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

作者： Cheshta Garg, Anand Gupta, Nishant Department of Engineering & Computer Science Sharda University of Engineering & Technology UP Greater Noida India

ISBN: (纸本)9798331300579

This research project aims to automatically control vehicle speed in zones with speed limits, such as those designated for educational and medical purposes, in addition to occurrences of *** select regions, transmitters operating at a frequency of 433 MHz are strategically *** transmitters emit an uninterrupted radio frequency (RF) *** a vehicle encounters this radiation, its internal receiver becomes *** a vehicle traverses into a designated area, it receives a signal that governs its ***, the vehicle's speed is reduced to a predetermined level and remains at that value until it departs the ***, the vehicle may resume acceleration autonomously. © Grenze Scientific Society, 2024.

关键词： Radio frequency identification (RFID)

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Achieving Ultra-low Latency for Timeout-less Congestion Control in Data Center Networks 22

Achieving Ultra-low Latency for Timeout-less Congestion Cont...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Zou, Shaojun Jiang, Yi Qu, Jiacheng Zhang, Tao Hu, Yuanzhen Peng, Yujie Hainan University School of Computer Science and Technology Haikou China Changsha University School of Computer Science and Engineering ChangSha China

ISBN: (纸本)9798331509712

Modern data centers are hosting a great number of various applications (e.g. MapReduce and web search) that require a high fan-in data communication, which easily causes serious packet losses and timeouts, substantially degrading the application performance. To address this issue, various host-based and switch-based transport protocols are proposed to eliminate timeout and improve the user experience. Unfortunately, although existing transport protocols can effectively eliminate the timeout, they inevitably result in persistent queueing backlog and degrade the network performance, especially delay-sensitive short flows. To this end, we propose a general scheme with ultra-low latency called UL2 to address the above problem. Concretely, the sender periodically estimates the queueing delay of each packet on the transmission path and senses the degree of congestion based on its measured result. Then the sender timely yet cautiously executes a pausing transmission operation based on measured queueing delay, guaranteeing fast elimination of queue delays and high link utilization. Our evaluation indicates that UL2 can effectively eliminate queue backlog and reduce the queueing delay by more than 90%. Moreover, UL2 enhances the performance of state-of-the-art transport protocols in terms of flow completion times by up to 44.98%. © 2024 IEEE.

关键词： Packet loss

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Machine Learning for Air Quality Prediction: Random Forest Classifier 4

Machine Learning for Air Quality Prediction: Random Forest C...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Vaishnavi, Kasula Sreya, Gummalla Reddy, Kishor Kumar Anisha, P.R. Stanley College of Engineering and Technology for Women Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798350343670

Air Pollution is the contamination of air due to the presence of substances in the atmosphere that are major issue to human life as well as to the other living organisms. Air quality is the result of the composite interactions of many elements, including the chemical reactions, meteorological parameters, and emissions from natural & anthropogenic (man-kind). The study implies that the forecasting performance differs across diverse regions & cities in India. Utilization of the Random Forest algorithm to anticipate the air quality index bucket is done in multiple locations across India annually, depending on air pollutants like PM2.5, PM10, NOx, CO, SO2, O3, NH3, and NO2 © 2024 IEEE.

关键词： Air Pollutants Air Quality Index Machine Learning Random Forest Statistics

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Advancing Industrial Effluent Monitoring Systems for Environmental Protection and Sustainable Development 11

Advancing Industrial Effluent Monitoring Systems for Environ...

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Mitra, Milan Kumar, Akash Yadav, Lokesh Kaur, Mandeep School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350350357

Industrial Effluent Monitoring Systems (IEMS) serve as vital tools for upholding environmental regulations and safeguarding public health and ecosystems. These systems are designed to assess the quality of wastewater discharged from industrial facilities, enabling monitoring of various parameters including pH, temperature, conductivity, Total Dissolved Solids (TDS), Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), nutrients, heavy metals, and toxic organic compounds. The choice of parameters to monitor depends on the specific industry and the regulations that pertain to it. In recent years, there has been a notable shift towards automated IEMS, driven by their increased efficiency and accuracy. Automated systems offer the significant advantage of real-time data collection, which facilitates the early identification and prompt resolution of environmental issues, reducing the risk of non-compliance and pollution incidents. This paper presents a comprehensive overview of the latest advancements in IEMS technology and underscores their role in enhancing environmental protection. It also references related research, contributing to a broader understanding of the field. By embracing these innovative monitoring systems and staying abreast of technological developments, industries can not only ensure compliance but also minimize their ecological footprint, fostering a more sustainable and responsible approach to wastewater management. © 2024 IEEE.

关键词： Environmental regulations

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Robust Technique for Detecting and Blocking of VPN over Networks 9

Robust Technique for Detecting and Blocking of VPN over Netw...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Vijaya, G. Dhiyanesh, M.S. Hashmi, M.D. Junaith Rehaan, K. Kavennesh, B.V. Sri Krishna College of Engineering And Technology Computer Science And Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350365092

In computer networks, data and information may be exchanged between linked devices through network communication. Networks are the backbone of the digital world, allowing data to be sent between computers, servers, and other devices. Standards and protocols like HTTP (Hypertext Transfer Protocol), which provide the guidelines for data interchange control this communication. The effectiveness and dependability of network communication, whether via wired or wireless connections, directly influences file sharing, real-time collaboration, and internet surfing. Organizations heavily rely on firewalls to safeguard internal networks from external threats. These firewalls enforce access rules, filter content, and scan for intrusions, providing robust protection. Virtual Private Networks (VPN) mask their real traffic under SSL (Secure Sockets Layer)/TLS (Transport Layer Security). Detecting such VPN services requires the use of firewalls that can analyze SSL/TLS actively. These features aren't cost-efficient and may not be suitable for every organization. VPN services may be used by users to bypass organizational compliance for accessing geo-restricted platforms, personal browsing, and connecting to personal network infrastructures that may be deemed off-limits within an organization. This usage poses a significant risk. The anonymity VPN services offer may influence users to engage in restricted content, and visit malicious sites, thereby posing a threat of inviting malware. Furthermore, these actions may expose the organization to legal consequences. Organizations may want to block the usage of VPN services to avoid sensitive information being exfiltrated, server compromization, and network jeopardization. In this paper, we have proposed an effective approach to detect VPN activity and block the user from accessing VPN services. Packet Sniffing is a process through which source IP addresses and destination IP addresses can be determined, and the source IP address and the d

关键词： HTTP

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Patterns of Widespread Structural Abnormalities in Autism Spectrum Disorder using Diffusion Tensor Imaging

Patterns of Widespread Structural Abnormalities in Autism Sp...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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Arduino based fire detection alarm in rural areas

Arduino based fire detection alarm in rural areas

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Bhattarai, Omkar Dubey, Abhay Aditya Singh, Shashank Singh, Avjeet Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9781032669663

Fire is the major disaster worldwide, and even worst condition at the village. Hence, the fire detection system or alarm should accurately locate the fire in the shortest amount of time to reduce financial loss and environmental damage. Fire is a deadly element as well as a highly useful instrument in daily life, and it regularly causes catastrophes that are more expensive than money. Fire incidents typically result in the greatest amount of damage in the shortest amount of time since they happen quickly and leave no other salvageable parts behind. Early fire detection and prevention techniques have been developed to counteract this. In this paper, a tool for early fire detection and message sender response have both been created. The instrument is designed to find heat, gas, and flame possible fire indicators. The device is made up of an Arduino module, a GSMmodule for text messaging, sensors, a buzzer, and LEDs. It also has an acrylic circuit cover. Additionally, the gadget has been successful in text messaging the given cellphone number to notify the intended receiver of the events it has identified, such as excessive temperatures and gas leaks. It can show the faster response towards the fire and asks the alarm to respond quickly so that everyone gets informed. © 2024 The Author(s).

关键词： Fires

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FeBT: A Feature Balancing Transformer for Corporate ESG Forecasting

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4063-4074页

作者： Li, Yawen Zhuang, Mengyu Ye, Guanhua Li, Yan Wang, Junheng Zhou, Jinyi Zhang, Pengfei Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Beijing University of Posts and Telecommunications School of Computer Science National Pilot School of Software Engineering Beijing China University of Electronic Science and Technology of China School of Management and Economics Chengdu China University of Science and Technology Beijing School of Economics and Management Beijing China The University of Queensland School of Electrical Engineering and Computer Science Brisbane Australia

Environmental, social, and governance (ESG) serves as a crucial indicator for evaluating firms in terms of sustainable development. However, the existing ESG evaluation systems suffer from limitations, such as narrow coverage, subjective bias, and lack of timeliness. Therefore, there is a pressing need to leverage machine learning methods to predict the ESG performance of firms using their publicly available data. Traditional machine learning models encounter the feature imbalance problem due to the heterogeneity in ESG-related features. Common approaches typically involve unfolding all features, thereby granting highdimensional folding features greater exposure and accessibility to downstream models, which results in the neglect of lowdimensional features. To fill the research gap regarding fully using the heterogeneous features of enterprises to enhance AIbased ESG prediction performance, we propose the Feature Balancing Transformer (FeBT), a model based on autoencoders and Transformer blocks. FeBT incorporates a novel feature balancing technique that compresses and enhances high-dimensional features from imbalanced data into low-dimensional representations, thereby ensuring a more balanced impact of high-dimensional and low-dimensional features on the model's performance in the downstream ESG forecasting module. Extensive experiments verified the superior performance of FeBT compared with stateof-the-art methods in real-world ESG-related datasets and evidenced that our feature balancing module provides significant insights from high-dimensional folding features. © 1989-2012 IEEE.

关键词： Predictive Models Transformers Data Models Forecasting Accuracy Biological System Modeling Training Time Series Analysis Support Vector Machines Vectors ESG Forecasting Data Masking Time Series Forecasting Machine Learning Models Autoencoder High Dimensional Feature Transformer Block Time Series Support Vector Machine Time Series Data Linear Discriminant Analysis Air Quality Long Short Term Memory Recurrent Neural Network Attention Mechanism Final Prediction Transformer Model Word Embedding XG Boost Gated Recurrent Unit Linear Probe Air Quality Index Positional Encoding Multi Head Attention Mechanism Light GBM Traditional Transformation Compressed Representation Time Series Prediction Attention Heads Prediction Model K Nearest Neighbor Imbalance In Levels Latent Representation

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Automated garden-insect recognition using improved lightweight convolution network

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Information Processing in Agriculture 2023年第2期10卷 256-266页

作者： Zhankui Yang Xinting Yang Ming Li Wenyong Li College of Computer Science and Technology Beijing University of TechnologyBeijing 100124China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China National Engineering Laboratory for Quality and Safety Traceability Technology and Application of Agricultural Products Beijing 100089China

Automated recognition of insect category,which currently is performed mainly by agriculture experts,is a challenging problem that has received increasing attention in recent *** goal of the present research is to develop an intelligent mobile-terminal recognition system based on deep neural networks to recognize garden insects in a device that can be conveniently deployed in mobile ***-of-the-art lightweight convolutional neural networks(such as SqueezeNet and ShuffleNet)have the same accuracy as classical convolutional neural networks such as AlexNet but fewer parameters,thereby not only requiring communication across servers during distributed training but also being more feasible to deploy on mobile terminals and other hardware with limited *** this research,we connect with the rich details of the low-level network features and the rich semantic information of the high-level network features to construct more rich semantic information feature maps which can effectively improve SqueezeNet model with a small computational *** addition,we developed an off-line insect recognition software that can be deployed on the mobile terminal to solve no network and the timedelay problems in the *** demonstrate that the proposed method is promising for recognition while remaining within a limited computational budget and delivers a much higher recognition accuracy of 91.64%with less training time relative to other classical convolutional neural *** have also verified the results that the improved SqueezeNet model has a 2.3%higher than of the original model in the open insect data IP102.

关键词： Insect classification Mobile-terminal recognition SqueezeNet model Deep lightweight convolution network

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Enhancing Waste Categorization Using Ensemble of Transfer Learning and Light-Weight Convolutional Neural Network

Enhancing Waste Categorization Using Ensemble of Transfer Le...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Srizon, Azmain Yakin Sarker, Ananya Mamun, Md. Al Faruk, Md. Farukuzzaman Hasan, S. M. Mahedy Rajshahi University of Engineering & Technology Department of Computer Science & Engineering Natore Bangladesh Bangladesh Army University of Engineering & Technology Department of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350316001

Proper waste management is essential to reduce the negative impacts of waste on human health and the environment. Waste management can play a significant role in achieving several Sustainable Development Goals (SDGs), including 'Sustainable Cities and Communities', 'Responsible Consumption and Production', 'Climate Action', and 'Life Below Water'. Automated waste sorting using machine learning improves waste management in smart cities by increasing efficiency and accuracy, leading to reduced disposal costs and increased recycling rates. However, limited research has been conducted on automatic waste categorization. In this study, a proposed ensembled architecture for waste type recognition using transfer learning techniques and a custom light-weight convolutional neural network (CNN) achieved an overall accuracy of 95.54% on the Flickr Material Database, outperforming previous approaches. The proposed light-weight CNN architecture utilized filters of different sizes to extract deeper and more complex features, while the ensemble technique of features from different models specialized in identifying different aspects of the input data, resulting in improved classification accuracy. The proposed approach exhibited only nine misclassifications, indicating its proficiency in accurately distinguishing between the different waste classes with comparable levels of accuracy, precision, recall, and f1-score. © 2023 IEEE.

关键词： Convolutional neural networks

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