Abstract
Due to resource scarcity, energy crises and environmental pollution, optimization is very important nowadays. For this purpose, various optimization methods have been developed, which are also used in the field of structural design. In this study for the first time, an optimization algorithm based on biogeography with specialized structural objectives was developed and used to minimize the weight and cost of bending steel frames. By adapting the algorithm for use with steel frames, its accuracy and efficiency is increased. In addition, the time to reach the optimal solution was reduced since the search space of the problem has smaller dimensions due to the characteristics of the structure, so that not all variables are evaluated by the objective function; therefore the analysis and design time is reduced. On the other hand, there is a logical balance between the two important features: exploration and exploitation in the optimization process, reducing the computational efforts in the analysis and design of frames. To show the performance and efficiency of this algorithm, four two-dimensional steel frames with three, ten, fifteen and twenty-four stories is presented and the geometric and design constraints is applied according to LRFD-AISC. The developed algorithm selects the optimal W-sections for beams and columns of the frames from the list of 267 W-sections. The optimization codes are written in MATLAB and the SAP2000 software is connected to MATLAB for the analysis and design of steel frames. The results show that this algorithm can develop superior frame designs with low weight compared to other optimization methods and improves computational efficiency in solving structural optimization problems.
Abstract
This study deals with a nonlocal isotropic thermoelastic solid with diffusion. Mathematical formulation of the problem has been prepared subjected to GN theory of type III. Laplace transform and Fourier transform have been used to solve the problem. The considered solid is under influence of thermomechanical sources. Numerical inversion techniques have been used to find the solution in physical domain. Effect of nonlocal parameter has been depicted on various field variables. Some particular cases have also been considered to validate the results. This research highlights the significant role of nonlocal parameters in understanding the thermomechanical interactions for future developments in material science and engineering.
Key Words
diffusion; fourier transformation; laplace transformation; nonlocal; thermoelastic; stress
Address
Parveen Lata, Satya Bir Singh: Department of Mathematics, Punjabi University, Patiala, 147002, India
Belay Fikadu Gerba: Department of Mathematics, Dambi Dollo University, Oromia, Ethiopia
Abstract
Blockchain is a promising technology that allows transactions to access the data in a decentralized and secure environment among participants on the open ledger network. Each bidder can access the copy of data in the blockchain network. On-chain data privacy for the permissionless blockchain e-bidding systems is the major issue of ensuring bid confidentiality, and bidder anonymity and privacy-preserving verification remains open challenge. To address this issue, the proposed Privacy-Preserving Enhanced Ring signature with Paillier based on Batch Verification for secure E-bidding (ERSPBV) that integrates ring-signed bid submission with Paillier Homomorphic Encryption (PHE) and batch verification smart contract to realize secure bidding, anonymity, and fine-grained access control. Ring signature ensures unlikability and anonymity of bidders during bid submission providing strong bidder privacy, this ensure untrace ability but prevent double signing. PHE supports secure aggregation and computation on ciphertexts without revealing individual values. The auctioneer checks bidder authorization using bidder authentication smart contract. Only those registered and permitted bidders to participate in the auction. Batch verification allows the auctioneer to verify multiple signatures submitted by multiple bidders at once to reduce total verification time. The bidders and auctioneer are interacting with each other through the smart contracts to achieve privacy and fairness and tamper-resistance. Emulated the cryptographic primitives and smart contract of this proposed e-bidding scheme on the Ethereum platform and the performance evaluation shows that high computational efficiency and secure bid processing is feasible in the Sepolia test network.
Key Words
blockchain technology; data privacy; e-bidding; homomorphic encryption; smart contract
Address
T. S. Vasughi, P. Muthulakshmi: Department of Computer Science, Faculty of Science and Humanities, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu 603203, India
Abstract
With the increasing number of networked devices, elevated network speeds, and sophisticated cyber threats, cybersecurity has become a difficult problem to solve. Many current approaches fall short due to inefficient data preprocessing, redundant features, low adaptability, and failure to recognize complex behavioral patterns. To address these challenges, an intelligent hybrid framework is proposed to deal with cybersecurity problems in smart networks. An Intelligent Adaptive Routing–Density Fusion Model (IARDFM) is utilized for efficiently collecting network traffic data and preprocessing it to remove noise, redundant, and outlying data while increasing quality and balance of the data. The preoptimized features are then processed by a Deep Probabilistic Cluster-Aware Behavior Learning Network (DPCB-Net) that utilizes deep learning with probabilistic clustering to perform accurate multiclass behavioral learning. A Spectral Hierarchical Swarm Feature Optimization Framework (SHSFOF) is then used to select the most relevant features for training a learning-based approach. To detect various types of cyber-attacks, a Graph Reinforced Intrusion Detection System (GRIDS) is proposed based on graph-based learning and trust-aware evaluation. A Deep Reinforcement Learning–Based Intrusion-Resilient Secure Routing (DRL-ISR2) framework is used for selectively routing packets to different destinations to prevent intrusion and increase network resilience. Comprehensive experiments and comparisons validate improved accuracy, precision, recall, and robustness of the intelligent hybrid framework for smart network cybersecurity.
Key Words
adaptive routing; cyber threat detection and prevention; density-based clustering; feature optimization and selection techniques; graph-based intrusion detection systems; probabilistic learning methods; reinforcement learning techniques for network security problems; trust evaluation models
Address
Jayalakshmi Sambandam: Department of Computer Science and Engineering (Emerging Technologies), SRM Institute of Science and Technology (SRM IST), Vadapalani Campus, Chennai – 600026, Tamil Nadu, India
S. Malathi: Department of Computer Science, St. Thomas College of Arts and Science, Chennai – 600107, Tamil Nadu, India
L. Logeshwari: Department of Computer Science with Data Science, Tagore College of Arts and Science, Chennai – 600044, Tamil Nadu, India
J. Dillibabu: Department of Computer Applications, St. Thomas College of Arts and Science, Chennai – 600107, Tamil Nadu, India
Lithin Kumble: School of Computing Science and Engineering, REVA University, Bengaluru – 562157, Karnataka, India
Abstract
Pedestrian trajectory prediction is fundamental to the safety and efficiency of autonomous systems, intelligent transportation, robotics, and urban management. This paper presents a comprehensive survey of recent advances in deep learning methodologies for pedestrian trajectory forecasting. Focusing on architectures including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), Transformer-based attention models, and generative frameworks, this review covers how these techniques model spatial-temporal dependencies, social interactions, and contextual environment cues. It further discusses multimodal sensor fusion, evaluation metrics, benchmark datasets, applications, challenges such as occlusion and real-time deployment, and outlines future research directions emphasizing goal awareness, transfer learning, and explainability. A critical comparative analysis highlights performance improvements over the past decade and identifies gaps for ongoing innovation, providing researchers and practitioners with a structured understanding of current capabilities and emerging trends.
Key Words
convolutional neural networks (CNNs); graph neural networks (GNNs); long short term memory (LSTM) networks; recurrent neural networks (RNNs)
Address
Evangeline R. C.: Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Nitte (Deemed to be University), Bengaluru, Karnataka, India/ Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru,Affiliated to Visvesvaraya Technological University (VTU), Belagavi, India
Raviraj P.: Department of Computer Science and Engineering, GSSS Institute of Engineering and Technology for Women, Mysuru, Affiliated with Visvesvaraya Technological University (VTU), Belagavi, India
