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International Conference on Power engineering and Intelligent Systems, PEIS 2024

作者： More, Sujeet Narang, Geetika Tirth, Sneha Saudagar, Saleh Maske, Rupali Bhosale, Prasad Department of Computer Engineering Trinity College of Engineering and Research Pune India

ISBN: (纸本)9789819767137

WSN is used in a variety of critical environments to monitor certain parameters such as border monitoring systems, among others. If any illegal devices are introduced or inserted into this network, it will result in significant data loss or danger. Several studies have been conducted in this field, including false node detection, encryption, and hostile node identification. If a sensor node becomes contaminated (fake node) but continues to deliver valid data, error recovery can be achieved using the system's physical and analytical redundancy (spatial redundancy). To increase security on wireless sensor networks, several approaches are used, including conditional random field, hidden Markov models (HMMs), SDN gateways, a hybrid model that combines CNF and HMM, and blockchain technology. Currently, blockchain technology has been implemented using encryption algorithms to enhance security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Hidden Markov models

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Machine Learning based approaches for Identification and Prediction of diverse Mental Health Conditions 1

Machine Learning based approaches for Identification and Pre...

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1st IEEE International Conference on Contemporary Computing and Communications, InC4 2023

作者： Abid, Mohammad Anas Dehghan, Zahra Shinde, Tanmay Narang, Geetika Trinity College of Engineering and Research Computer Engineering Department Pune India

ISBN: (纸本)9798350335774

Today, looking after our mental health has become as essential as maintaining our physical health and well-being. It is found that a lot of people belonging to different age groups, gender and a wide range of professional backgrounds suffer from different mental disorders globally that lead to sizable losses in health and functioning of an individual. Mental health issues such as depression, anxiety and stress can originate due to various biological, psychological, and environmental factors which give rise to suicidal thoughts. These challenges have encouraged the advancement of numerous machine learning based approaches for providing an impactful solution to predict and identify the severity of various mental health issues on time, and therefore suggest potential treatment outcomes. It has been observed that Machine learning is one of the most efficient approaches that can be used to analyze enormous amounts of data for making accurate predictions in the healthcare system. This paper explores various sources for data collection (Clinical questionnaires, interviews, social media, medical history and speech alterations), automated methods and ML-based depression detection algorithms of different classes including classification, regression algorithms, deep learning, and ensemble method used by researchers for analyzing varied mental health conditions. Few studies made use of Synthetic Minority Oversampling Technique (SMOTE) to reduce the class imbalance of the training data in order to achieve better accuracy. A comparison of different parameters such as objectives, results and limitations between the referred research papers presented in the domain of depression detection has also been included in this paper. © 2023 IEEE.

关键词： Deep learning

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Bridging the gap: combining expert guidance and self-learned knowledge for efficient and robust autonomous navigation in complex settings

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Journal of Ambient Intelligence and Humanized Computing 2025年第2期16卷 425-441页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Naini Uttar Pradesh Prayagraj211010 India

Autonomous navigation in dynamic environments poses significant challenges due to complex agent–environment interactions, real-time decision-making, and adaptability requirements. We propose the Reinforcement-Imitation Learning for Autonomous Navigation (RILAN) algorithm, which integrates reinforcement learning, imitation learning, and uncertainty estimation for robust and adaptable navigation. RILAN combines the strengths of expert demonstrations and self-learned knowledge, overcoming individual limitations. The modular RILAN architecture comprises an imitation learning module, reinforcement learning module, and an uncertainty-aware policy selector, enabling efficient information processing and dynamic policy selection. Extensive experiments compare RILAN to state-of-the-art algorithms, including E2E-IL, CIL, DQN, and PPO. Results show RILAN outperforms these algorithms in success rate, average time to goal, collision rate, and off-road rate, demonstrating the proposed approach’s efficacy for robust autonomous navigation in applications like self-driving vehicles and robotics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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Taming diffusion model for exemplar-based image translation

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Computational Visual Media 2024年第6期10卷 1031-1043页

作者： Ma, Hao Yang, Jingyuan Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen China

Exemplar-based image translation involves converting semantic masks into photorealistic images that adopt the style of a given ***,most existing GAN-based translation methods fail to produce photorealistic *** this study,we propose a new diffusion model-based approach for generating high-quality images that are semantically aligned with the input mask and resemble an exemplar in *** proposed method trains a conditional denoising diffusion probabilistic model(DDPM)with a SPADE module to integrate the semantic *** then used a novel contextual loss and auxiliary color loss to guide the optimization process,resulting in images that were visually pleasing and semantically *** demonstrate that our method outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

关键词： exemplar image translation denoising diffusion probabilistic model(DDPM)

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Reinforcement imitation learning for reliable and efficient autonomous navigation in complex environments

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Neural Computing and Applications 2024年第20期36卷 11945-11961页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Uttar Pradesh Naini Prayagraj211010 India

Reinforcement learning (RL) and imitation learning (IL) are quite two useful machine learning techniques that were shown to be potential in enhancing navigation performance. Basically, both of these methods try to find a policy decision function in a reinforcement learning fashion or through imitation. In this paper, we propose a novel algorithm named Reinforcement Imitation Learning (RIL) that naturally combines RL and IL together in accelerating more reliable and efficient navigation in dynamic environments. RIL is a hybrid approach that utilizes RL for policy optimization and IL as some kind of learning from expert demonstrations with the inclusion of guidance. We present the comparison of the convergence of RIL with conventional RL and IL to provide the support for our algorithm’s performance in a dynamic environment with moving obstacles. The results of the testing indicate that the RIL algorithm has better collision avoidance and navigation efficiency than traditional methods. The proposed RIL algorithm has broad application prospects in many specific areas such as an autonomous driving, unmanned aerial vehicles, and robots. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Reinforcement learning

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Sol–gel‑templated bioactive glass scaffold: a review

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research on Biomedical engineering 2024年第1期40卷 281-296页

作者： Shoushtari, Maryam Sarmast Hoey, David Biak, Dayang Radiah Awang Abdullah, Norhafizah Kamarudin, Suryani Zainuddin, Halimatun S. Department of Chemical & amp Environmental Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Serdang43400 Malaysia Trinity Centre for Biomedical Engineering Trinity Biomedical Sciences Institute Trinity College Dublin Dublin Ireland Department of Mechanical Manufacturing and Biomedical Engineering School of Engineering Trinity College Dublin Dublin Ireland Advanced Materials and Bioengineering Research Centre Trinity College Dublin & ampRCSI Dublin Ireland Institute of Nanoscience and Nanotechnology Universiti Putra Malaysia Selangor Serdang43400 Malaysia

Purpose: This review paper explores diverse synthesis strategies within the sol–gel technique for producing silicate bioglass with a focus on tailoring these materials for bone scaffold design. Method: A comprehensive search was conducted across various databases, including ScienceDirect, Taylor & Francis, PubMed, Hindawi, Royal Society of Chemistry (RSC), Wiley Online Library, researchGate, and Google Scholar, using keywords such as "silica bioglass," "Sol–gel technique," "Templating," and "Bone scaffold." The analysis considered variables such as the sol–gel method, the templating approach, and materials used to fabricate silica bioglass bone scaffolds. Out of 140 initially identified studies, 92 were selected for detailed review published within the last two decades. Result and conclusion: In this study, the effect of the sol–gel fabrication technique on the improvement of the structure of silicate bioglass bone scaffolds has been reviewed, along with a consideration of the associated advantages and disadvantages. Specifically, the focus of this study was on the templating sol–gel method and its direct impact on morphology and pore structures. Consequently, these findings have evaluated the development of templating sol–gel fabrication techniques for enhancing the bioactivity and biocompatibility of bone scaffolds. © The Author(s) 2024.

关键词： Bioactive glass

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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Novel Framework for an Intrusion Detection System Using Multiple Feature Selection Methods Based on Deep Learning

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Tsinghua Science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy Mohammed Yousuf Uddin Sultan Ahmad Department of Information Systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (IDSs) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust IDS in software-defined networks is an inevitable component of network security. The main challenges of such an IDS are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional IDSs in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust IDSs. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient IDS starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the Software-defined networking (SDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

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Design and Comprehensive Review on Number of Different States of the Art Works Related to Botnet Attack Detection

Design and Comprehensive Review on Number of Different State...

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2024 MIT Art, Design and Technology School of Computing International Conference, MITADTSoCiCon 2024

作者： Bobade, Sandip Y. Apare, Ravindra S. Borhade, Ravindra H. SMT. Kashibai Navale College of Engineering SPPU Computer Engineering Dept Pune India Trinity College of Engineering and Research SPPU IT Department Pune India College of Engineering SPPU STES Smt. Kashibai Navale Department of Computer Engineering Pune India

ISBN: (纸本)9798350362879

The Internet of Things is a novel example for communication which use the internet to connect a extensive range of common place things. A new age of clever gadgets that are effortlessly integrated into our day-To-day lives has arrived as a outcome of its rapid proliferation. Intelligent homes, intelligent offices, smart utilities, intelligent healthcare, intelligent farming, intelligent transportation, intelligent villages, and more have all seen creative uses spurred by this game-changing technology. These applications have the ability to significantly improve our quality of life by enabling proactive, self-governing systems that don't require constant human interaction. Additionally, IoT applications and devices have been smoothly incorporated into key infrastructures, which include transportation networks, healthcare facilities, nuclear power plants, water treatment centres, and power plants. Their integration into these crucial areas has resulted in enhanced functioning and accessibility. capabilities for remote management enable. However, at the same time as IoT has becoming widely adopted, major cyber security flaws have also been exposed. These weaknesses include passwords that are hardcoded and security setups that aren't up to par. The systemic problems can be attributed to IoT providers' past disregard for strong security protocols. Numerous research projects have proposed machine learning centred methods to detect threats like malware networks and Distributed Denial of Service (DDoS) assaults in response to these security challenges. Interestingly, these methods have demonstrated that applying optimization algorithms can significantly increase machine learning accuracy. Still, there is a constant search for more sophisticated methods, driven by the need to overcome the limitations that come with making mistakes. © 2024 IEEE.

关键词： Internet of things

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The Future of Smart Waste Management in Urban Areas: A Holistic Review

The Future of Smart Waste Management in Urban Areas: A Holis...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Joshi, Aditya Deshmukh, Ashwini Auti, Abhijeet Trinity College of Engineering & Research Department of Electronics & Telecommunication Engineering Pune India Trinity College of Engineering & Research Department of Mechanical Engineering Pune India

ISBN: (纸本)9798350319088

With the global population steadily gravitating towards urban living, the demand for innovative and sustainable waste management solutions is paramount. This paper presents a comprehensive framework for Smart Waste Management (SWM) designed to address the escalating challenges of waste disposal in urban areas. It integrates cutting-edge technologies, including IoT-enabled sensors and real-time data analytics, to transform traditional waste collection processes. Emphasizing the significance of efficient waste management, the paper explores existing approaches and initiatives worldwide, with a particular focus on Sweden's successful waste management practices. The proposed framework introduces a systematic approach to waste segregation at its source, deploying colour-coded bags and bins for effective categorization. A diverse array of sensors, including ultrasonic and infrared can be seamlessly integrated into the framework. These sensors, in conjunction with microcontrollers and cloud storage, form a robust Smart Garbage Bin (SGB) network. The data generated by the SGBs undergoes meticulous processing, contributing to optimal route selection and dynamic route optimization, the Vehicle Tracking System (VTS), a waste monitoring application, provides real-time monitoring of waste collection operations, enhancing transparency and operational control. The proposed framework aims to revolutionize waste management, offering an environmentally sustainable, cost-effective, and efficient solution for urban areas facing burgeoning waste challenges. © 2023 IEEE.

关键词： Smart city

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