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An Intrusion Detection System on The Internet of Things Using Deep Learning and Multi-objective Enhanced Gorilla Troops Optimizer

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Journal of Bionic engineering 2024年第5期21卷 2658-2684页

作者： Hossein Asgharzadeh Ali Ghaffari Mohammad Masdari Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia63896-57169Iran Department of Computer Engineering Technical and Vocational University(TUV)TehranIran Department of Computer Engineering Tabriz BranchIslamic Azad UniversityTabriz***Iran Department of Computer Engineering Faculty of Engineering and Natural ScienceIstinye University34010IstanbulTurkey Department of Computer Engineering Khazar UniversityAZ1096BakuAzerbaijan

In recent years,developed Intrusion Detection Systems(IDSs)perform a vital function in improving security and anomaly *** effectiveness of deep learning-based methods has been proven in extracting better features and more accurate classification than other *** this paper,a feature extraction with convolutional neural network on Internet of Things(IoT)called FECNNIoT is designed and implemented to better detect anomalies on the ***,a binary multi-objective enhance of the Gorilla troops optimizer called BMEGTO is developed for effective feature ***,the combination of FECNNIoT and BMEGTO and KNN algorithm-based classification technique has led to the presentation of a hybrid method called *** the next step,the proposed model is implemented on two benchmark data sets,NSL-KDD and TON-IoT and tested regarding the accuracy,precision,recall,and Fl-score *** proposed CNN-BMEGTO-KNN model has reached 99.99%and 99.86%accuracy on TON-IoT and NSL-KDD datasets,*** addition,the proposed BMEGTO method can identify about 27%and 25%of the effective features of the NSL-KDD and TON-IoT datasets,respectively.

关键词： Intrusion detection Internet of Things-Convolutional neural network Multi-objective Gorilla troops optimizer

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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Dual image based secured reversible data hiding scheme exploiting huffman compression tree combining bit-reversal permutation technique

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Multimedia Tools and applications 2025年第9期84卷 5653-5698页

作者： Datta, Kankana Jana, Biswapati Chakraborty, Mamata Dalui Department of Computer Applications Haldia Institute of Technology HIT Campus Hatiberia Haldia Purba Medinipur West Bengal Haldia India Department of Computer Science Vidyasagar University West Bengal Midnapore India Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India

In the area of data hiding and information security, the greater need is to ensure high embedding capacity of the stego media without hampering the visual quality while ensuring the tightest possible security. Now, it has become one of the biggest challenge to meet all these requirements. Embedding secret messages in dual images is one of the latest techniques that aims to cater such needs. However, there are very few data hiding schemes that are currently using dual images, but are suffering from several shortcomings, such as security loophole and poor visual quality with limited hiding capacity. In this paper, a dual image-based reversible data hiding scheme has been proposed where multiple bits can be embedded within a pair of pixels dynamically by Huffman compression where different pseudo codes are generated for different chunk of secret bits which are embedded by Bit-Reversal Permutation (BRP) and X-OR operations. Finally, pixels are interchanged between the dual images based on odd-even properties of the pixel values. The functional efficiency of the proposed scheme is evaluated by comparing the relevant parameters of different state-of-the-art schemes. It is established that the proposed scheme provides more than 64 dB PSNR value on average after embedding a maximum of 3, 27, 680 bits of secret data which ensures good visual quality and high embedding capacity as compared to other state-of-the-art schemes thereby benefitting innumerable applications across public and private sectors. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Pixels

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A new bi-level deep human action representation structure based on the sequence of sub-actions

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Neural Computing and applications 2025年第2期37卷 985-1008页

作者： Akhlaghian Tab, Fardin Ramezani, Mohsen Afshoon, Hadi Seyedi, Seyed Amjad Moradyani, Atefeh Department of Computer Engineering Faculty of Engineering University of Kurdistan Sanandaj Iran Department of Computer Science University of Kurdistan Sanandaj Iran

Human action recognition is applicable in different domains. Previously proposed methods cannot appropriately consider the sequence of sub-actions. Herein, we propose a semantical action model based on the sequence of sub-actions. A technique is used to segment actions on the time axis based on body movements via an energy diagram. After dividing actions into sub-actions, a novel bi-level deep structure is used to extract their features. Then, the sequence of sub-action features is modeled by a deep network to create the action model. As extracted sub-actions have fewer variations in execution manner, their representation is more stable, and modeling their sequence would be an efficient model. Experimental results on UCF-YouTube, UCF-Sport, and Human Motion DataBase (HMDB) datasets indicate the sustainable performance of this method. Overall, the accuracy of the proposed method is 0.972 on average, while the value for the second-best method is 0.925. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Human form models

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Application of human activity/action recognition: a review

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Multimedia Tools and applications 2025年 1-30页

作者： sedaghati, Nazanin ardebili, Sondos Ghaffari, Ali Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey Department of Computer Science Khazar University Baku Azerbaijan

Human activity recognition is a crucial domain in computer science and artificial intelligence that involves the Detection, Classification, and Prediction of human activities using sensor data such as accelerometers, gyroscopes, etc. This field utilizes time-series signals from sensors present in smartphones and wearable devices to extract human activities. Various types of sensors, including inertial HAR sensors, physiological sensors, location sensors, cameras, and temporal sensors, are employed in diverse environments within this domain. It finds valuable applications in various areas such as smart homes, elderly care, the Internet of Things (IoT), personal care, social sciences, rehabilitation engineering, fitness, and more. With the advancement of computational power, deep learning algorithms have been recognized as effective and efficient methods for detecting and solving well-established HAR issues. In this research, a review of various deep learning algorithms is presented with a focus on distinguishing between two key aspects: activity and action. Action refers to specific, short-term movements and behaviors, while activity refers to a set of related, continuous affairs over time. The reviewed articles are categorized based on the type of algorithms and applications, specifically sensor-based and vision-based. The total number of reviewed articles in this research is 80 sources, categorized into 42 references. By offering a detailed classification of relevant articles, this comprehensive review delves into the analysis and scrutiny of the scientific community in the HAR domain using deep learning algorithms. It serves as a valuable guide for researchers and enthusiasts to gain a better understanding of the advancements and challenges within this field. © The Author(s) 2025.

关键词： Human Action Detection Human Activity Recognition (HAR) Human–computer Interaction (HCI) Sensors Smartphones

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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MBASE: Meta-heuristic Based optimized location allocation algorithm for baSE station in IoT assist wireless sensor networks

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Multimedia Tools and applications 2024年第18期83卷 53383-53415页

作者： Chaurasia, Soni Kumar, Kamal Department of Computer Science and Engineering National Institute of Technology Uttarakhand India Department of Computer Science and Engineering SGT University Gurgaon India

In an Internet of Things (IoT) assisted Wireless Sensor Network (WSN), the location of the Base Station (BS) remains important. BS serves as the central hub for data collection, aggregation and communication within the network. Its location significantly impacts the overall network performance and energy efficiency. However, the role and requirements of the BS may vary depending on the specific implementation and integration of IoT assist opportunistic routing protocol. Therefore, this work propose a Meta-heuristic Based optimized location Allocation algorithm for baSE station in IoT assist WSNs (MBASE). The MBASE algorithm is based on Forwarder Node (FN) selection and using meta-heuristic based opportunistic routing. The FN is selected by a decision-making approach based on meta-heuristic search strategies which offers viable options for local and global optimization problems. This work aims to find a way to achieve a trade-off in this algorithm for efficient and appropriate placement of BS either at the centre or outside of the network deployment zone. Both positions of BS are optimization problems in the design of WSNs/IoTs. MBASE algorithm is proposed for identifying the optimized location allocation for BS and is based on metaheuristic. Simulation based analysis of essential operational and functional parameters such as Message Success Rate (MSR), Packet Delivery Ratio (PDR), Throughput, and Average energy consumption (AEC) is reported in the paper. Additionally, work compares BS locations using a common neighbourhood concept to assure fairness. The evaluation shows a significant improvements in the PDR, MSR, Throughput and AEC for two different BS locations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Base stations

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ILAPU-Q: An Improved Lightweight Authentication Protocol for IoT Based on U-quark Hash Function

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Recent Advances in computer science and Communications 2024年第2期17卷 78-87页

作者： Jebrane, Jihane Lazaar, Saiida Mathematics and Computer Science Department Mathematics Computer Science and Applications ERMIA-Team National School of Applied Science-ENSA of Tangier AbdelMalek Essaadi University Tetouan Morocco

Background: In the last decades, the development of Internet activities has been significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other with-out human interaction. Due to this revolution, many applications have been adopted in the are-na of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to promote and facilitate people's standards of living around the world. However, these unbound-ed applications bring more challenges to the storage capabilities of devices, and their security and privacy preservation. Moreover, security issues suffer from weak authentication protocols. Methods: To address these issues, suitable and secure lightweight mutual authentication schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual authentication scheme based on ECC, coupled with a relevant seminal work considered as a reference in the field. This scheme is combined with U-quark, a lightweight hash function, to guarantee the security needed in the IoT environment. Results: We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that our version can ensure the most favorable performance during the authentication process. Conclusion: Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the same computational environment. © 2024, Bentham science Publishers. All rights reserved.

关键词： Hash functions

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Local Search-Based Anytime Algorithms for Continuous Distributed Constraint Optimization Problems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 288-290页

作者： Xin Liao Khoi Hoang Xin Luo the College of Computer and Information Science Southwest University the Department of Computer Science and Engineering Washington University in St.Louis IEEE

Dear Editor,The distributed constraint optimization problems(DCOPs) [1]-[3]provide an efficient model for solving the cooperative problems of multi-agent systems, which has been successfully applied to model the real-world problems like the distributed scheduling [4], sensor network management [5], [6], multi-robot coordination [7], and smart grid [8]. However, DCOPs were not well suited to solve the problems with continuous variables and constraint cost in functional form, such as the target tracking sensor orientation [9], the air and ground cooperative surveillance [10], and the sensor network coverage [11].

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Motion Signal-based Recognition of Human Activity from Video Stream Dataset Using Deep Learning Approach

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Recent Advances in computer science and Communications 2024年第3期17卷 77-91页

作者： Yadav, Ram Kumar Arockiam, Daniel Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Madhya Pradesh Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted outstanding research attention due to the implementation of machine learning and deep learning approaches. Aim: This paper proposes a unique deep learning framework based on motion signals to recognize human activity to handle these constraints and challenges through deep learning (e.g., enhance CNN, LR, RF, DT, KNN, and SVM) approaches. Method: This research article uses the BML (Biological Motion Library) dataset gathered from thirty volunteers with four various activities to analyze the performance metrics. It compares the evaluated results with existing results, which are found by machine learning and deep learning methods to identify human activity. Result: This framework was successfully investigated with the help of laboratory metrics with convolutional neural networks (CNN) and achieved 89.0% accuracy compared to machine learning methods. Conclusion: The novel work of this research is to increase classification accuracy with a lower error rate and faster execution. Moreover, it introduces a novel approach to human activity recognition in the BML dataset using the CNN with Adam optimizer approach. © 2024 Bentham science Publishers.

关键词： Support vector machines

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