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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Solunke, Babruvan R. Gengaje, Sachin R. Computer Science and Engineering Walchand Institute of Technology Solapur India Electronics and Computer Engineering Walchand Institute of Technology Solapur India

ISBN: (纸本)9789819734658

Accurate and efficient multi-object localization and categorization is one of the key needs for applications of robotic vision, intelligent military surveillance systems, security, and ADAS. It is a significant and complex issue with computer vision that has received a lot of attention. Due to the development of self-driving cars, smart video monitoring, face recognition, and several people tracking services, there is a great demand for quick and precise object detection systems. These methods locate each object by drawing bounding box around it in addition to identify and classify every object in an image or video frame. One of the basic needs for driverless vehicles and many modern driving aid technologies is the ability to recognize and interpret all stationary and moving objects surrounding a vehicle under varying driving and weather circumstances. Convolutional neural network (CNN) technology can provide safety in modern vehicles. This paper analyzes the recent deep learning-based object detection methods, challenges and presents an improved general framework for real-time multi-object detections in ADAS-based on YLOv7 and CNN. We compared the YOLOv7 approach with earlier versions in the YOLO family, and it was found that YOLOv7 outperforms all earlier real-time object detectors to obtain a reliable speed between 5 and 160 frames per second and improved accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in Electrical Networks

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computer Modeling in engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Agriconnect: Revolutionizing Farm Machinery Booking 2

Agriconnect: Revolutionizing Farm Machinery Booking

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Subrahmanya Pradhan, Trilochan Shaikh, Sauda Shabbir Shetty, Ruchitha N. Shreekumar, T. Department of Information Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India Department of Computer Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9798350383867

Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnect is the largest network combining image-based crop analysis with rental equipment to optimize agriculture. Agriconnect uses the latest image processing technology to identify crops from field images, allowing farmers to make informed decisions about their crops. In addition, the platform supports easy-to-assemble equipment, allowing farmers to adapt machines to their specific needs, thus reducing waste and improving resource allocation. Agriconnect simplifies the farming process and increases overall profitability by providing a user-friendly interface for product identification and equipment booking. By combining forward-thinking technology with powerful management tools, Agriconnect not only improves the accuracy of farming but also improves efficiency. He envisions a future in which technology will play a key role in making farming easier and more efficient for farmers around the world. Agriconnect is a prime example of how technology is changing agriculture, enabling farmers to make informed decisions, improving resource use, and ultimately increasing production to implement permaculture practices. © 2024 IEEE.

关键词： Algae control

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Solar-Powered Waterborne Debris Collector Equipped with Sprinkler System 2

Solar-Powered Waterborne Debris Collector Equipped with Spri...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Keerthika, C. Jenitha Blesy, A. Grace Gnanam, J. Narmadha, D. Sundar, G. Naveen School of Computer Science and Engineering Karunya Institute of Technology and Sciences Divison of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350328202

Conventional machine models of water trash collection is here enhanced with the integration of sprinkler system. This enhanced model for water trash collection combines conventional methods integrated with a sprinkler system. The incorporation of the sprinkler system enhances water quality by agitating the surface, elevating oxygen levels, and facilitating the decomposition of organic substances. Moreover, the sprinkler system allows for the introduction of medications and necessary supplements to further improve water conditions. As a primary focus, the collection of debris is carried out with the help of a conveyor belt which Operates with the help of a motor which is connected to the power supply obtained from solar panels. The energy obtained from the solar panel is harnessed and stored for usage in times of less or no sunlight. With the optimized usage of sensors and imaging cameras, the target trash is identified. The direction of movement of the mechanism is instructed by the condition generated on analyzing the objects ahead to be identified as obstacles. The overall features of the mechanism are effective in energy efficiency and data-driven decision contributing to a holistic approach to the majority of the problems faced during the expected phases of operation. © 2024 IEEE.

关键词： Energy efficiency

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Advanced deep learning models for automatic detection of driver’s facial expressions, movements, and alertness in varied lighting conditions: a comparative analysis

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Multimedia Tools and Applications 2025年第6期84卷 2947-2983页

作者： Das, Shiplu Pratihar, Sanjoy Pradhan, Buddhadeb Computer Science and Engineering Indian Institute of Information Technology West Bengal Kalyani India University of Engineering and Management Kolkata West Bengal New Town India

Emotion recognition is vital in understanding human behavior and improving the driving experience. We present a systematic analysis of automatic detection of driver’s facial expressions and movements while crying, feeling sad, experiencing anger, drowsiness, fear and surprise to reduce road accident using a deep learning method in varied lighting conditions. The proposed approach aims to accurately detect and analyze driver emotions in real time, it would help in advancing the growth of smart driver assistance systems. To conduct our analysis, we create a comprehensive dataset comprising annotated facial images of drivers exhibiting crying, sadness, anger, drowsiness, fear and surprise. The dataset is carefully designed to encompass diverse lighting conditions, facial orientations, and driver demographics, ensuring the generalizability and robustness of the proposed method. The CNN-BiLSTM model is compared against the CNN, LSTM, CNN-LSTM, BiLSTM, and CNN-LSTM models in the research to perform precise localization of facial expressions and movements. The trial findings show how well our technique accurately identifies and evaluates a driver’s facial expressions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Advanced driver assistance systems

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Advanced NID-VGG16 with Orca Predation Optimization Based 1DCNN-BiLSTM for Network Intrusion Detection 14

Advanced NID-VGG16 with Orca Predation Optimization Based 1D...

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14th International Conference on Cloud Computing, Data science and engineering, Confluence 2024

作者： Ratna Kumari, K. Durga, N. Gayathri, T. Sundari, Ch. Lalitha Syama Uppalapati, Padma Jyothi Shri Vishnu Engineering College for Women Computer Science and Engineering Bhimavaram India Sasi Institute of Technology and Engineering Information Technology Tadepalligudem India Vishnu Institute of Technology Computer Science and Engineering Bhimavaram India

ISBN: (纸本)9798350344837

Many cybersecurity dangers exist for modern computer networks, which emphasize how crucial Network Intrusion Detection (NID) is necessary. The ever-changing threat landscape has made it difficult for traditional intrusion detection systems (IDS) to remain successful, which emphasizes the growing significance of artificial intelligence (AI) solutions. To improve threat detection effectiveness, the paper used Deep Learning-powered network intrusion detection (DL-powered NID) in this study. The tests utilized two datasets: the Canadian institute for Cybersecurity 2017 (CICIDS2017) dataset and Internet of Things network traffic (IOT23) captures to verify the efficacy of this technique. Preprocessing the dataset entails applying the Preprocessing and Minimax Scaling (PMS) method, which includes filtering, transforming, and normalizing the data. The paper offer the NID-VGGI6 framework, a 16-convolution-layer network intrusion detection visual geometry group based on Convolutional Neural Networks (CNNs), for feature extraction. In order to provide a class and scale-invariant architecture, this framework combines multilevel and multiscale features with data augmentation approaches. During NID-VGGI6 training, the focal loss function solves class imbalance, and the Flatten-T Swish (FTS) activation function reduces gradient vanishing and explosion problems. In order to improve decision-making in NID, a one-dimensional CNN-based Bidirectional Long Short-Term Memory (1DCNN-BiLSTM) model is used for classification after feature extraction. The paper utilized the Orca Prediction Optimization Algorithm (OPOA) to fine-tune classification hyperparameters for maximum accuracy. The findings show that the suggested model performs better than the existing ones and reaches an impressive 99.9% accuracy rate for both the datasets. © 2024 IEEE.

关键词： Feature extraction

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Integrated IoT based Smart Agriculture Monitoring System 6

Integrated IoT based Smart Agriculture Monitoring System

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Saraswathi, R. Vijaya Rahul, L. Lokeswar, S. Varma, R. Vathsav Ajay, U. Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

ISBN: (纸本)9798331522667

The adoption of IoT technology in agriculture is revolutionizing traditional farming, making it a data-driven and resource-efficient field. Proposed paper introduces an IoT-based smart agriculture monitoring system designed to provide farmers with accurate real-time insights for improved crop and resource management. The system incorporates a network of sensors to monitor essential environmental variables like soil moisture and temperature. These signals are processed by a microcontroller and securely transmitted to a cloud platform for advanced analytics. A mobile-friendly application offers actionable insights, notifications, and remote access, facilitating informed decision-making. Built on a Wi-Fi-based infrastructure, the system ensures reliable communication even in areas with inconsistent internet connectivity. Field implementations reveal notable advancements in resource utilization, crop yields, and labor efficiency. Additionally, an embedded AI module forecasts crop prices with 92% accuracy, enabling farmers to optimize sale timings for increased profitability in competitive markets. This comprehensive solution highlights the transformative potential of IoT in fostering sustainable and profitable agricultural practices. © 2025 IEEE.

关键词： Smart agriculture

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Ensemble activation enabled deep classifier for Alzheimer's disease detection in the blockchain-enabled distributed edge environment

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International Journal of Information technology (Singapore) 2024年 1-18页

作者： Mandawkar, Umakant Diwan, Tausif Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur India Department of Computer Engineering SVKM’s Institute of Technology Maharashtra Dhule India

Alzheimer's disease (AD) is the most well-known cause of dementia that affects memory. Alzheimer's patients have a neurodegenerative disorder that results in the loss of many brain functions. Today’s researchers have determined that the key to enhancing patient care and treatment is early disease diagnosis. Traditional techniques for diagnosing AD are difficult ineffective, and take a lot of time to learn and execute. In this research, a disease detection system is developed using the proposed tawny flamingo-based federated deep CNN (Convolutional Neural Networks) (TF based-deep CNN) which can manage accurate patient diagnosis in dispersed contexts with blockchain support. The collected data is maintained and structured in a blockchain-based database, which upholds a high level of data security and increases the detection accuracy. After the medical professional receives the data, disease detection is done using the proposed federated learning framework, which makes it feasible to manage remote data effectively. The features are then extracted from the segments created by the CUF-GW (Cuttlefish-GreyWolf) algorithm. The detection is then carried out by the proposed ensemble activation-based deep CNN classifier, with the ensemble process being assisted by the TF (Tawny Flamingo) approach. According to AD accomplishments, at TP (Training Percentage) 80, acc (Accuracy), sen (Sensitivity), and spe (Specificity) are respectively 99.19%, 99.99%, and 98.87%;similarly, based on the k-fold 10, acc, sen, and spe are respectively 99.45%, 99.99%, and 98.45%. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Alzheimer's disease CUF-GW algorithm Deep CNN Federated learning TF

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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Cohesive Heartbeat Monitoring for Geolocation and Log Correlation in Campus Networks 15

Cohesive Heartbeat Monitoring for Geolocation and Log Correl...

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

作者： Suganya, R. Shazil Nath, Hiran V. Vellore Institute of Technology Chennai Department of Computer Science and Engineering Chennai India Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India National Institute of Technology Department of Computer Science and Engineering Kozhikode Calicut India

ISBN: (纸本)9798350370249

A campus network is made up of a dense interconnection of local area networks within a limited geographical area characterized by a centralized infrastructure that interconnects various facilities within an institution, providing communication and data exchange services to support organizational activities. In a campus network, relevant information about connected clients such as connection status, duration, and relative location has to be maintained and updated in real-time to facilitate availability, log correlation and monitoring capabilities. It is important to establish a multi-pronged system in place to combat security threats and potential data loss. In this paper, we examine several existing standard practices and identify the operational and functional bottlenecks associated with each implementation with respect to the campus network. The study underscores the critical need for robust and adaptive network architectures and practices to safeguard organizational communication and data integrity with emphasis on the dynamic landscape of campus environments. Finally, we propose a novel cohesive heartbeat monitoring system that leverages data from various sources to ensure connection availability and provide advanced logging capabilities. © 2024 IEEE.

关键词： 802.1X campus network heartbeat logging RADIUS SNMP

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