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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Innovative Horizons in Agricultural Technology with TSA Based StrawberrySqueezeNet Classification Model 2

Innovative Horizons in Agricultural Technology with TSA Base...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Vindhya, G.B. Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Tumkur Gubbi India

ISBN: (纸本)9798350373110

A fruit plant with sufficient value is the strawberry plant. High concentrations of fibre, antioxidants, potassium, vitamin C, and folic acid can be found in strawberry fruit. Because bacteria, pests, and microbes frequently impact strawberry plants, cultivating strawberries is a simple operation. Digital image processing will be used to introduce strawberry disease initially and limit its spread in strawberry plants. To improve strawberry disease diagnosis, a multi-pronged strategy was used, starting with the application of a carefully selected dataset. The dataset was then enriched and diversified using data augmentation techniques, which made it easier to build a reliable and complex model. For classification, the StrawberrySqueezeNet architecture was used, utilising its inherent capacity to identify complex patterns in the data. For hyperparameter tuning, a Tangent Search technique was used to maximise the model's performance. In order to get higher classification accuracy, the iterative optimisation procedure dynamically adjusted parameters as it explored the hyperparameter space. The result of these efforts was an astounding accuracy of 98.3%, which beat the industry standards set by other models. © 2024 IEEE.

关键词： Fruits

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PFCM Based Segmentation and TFA Based DCNN Model for Skin Cancer Classification Using Dermoscopic Images 2

PFCM Based Segmentation and TFA Based DCNN Model for Skin Ca...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Manoj, H.M. Pawar, Priyanka Pramod Krupa, R. Pareek, Piyush Kumar Kumar, Deepak Bandeppa, Lalitha Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Worldwide, skin lesions have emerged as a serious health concern;however, the survival rate can be improved by identifying skin lesions sooner with dermoscopy pictures. It will be a painstaking process to classify the skin lesion from those dermoscopy photographs. Using deep learning models improves the accuracy of skin lesion categorization. There has been a recent influx of convolutional neural networks (CNN) into this field;CNNs have well-established methods for feature extraction, which improves classification. This project aims to create AI-based solutions, namely deep learning (DL) procedures, to differentiate among benign and malignant skin lesions in dermoscopy pictures. Seven types of skin lesions were used in the Skin Cancer MNIST: HAM10000 dataset. Visual illness diagnosis using deep learning models is becoming increasingly common. For the purpose of skin cancer classification, this study suggests using a deep convolutional neural network (DCNN). In order to enhance the accuracy of the proposed DCNN classifier, the Tangent Flight Algorithm (TFA) is recommended as the optimum approach for optimising the weights, and possibilistic fuzzy clustering (PFCM) is employed for segmentation. Results from this study demonstrate that, compared to convolutional approaches, the suggested technology provides superior accuracy. The proposed TFA based DCNN model outperformed state-of-the-art prediction methods with training accuracy of 0.962, testing accuracy of 0.947, sensitivity of 0.824, and F1-score of 0.822. © 2024 IEEE.

关键词： Convolutional neural networks

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In-depth and Comparative Analysis of Deep learning and IoT Privacy, Trust, Attacks, and Algorithm and Architectural Issues

In-depth and Comparative Analysis of Deep Learning and IoT P...

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Augmented intelligence and Sustainable Systems (ICAISS), International Conference on

作者： Anuradha. P Prabakeran. S Department of Computer Science and Engineering - Artificial Intelligence and Machine Learning Easwari Engineering College Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu India

ISBN: (数字)9798331507244

ISBN: (纸本)9798331507251

This study offers a detailed analysis of the security, problems, privacy, and architectural features of the Internet of Things (IoT) and deep learning systems. IoT refers to creating a massive network of interconnected physical objects, ranging from everyday household items like smart refrigerators and thermostats to sophisticated industrial machinery. The devices are made to communicate, collect, and share data to make it more efficient, convenient, and easier for decision-making. Deep learning is a subset of machine learning and artificial intelligence (AI), which models the human brain using neural networks to understand and represent complex patterns in huge datasets. The research starts by detailing the security concerns inherent in these technologies, outlining vulnerabilities, common threats, and what is required for safeguarding measures. It has privacy issues in close examination on how data is collected, stored, and managed to protect user information and maintain trust. The study then addresses the architectural challenges of IoT and deep learning systems, which are quite complex in building scalable, efficient, and robust infrastructures, including network design, data integration, and computational power requirements. The paper discusses and concludes with a comparative analysis, with shared characteristics and some distinctions between IoT and deep learning. From this comes an all- round understanding of their convergence and divergence, and information to inform subsequent research and development work. For this purpose, the study brings out a precise investigation in relation to this argument.

关键词： Deep learning Privacy Buildings Data integration Internet of Things Security Biological neural networks Thermostats Research and development Convergence

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SINN Based Federated learning Model for Intrusion Detection with Blockchain Technology in Digital Forensic 2

SINN Based Federated Learning Model for Intrusion Detection ...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Pramod Pawar, Priyanka Kumar, Deepak Krupa, R. Pareek, Piyush Kumar Manoj, H.M. Deepika, K.S. University of the Cumberlands Information Technology Cumberlands United States Nitte Meenakshi Institute of Technology Mechanical Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India Bms Institute of Technology and Management Computer Science and Engineering Bengaluru India Channabasaveshwara Institute of Technology Gubbi India

ISBN: (纸本)9798350373110

Today, smart devices permeate every part of our lives, ushering in a new technological era known as Internet of Things (IoT). There ration of cybersecurity risks due to the proliferation of smart devices in IoT contexts and the massive volumes of sensitive data they store. In order to determine the time and place of these attacks and to collect evidence that can lead to the identification of the perpetrators, digital forensics is essential. Research into digital forensics in an IoT setting is fraught with difficulties owing to factors such as the dispersed nature of data storage, the need to be able to track and verify the provenance of evidence, the difficulty in obtaining data from many sources, and the lack of visibility into the evidence collection procedure. This is why the research suggested merging two potential technologies to offer a complete answer. The research employed federated learning to train representations locally using data kept by IoT devices, with the help of a dataset that was created to mimic attacks on the IoT ecosystem. The study utilises the BoT-IoT dataset and employs a classification method called Shepard Interpolation Neural Network (SINN) with the Shuffled Frog-Leaping algorithm (SFLA). After that, in order to make the blockchain lightweight, the study aggregated data using blockchain by gathering parameters from the IoT gateway © 2024 IEEE.

关键词： Cybersecurity

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A Hybrid Remora Optimization Algorithm with Variable Neighborhood Search for the Flexible Job Shop Scheduling Problem 7

A Hybrid Remora Optimization Algorithm with Variable Neighbo...

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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Yang, Zheng Liang, Xiaodan Li, Yajing Wang, Haoyue School Of Computer Science And Technology Tiangong University Tianjin China Engineering Research Center Of Integration And Application Of Digital Learning Technology Ministry Of Education The Open University Of China Beijing China College Of Artificial Intelligence Tianjin University Of Science And Technology Beijing China

ISBN: (纸本)9798350361445

Reducing the production makespan of modern small-batch, multi-variety jobs can be abstracted into a flexible job shop scheduling problem (FJSP). The organization can greatly improve production efficiency by scheduling according to the optimal solution of FJSP. However, due to the high computational complexity of FJSP, it is often difficult to achieve higher performance in actual production. To optimize the makespan of the FJSP, a hybrid remora optimization algorithm with variable neighborhood search (HROA-VNS) is proposed in this work. A hybrid initialization method based on machine load balancing is utilized to improve the quality of the initial population. To solve discrete FJSP, a conversion method for individual positions and scheduling solutions is proposed, and a host switching mechanism (HSM) is designed to enhance the development and exploration capabilities of the algorithm. Furthermore, for enhancing the accuracy and effectiveness of the optimal solution, a variable neighborhood search (VNS) strategy which contains multiple structures is designed for local search. According to the experimental data, compared with the basic algorithm, the HROA-VNS reduced the makespan by an average of 33.02% and 14.40% on the two benchmark test sets respectively, and it shows better performance than related algorithms. © 2024 IEEE.

关键词： Evolutionary algorithms

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New Approach of artificial intelligence in Digital Forensic Investigation: A Literature Review 1

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International Conference on Advances in Communication Technology and computer Engineering, ICACTCE 2023

作者： Verma, Raghav Garg, Sonia Kumar, Karan Gupta, Gaurav Salehi, Waleed Pareek, Piyush Kumar Kniežova, Jaroslava Yogananda School of Artificial Intelligence Computers and Data Science Himachal Pradesh Solan173229 India Mullana Ambala133207 India Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India Faculty of Management Comenius University in Bratislava 25 Odbojárov 10 Bratislava82005 Slovakia

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

A Digital Forensic is a subfield of forensic science known as "digital forensic science" that focuses on the recovery and examination of data from digital devices that are connected to cybercrime. computer forensics was the original meaning of the word "digital forensics." it has broadened to include any device that could store digital data. National policies on digital forensics didn't start to arise until the beginning of the twenty-first century. The process of locating, protecting, analyzing, and documenting digital evidence is known as digital forensics. When necessary, this is done so that evidence can be presented in court. AI in Digital forensics is one of the technologies that is developing the fastest, and it has had a significant impact on the methods and tools used to examine, monitor, and visualize crime scenes as well as develop effective strategies for dealing with impending threats and attacks on the internet and cyberspace. Modern technology has replaced human labor with machine-oriented designed work that has the capability and ability to minimize error and increase quality, reducing human effort and achieving maximum results with the fewest possible errors. It can influence the outcome and analyze the evidence in the field of digital forensics in a more effective manner to track the outcomes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： artificial intelligence

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Life Expectancy Prediction using machine learning Technique – KNN

Life Expectancy Prediction using Machine Learning Technique ...

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Applied intelligence and Computing (AIC), IEEE World Conference on

作者： Preeti Narooka Aarushi Vashistha Priyanka Mitra Gulisha Derashri Department of Artificial Intelligence and Machine Learning School of Computer Science Engineering Manipal University Jaipur Rajasthan India Department of Computer Science and Engineering Jaipur Engineering College and Research Centre Jaipur India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In the modern world, life expectancy turns out to be the most valuable factor in determining population’s health. Today, it is more crucial to predict the future because life is more precarious due to a variety of circumstances, including social, economic, environmental, educational, lifestyle, and health conditions. The machine learning algorithm KNN for life expectancy is presented in this work. The dataset gathered for this study is separated into a training set (70%) and a test set (30%), and the forecasts precision is examined. To avoid misclassification, KNN needs a larger sample size, and the best K are chosen more in accordance with cross-validation experience than with strong statistical theory.

关键词： Training Support vector machines Logistic regression machine learning algorithms Pandemics Biological system modeling Medical services Nearest neighbor methods Socioeconomics Random forests

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An Efficient Biomedical Solicitation in Liver Cancer Classification by Deep learning Approach

An Efficient Biomedical Solicitation in Liver Cancer Classif...

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2023 IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Vasundhara, N. Nandan, Archana S Hemanth, S.V. MacHerla, Sivudu Madhura, G.K. Cmr Technical Campus Department of Cse Hyderabad India Gitam School of Technology Gitam University Department of Computer Science & Engineering India Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana India Pace Institute of Technology & Science Andhra Pradesh India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798350398465

The liver, a significant physical organ, is located in the upper right and left abdominal cavities just below the diaphragm. It produces several different chemicals that the body needs to function properly. Researchers can benefit from converting images into data to more easily exchange and generate precise results. Since the process of conversion relies on technology and an algorithm, it eliminates the possibility of human error. Liver cancer has the highest fatality rate of any cancer since its symptoms don't present until late in the disease's progression, making early detection difficult. Skimming, sifting, segmenting, feature abstraction, and presentation via ANN (artificial Neural Network) are the primary topics of the first stage of the debate. Real-time data sets use Feed-Forward Neural Network (FFNN) for identifying liver cancer and classifying its severity. Filtering has two primary applications: noise suppression and edge rounding. Then, segmentation is employed to isolate the relevant area, allowing for more compact data storage. The Gray Level and Co-occurrence Matrix (GLCM) is used to extract features, and the resulting matrix can have many different forms. This criterion helps classify tumors as benign or malignant. Metrics such as accuracy, sensitivity, positive and negative predictive values, and precision are used to assess the rate. The experimental method for identifying liver tumors uses CT liver pictures to achieve an average accuracy of 99.45% for malignant images. © 2023 IEEE.

关键词： computerized tomography

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MECASA: Motor Execution Classification using Additive Self-Attention for Hybrid EEG-fNIRS Data

arXiv

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arXiv 2025年

作者： Siddhad, Gourav Singh, Juhi Roy, Partha Pratim Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India Department of Artificial Intelligence & Machine Learning Manipal University Rajasthan Jaipur303007 India

Motor execution, a fundamental aspect of human behavior, has been extensively studied using BCI technologies. EEG and fNIRS have been utilized to provide valuable insights, but their individual limitations have hindered performance. This study investigates the effectiveness of fusing electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) data for classifying rest versus task states in a motor execution paradigm. Using the SMR Hybrid BCI dataset, this work compares unimodal (EEG and fNIRS) classifiers with a multimodal fusion approach. It proposes Motor Execution using Convolutional Additive Self-Attention Mechanisms (MECASA), a novel architecture leveraging convolutional operations and self-attention to capture complex patterns in multimodal data. MECASA, built upon the CAS-ViT architecture, employs a computationally efficient, convolutional-based self-attention module (CASA), a hybrid block design, and a dedicated fusion network to combine features from separate EEG and fNIRS processing streams. Experimental results demonstrate that MECASA consistently outperforms established methods across all modalities (EEG, fNIRS, and fused), with fusion consistently improving accuracy compared to single-modality approaches. fNIRS generally achieved higher accuracy than EEG alone. Ablation studies revealed optimal configurations for MECASA, with embedding dimensions of 64-128 providing the best performance for EEG data and OD128 (upsampled optical density) yielding superior results for fNIRS data. This work highlights the potential of deep learning, specifically MECASA, to enhance EEG-fNIRS fusion for BCI applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Near infrared spectroscopy

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