remaining operational. This poses risks to users due to factors like heavy vehicles, rising water levels, pressure variations, and heavy rainfall all potentially leading to collapses and disasters. Apart from these usual factors, earthquake is also one of the most disastrous factor which poses a great risk to the safety and structural integrity of bridges. Hence, it is of an utmost importance that periodic audits of structural integrity and safety of these bridges is critically evaluated. In this study we put forward development of a smart bridge system equipped with a network of sensors controlled using IoT system. We have developed a model prototype of a smart bridge wherein, sensors monitor and responds to variables such as water levels and structural deflections, vibrations, object detection etc. on a real time basis. The sensors we used included flex sensors which monitor deflections, IR sensors to detect objects, water sensors monitoring water level and vibration sensors. In addition to the conventional parameter evaluation, application of the modern sensor based techniques in combination with IoT controlling provide an enhanced system for monitoring of structural integrity and safety of the bridges. Should any monitored parameter exceed its safe threshold signifying a risk of collapse, the system triggers alerts via a monitoring system and activates automatic barriers. Additionally, the controlling system codes provide multidimensional approach of monitoring. This proactive approach aims to enhance safety and prevent potential disasters caused by bridge failures. Daily analysis carried out on the data

关键词： Structural health monitoring

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"affectAlchemy": An Affective Dataset Based on Plutchik's Psychological Model for Text-Based Emotion Recognition and its Analysis Using ML Techniques 8

"affectAlchemy": An Affective Dataset Based on Plutchik's Ps...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Kapase, Ajay Uke, Nilesh Savant, Jatin Desai, Mrunali Ghatage, Shravani Rahangdale, Atul Vishwakarma Institute of Information Technology Computer Science & Engineering Pune India Trinity Academy of Engineering Computer Science & Engineering Pune India KITs College of Engineering Computer Science & Engineering Kolhapur India

ISBN: (纸本)9798350391770

A technique to identify people's attitudes, and sentiments towards specified targets such as things, services, and subjects, is called sentiment analysis. As a dedicated subset of NLP, it deals with predicting specific reactions broadly characterizing sentiments as positive, negative, or neutral. However, the accuracy and depth vary since there are a variety of human emotions that exist rather than just positive, negative, and neutral. In the past few decades, the term affective computing has become well-liked which involves the use of AI for identifying, interpreting, and responding to human emotions. A part of it called text-based affective systems are artificial emotional intelligent systems that often categorize the text into core wide range of emotions like fear, sadness, anger, and happiness. These affective systems use various psychological models for emotion recognition. Our study incorporates the development of a dataset based on Robert Plutchik's psychological model of emotions, which sets forth eight primary emotions which are combined to form more complex emotions, resulting in a total of 24 secondary emotions resulting total of 32 variety of emotions. The work introduces the "AffectAlchemy"dataset which is validated using multiclass classification machine learning techniques. This dataset can contribute to the field of affective computing for the development of models that can do enhanced emotion recognition, and improved emotional context understanding to develop emotion-aware applications and facilitating research in affective systems. © 2024 IEEE.

关键词： Intelligent systems

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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Design of Smart Wearable for Quality Analysis 2

Design of Smart Wearable for Quality Analysis

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： More, Sujeet Hosur, Ravi Arsalwad, Gajanan Sayyad, Ayesha Raskar, Ishwari Pande, Deepti Trinity College of Engineering and Research Department of Information Technology Pune India BLDEA's College of Engineering Department of Computer Science and Engineering Vijayapura India

ISBN: (纸本)9781665491990

This study introduces an advanced portable device using reliable wireless technology to establish the communication between people and nearby equipment. Due to modernization, the electronic devices require a compact antenna. The compact size, low cost, and better efficiency is the key requirement of the current research. This study is mainly focused on the ultra-wide band antenna of notched antenna, which is simulated using different substrate mounted on a ground and is generally attached to some part of the ground surface. A rectangular substrate with dimensions 25mm×23mm×1.5mm is inserted within the ground and patch. The dimensions of radiating antenna placed on the substrate is 15mm×15mm×0.035mm and for the Ultra-Wideband (UWB) antenna the resonant frequency is 4.8GHz and 10.3GHz respectively and its fractional bandwidth is 110% in the frequency range of the 3.1GHz to 10.7GHz. The simulation is performed in HFSS 2021. After obtaining all the parameters of the antenna, which is comparatively analyzed by using the different types of the substrate material like FR4, jeans, rubber, Rogers RT 5880 etc. The proposed antenna is useful for the wearable applications generally for the on or off body centric communication. © 2023 IEEE.

关键词： Substrates

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Segmentation of Head and Neck Tumors Using Dual PET/CT Imaging:Comparative Analysis of 2D,2.5D,and 3D Approaches Using UNet Transformer

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computer Modeling in engineering & Sciences 2024年第12期141卷 2351-2373页

作者： Mohammed A.Mahdi Shahanawaj Ahamad Sawsan A.Saad Alaa Dafhalla Alawi Alqushaibi Rizwan Qureshi Information and Computer Science Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Software Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Computer Engineering Department College of Computer Science and EngineeringUniversity of Ha’ilHa’il55476Saudi Arabia Department of Computer and Information Sciences Universiti Teknologi PetronasSeri Iskandar32610Malaysia Center for Research in Computer Vision(CRCV) University of Central FloridaOrlandoFL 32816USA

The segmentation of head and neck(H&N)tumors in dual Positron Emission Tomography/Computed Tomogra-phy(PET/CT)imaging is a critical task in medical imaging,providing essential information for diagnosis,treatment planning,and outcome *** by the need for more accurate and robust segmentation methods,this study addresses key research gaps in the application of deep learning techniques to multimodal medical ***,it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a *** primary research questions guiding this study are:(1)How can the integration of convolutional neural networks(CNNs)and transformer networks enhance segmentation accuracy in dual PET/CT imaging?(2)What are the comparative advantages of 2D,2.5D,and 3D model configurations in this context?To answer these questions,we aimed to develop and evaluate advanced deep-learning models that leverage the strengths of both CNNs and *** proposed methodology involved a comprehensive preprocessing pipeline,including normalization,contrast enhancement,and resampling,followed by segmentation using 2D,2.5D,and 3D UNet Transformer *** models were trained and tested on three diverse datasets:HeckTor2022,AutoPET2023,and *** was assessed using metrics such as Dice Similarity Coefficient,Jaccard Index,Average Surface Distance(ASD),and Relative Absolute Volume Difference(RAVD).The findings demonstrate that the 2.5D UNet Transformer model consistently outperformed the 2D and 3D models across most metrics,achieving the highest Dice and Jaccard values,indicating superior segmentation *** instance,on the HeckTor2022 dataset,the 2.5D model achieved a Dice score of 81.777 and a Jaccard index of 0.705,surpassing other model *** 3D model showed strong boundary delineation performance but exhibited variability across datasets,while the

关键词： PET/CT imaging tumor segmentation weighted fusion transformer multi-modal imaging deep learning neural networks clinical oncology

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YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enhanced Performance using Deep Learning 3

YOLOv8: A Novel Vehicle Mirror Detection Algorithm with Enha...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Narayanan, Sundarakrishnan Donthi, Yashas Mohana Ramakanth Kumar, P. Sreelakshmi, K. Rv College of Engineering® Center for Computer Vision Research Bengaluru India Rv College of Engineering® Center for Computer Vision Research Bengaluru India

ISBN: (纸本)9798331509675

In recent times, Artificial intelligence (AI) and machine learning (ML) technologies have demonstrated remarkable capabilities in addressing real time needs and challenges across multiple domains and fields. In the world of deep learning, various Neural Network architectures like DNN, CNN, and RNN have been instrumental in advancing solutions to complex problems spanning multiple industries, business environments and law enforcement use cases. This paper implemented a special type of CNN, YOLOv8(You Only Look Once), a SOTA architecture to detect the presence of rear-view mirrors in two-wheelers for traffic surveillance and law enforcement applications. The proposed research introduces a novel idea in the realm of road safety by presenting the first ever paper on vehicle mirror detection. This approach sheds light on an often-overlooked aspect, contributing significantly to the enhancement of road safety measures. A custom dataset of 1246 training images, 155 test images and 157 validation images has been considered. This dataset has been used to train and test a binary classification model with the label "mirror". Images with the label "mirror"indicate the presence of one or both rear-view mirrors on two-wheelers. On the other hand, images with no mirrors are treated as "background"images. The model achieved a maximum precision of 93.8%, recall of 87.9%, mAP50 of 94.1% and F1-score of 90.9%. These results stand as evidence of the robustness and proficiency of the model, ensuring that it can be potentially used in real world use cases, specifically for law enforcement applications. © 2025 IEEE.

关键词： Convolutional neural networks

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Efficient Smart Irrigation System using Internet of Things (IoT) for Agriculture Crops 3

Efficient Smart Irrigation System using Internet of Things (...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Ambika, G. Mohana Sudarshan, S. Kavitha, A. Sudhangowda, B.S. Nataraj, K. Rv College of Engineering® Center for Computer Vision Research Bengaluru India Rv College of Engineering® Center for Computer Vision Research Bengaluru India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509675

In this paper, application-based solution implemented for smart irrigation system using IoT and sensors. The proposed model provides a prediction of the user's time before which plant must need spraying, to compare water content present in soil and measure surrounding temperature and humidity in real-time. This application provides intelligent predictions which depend on environmental factors. If water is required, the tank valve is automatically opened and will be closed if water is not necessary. Nutrients dissolved in water are transported to each part of the plant. Water also protects plants from both frost and hot air currents. To maintain moisture of soil for efficient plant growth, it must be watered regularly. Instead of watering a little every day, opting for a more thorough watering at intervals is comparatively better. water needs to trickle below surface and reach end of pot. This ensures better root development. In this paper, as a first level plants of rose are considered, and first performed statistical analysis to find out the relationship between soil moisture with temperature and humidity. On a day, data is collected at three instances that is at morning, afternoon, and evening on both rainy and normal. This categorization is done as soil moisture and rate of evaporation differs at each different instance. On a rainy day, rate of evaporation will be nearly 0.0384614 due to more humidity and will have soil moisture in between 70% to 78% so plant may not need water whereas on a normal day, during afternoon there will be rate of evaporation nearly equal to 0.051156 which is more among all other cases and less soil moisture of 52% to 63% so watering is required. © 2025 IEEE.

关键词： Soil moisture

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