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Optimizing Urban Waste Collection Routes: Comparative Study of Shortest Path Algorithms

Optimizing Urban Waste Collection Routes: Comparative Study ...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Begum, Chimakurthy Mounika Reddy, Kamalapuram Vigneswara Neeraja Bhaskaran, Sreebha Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

Collection of waste is one of the important goals of Waste Management Unit (WMU), where collecting waste decreases the amount of time, expenses, and the impact of waste collectors on the environment. The work is to create a set of routes for waste collection that will take minimal time to get through several collection centers given the capabilities of the collection vehicles, and road systems and traffic conditions. For waste collection points, ten major stations in Bengaluru were selected along with the disposal point. For this purpose, several algorithms such as Dijkstra's Algorithm, Bellman-Ford Algorithm, Floyd-Warshall Algorithm, Johnson's Algorithm, Nearest Neighbour Algorithm, and Ant Colony Optimization (ACO) were designed and evaluated. Algorithms were measured based on parameters such as the time taken, the fuel consumed, the CO2 produced, the effectiveness of the trip that was made. The theoretical and applied objectives of the project correlate with the goals of the integration of improved optimization possibilities with operational issues with the purpose of defining such effective methods in relation to the containment of fuel consumption, the decrease in emissions and the waste management optimization in the urban environment. © 2025 IEEE.

关键词： Wastewater disposal

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Feature Selection-driven Bias Deduction in Histopathology Images: Tackling Site-Specific Influences 13

Feature Selection-driven Bias Deduction in Histopathology Im...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Kheiri, Farnaz Bidgoli, Azam Asilian Makrehchi, Masoud Rahnamayan, Shahryar Ontario Tech University Dept. of Electrical Computer and Software Engineering Oshawa Canada Wilfrid Laurier University Dept. of Computer Science Waterloo Canada Brock University Dept. of Engineering St. Catharines Canada

ISBN: (纸本)9798350308365

The emergence of bias in deep neural models represents a significant reliability concern, which may lead to overoptimistic results on seen data while compromising the model's ability to generalize effectively on unseen datasets. Recent studies conducted on The Cancer Genome Atlas (TCGA), which is a publicly available repository of histopathology images, reveal that the TCGA cancerous features extracted by deep neural networks surprisingly are able to discriminate slides based on their origin sites. This finding undoubtedly indicates the existence of site-specific patterns embedded in the extracted features learned by deep networks rather than focusing on histomorphologic patterns. Consequently, this biased behavior raises concerns about the reliability of these networks. This observation motivates us to conduct a series of experiments in which we present two distinct evolutionary feature selection strategies, each differentiated by its objective function evaluation. The primary goal is to select the features with a minimized foot-print of data source signatures, thereby ensuring a more accurate and site-independent cancer classification. We have conducted nine comprehensive independent experiments across nine cancer types, employing each evolutionary strategy. The comparison between results obtained through evolutionary strategies and the original feature sets highlights the substantial impact of feature selection methods on bias reduction while maintaining accuracy in cancer-type discrimination. Furthermore, the comparison of the two strategies with each other demonstrates the intricate nature of bias and its integration with cancerous features during the training process. © 2024 IEEE.

关键词： Lung cancer

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Artificial Intelligence to Enhance the Security of Internet of Things: A Comprehensive Review 3

Artificial Intelligence to Enhance the Security of Internet ...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Dheekshaa, V.M. Harsha, V. Sindhu, M. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai600069 India

ISBN: (纸本)9798331527549

The Internet of Things is the term we call when devices are connected to the network and work together to provide a better experience for users, facilitate better decision-making, and improve operations. As with any invention, this one also has its disadvantages. IoT devices have complex structures and low processing capacities. There are serious security risks in designing IoT like poor authentication, data leakage, and DDoS attacks. In this review paper, how AI can secure IoT is discussed. A few AI-driven strategies that help mask all these weaknesses are also discussed regarding intrusion detection systems (IDS), behavioural analytics, anomaly detection, and real-time threat monitoring. It presents a four-layered IoT architecture consisting of perception, network, support, and application layers. The use of the IoT systems will become more flexible and efficient in data security and collection, secure and encrypted communication, and predictive security analytics. AI will play the same role in improving the threat perception, real-time identification, and response, as this will make it indispensable to ensure the IoT environment security. This work emphasizes the urgent need for solid AI-powered, multilayered adaptive security frameworks to match the evolving demands of cybersecurity. © 2025 IEEE.

关键词： Network intrusion

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Fish Detection using Dual Branch Image Enhanced Pipeline with YOLOv8 3

Fish Detection using Dual Branch Image Enhanced Pipeline wit...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Kumar, Vivek Kumaran, P. Dept. of Computer Science Engineering National Institute of Technology-Puducherry Puducherry Karaikal609609 India

Fish species identification plays a crucial role in the aquaculture industry. Current methods such as using sensors, acoustic devices and density map regression, face multiple challenges related to costs, computational complexity and corrosion or malfunctioning of devices in underwater environment. To address these limitations we use YOLOv8(You Only Look Once) for the identification of fish species using visual perspective through the camera. The model is trained using a dataset of fish aquarium images to provide ground truth labels for training and evaluation purposes. Two species were observed in this research: Iridescent Shark Catfish (Pangasianodon hypophthalmus) and Goldfish (Carassius auratus). Our study achieved a notable breakthrough in identifying all the two species within a comprehensive dataset comprising 173 images. The top-performing model demonstrated exceptional accuracy on the test dataset with splitting ratios of 70-20-10 for training, validation, and testing, respectively. The custom model attained a mAP which is mean Average Precision, of 92% at an IoU threshold of 0.5, underscoring its impressive capabilities to detect across all classes. To remove the turbidity, a dual branch image enhancement pipeline is used with yolov8, which gives the best confidence score of 66% in detecting the fish species. © 2025 The Author(s).

关键词： Fisheries

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

引用

16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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A Noble Approach to the Object Detection in Defense system in Terms of Image Processing 3

A Noble Approach to the Object Detection in Defense System i...

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3rd International Conference on Disruptive Technologies, ICDT 2025

作者： Priyadarsan, Soumendra Sethy, Soorjyakanta Xalxo, Basant Behera, Sasmita Veer Surendra Sai University of Technology Dept. of Computer Science and Engineering Odisha Burla India

ISBN: (纸本)9798331519582

Object identification is very important for modern defense systems because it helps ensure security and extends support in various operations. Realistic images come with issues of low contrast, motion blur, and noise, which ultimately affect detection performance. This research seeks to come up with ways of enhancing a procedure that enhances image quality and consequently detection. The method used here applies Histogram Equalization and Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve the contrast. Then, Gaussian Blur & Median Blur are used to mimic and reduce the motion blur effect and eliminate noise respectively. Finally, sharpening is done to highlight edges. As a result, the image quality has significantly improved. Sharper edges, improved contrast and less noise will help in better AI detection. In the future, we plan to add machine learning models to support real-time detection which will make the system adaptable to complex environments. © 2025 IEEE.

关键词： Motion analysis

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Network Intrusion Detection by Integrating BI-LSTM for Enhanced Security 3

Network Intrusion Detection by Integrating BI-LSTM for Enhan...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Lakshmi Priya, P. Lohith, Kandibanda Johnson, Joel M. Jyotsna, C. Aiswariya Milan, K. Bengaluru Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331523893

The rising interconnectivity of digital systems has brought about the challenges of network security as high risks of data theft and unauthorized access through intrusions in networks. This paper details the development of a Network Intrusion Detection system that uses deep learning techniques and is designed to detect normal as well as anomalous activities within the network, such as zero-Day vulnerabilities, as discussed has proven that this model was capable of obtaining excellent accuracy, up to 99.25%, with precision and recall metric being well achieved, owed to advanced techniques, in this case, hyperparameter tuning. This will mean the model's capability to clearly differentiate legitimate and malicious traffic. In addition, explainable AI techniques such as SHAP and LIME deliver insights into the contributions or importance of various features about a model's decision-making. © 2025 IEEE.

关键词： Network intrusion

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework 3

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Vvs, Sriharsha Nair, Pranav B. Akarsh, S.V. Narmada, Nakka Pati, Peeta Basa Amrita School of Computing Dept. of Computer Science & Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331508456

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications. © 2025 IEEE.

关键词： Java programming language

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

Personal AI Trainer Using Two-Way Mirror and Raspberry Pi

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Singh, Ratan Tejnarayan Wasnik, Aarush Mistry, Krishna Modi, Jayen Koshti, Dipali Fr. Conceicao Rodrigues College of Engineering Dept. of Electronics and Computer Science Mumbai India

ISBN: (纸本)9798331512965

A novel smart mirror system is proposed to enhance users' daily routines by integrating advanced technologies. Utilizing a Raspberry Pi, an electronic display, a two-way mirror, a camera, and control mechanisms powered by OpenCV and MediaPipe, the system provides personalized interactions through facial recognition. This feature allows the smart mirror to identify users and tailor its responses accordingly. Beyond its aesthetic appeal, the system functions as an interactive fitness coach, guiding users through workouts, correcting their form, and delivering real-time feedback. This paper explores the potential of smart mirrors to revolutionize personal fitness and care, offering an innovative approach to interactive user engagement. © 2025 IEEE.

关键词： Face recognition

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