International Journal of Autonomous and Adaptive Communications Systems (18 papers in press)

Regular Issues

• An improved salp swarm algorithm for collaborative scheduling of discrete manufacturing logistics with multiple depots  
  by Chen Huajun, Yanguang Cai 
  Abstract: Aiming at the situation of a single factory, multiple depots and multiple customers, considering storage, time windows and capacity constraints, a collaborative scheduling of discrete manufacturing logistics with multiple depots (CSDMLMD) model is established. This problem includes discrete manufacturing process, depot storage process and logistics transportation scheduling process. Based on the basic principle of salp swarm algorithm, an improved salp swarm algorithm (ISSA) is proposed to solve the CSDMLMD problem. It is compared with simulated annealing algorithm, genetic algorithm and particle swarm algorithm, and relatively good results can be obtained. The experimental results presented in this paper verify the feasibility of solving this problem.
  Keywords: open shop scheduling; vehicle routing; discrete manufacturing; collaborative scheduling.
  DOI: 10.1504/IJAACS.2025.10059647
   
  
• Detection of Primary User Emulation Attack using the Share and hunt optimization based deep CNN classifier  
  by Asmita A. Desai Asmita A. Desai, Pramod B. Patil 
  Abstract: In this research, the share and hunt optimization-based deep classifier is developed for accurate PUEA detection, which improvise the efficiency of utilization. The detection of primary user emulation attacks and enhancing the primary user performance is done by the three-layered approach in the cognitive radio network. The proposed method investigates the malicious user actions in the CR network, which prevents interference involved in the primary user. The performance of the proposed three-layered approach using the share and hunt optimization based deep CNN classifier is evaluated using the parameters, such as detection rate, delay, and throughput, and the analysis is performed using the Rayleigh and the awgn channel in the CR environment. The detection rate and the throughput of the attack detection are highly accurate and the delay is rapid for the developed method. In imminent, the protection of the CR network is highly improved with other enhanced approaches.
  Keywords: Cognitive Radio Network; Deep CNN; PUEA detection; Optimization; Secured Spectrum sensing.
  DOI: 10.1504/IJAACS.2025.10061537
   
  
• Reversible Data Hiding in Encrypted Images Based on Histogram Shifting and Prediction Error Block Coding  
  by Zhilin Chen, Jiaohua Qin 
  Abstract: To reduce prediction errors and create more room for embedding data, the paper proposes a reversible data hiding in encrypted images scheme based on histogram shifting and prediction error block coding. Firstly, the histogram of the prediction error image is shifted according to the signs of prediction errors. Next, the prediction error plane is partitioned into uniformly sized blocks, and these blocks are labeled as three types: an all-zero block, a block containing only one 1, and a block containing more than one 1. These three types of blocks are compressed using labeling, binary tree coding, and Huffman coding, respectively. To better compress the label map, an improved extended run-length coding is proposed. Finally, the image is secured by encryption and the secret data is hidden within it. The experimental results indicate a significant improvement in the embedding rate of the scheme compared to other schemes.
  Keywords: RDH; reversible data hiding; prediction error; Huffman coding; encrypted images; extended run-length coding.
  DOI: 10.1504/IJAACS.2025.10061182
   
  
• Survey on Sport Video Analysis and Event Detection  
  by Suhas Patel, Dipesh Kamdar 
  Abstract: In recent years, sports video analysis has gained prominence in areas such as sports coaching, player tracking, and event detection. This survey focuses on two main approaches: handcrafted features and deep learning methods. Handcrafted feature-based methods like SIFT, HOG, and SURF show promise in sports video analysis, but have limitations in handling complex actions and require manual parameter tuning. In contrast, deep learning methods, including CNNs and LSTMs, offer automated feature learning and high accuracy in action recognition and event detection. This survey offers insights into the latest techniques, their performance, and future research possibilities. By reviewing research on handcrafted features and deep learning in sports video analysis, it provides a comprehensive understanding of state-of-the-art techniques and research gaps. Sports video analysis can extract crucial information from large video datasets, including action recognition, event detection, and team behavior analysis. Advanced computer vision and machine learning automate analysis for valuable insights.
  Keywords: Sports Video Analysis; Event Detection; CNN; RNN; VGG-16,Hand Crafted Features; Deep Learning.
  DOI: 10.1504/IJAACS.2025.10059628
   
  
• ASER with QAM techniques for SISO communication system over Fisher-Snedecor F fading channels  
  by Rajkishur Mudoi 
  Abstract: The average symbol error rate (ASER) applying various quadrature amplitude modulation (QAM) techniques is analyzed for single input and single output (SISO) system. QAM schemes are more useful to increase bandwidth efficiency for 5G and beyond wireless transmission systems. The channel of the system is influenced by Fisher-Snedecor F composite distribution. This distribution is commonly used to model fading channels due to its high accuracy and mathematical conformity. Various QAM schemes like hexagonal QAM, cross-QAM, square QAM and rectangular QAM are employed for ASER derivations. ASER expressions are acquired with regard to the Fox H-function which is the most general function and Prony approximation for Gaussian Q-function is utilized. Computer simulation is achieved to verify the certainty of the analyzed ASER equations.
  Keywords: Fisher-Snedecor F fading; ASER; Quadrature amplitude modulation (QAM); Prony approximation.
  DOI: 10.1504/IJAACS.2025.10061540
   
  
• Robust Watermarking Algorithm for Screen-Shooting Images Based on Pattern Complexity JND Model  
  by Jia Peng, Jiaohua Qin, Xuyu Xiang, Yun Tan, Dashan Qing 
  Abstract: The popularity of smart devices has made it more convenient for users to take screen shots, but it has also made it easier to take clandestine shots, resulting in compromised and untraceable information. Therefore, this paper introduces a screen-shooting robust watermarking algorithm based on the pattern complexity just noticeable difference (PC-JND) model. This approach involves the utilization of local binary patterns (LBP) for block filtering based on texture complexity in the original image. Stable feature blocks are selected and processed using the speeded-up robust features (SURF) algorithm to extract key feature points, defining them as the watermark embedding regions. Finally, the watermark is embedded in the integer wavelet domain's HH sub-band, guided by the PC-JND model. Experimental results demonstrate that this algorithm not only significantly improves the visual quality of images in a shorter embedding time but also exhibits enhanced robustness against screen captures from various angles.
  Keywords: screen-shooting; robust watermarking; LBP; SURF; JND.
  DOI: 10.1504/IJAACS.2025.10062271
   
  
• Security Vulnerability Analysis and Formal Verification of Smart Contracts: A Review  
  by Monika Bishnoi, Rajesh Bhaitia 
  Abstract: Since the evolution of bitcoin, Blockchain technology has shown promising improvement and application prospects. However, blockchain gained momentum when Vitalik Buterin launched the Ethereum platform in July 2015. It includes smart contracts (SCs), a program to automate, enforce, and verify a set of rules for a transaction to be valid. Since some SCs handle millions of dollars, their security becomes critical. In the past, some hackers have exploited the vulnerabilities in Ethereum SCs and have caused significant losses to the community and users. However, to use blockchain to its full potential, we need SCs; otherwise, it is just a third-party free, decentralized system for transferring money. This paper focuses on the security aspect of SCs in terms of security vulnerabilities and tools developed to discover and locate these vulnerabilities. To prove the correctness of SCs this study also focuses on formal verification techniques used to model and verify SCs.
  Keywords: Blockchain; Smart Contracts; Security vulnerability; Security analysis; Formal methods; Formal verification.
  DOI: 10.1504/IJAACS.2025.10062281
   
  
• Optimal Microstrip MIMO Antenna design: An optimisation based approach  
  by Shaktimayee Mishra, Asit Kumar Panda, Agarwal Arun 
  Abstract: One of the most exciting features of 5G is the MIMO antenna. MIMO technology can increase data transfer speeds while also providing multi-method resistance to fading. This device has demonstrated the ability to improve communication spectral efficiency across a broad range of applications. For that reason, we have developed an optimal design technique for microstrip MIMO antenna, in this work, which uses Modified Shark Smell Optimization to optimally select the antenna design parameters. Our proposed Modified Shark Smell Optimization is a reliable and less vulnerable optimization, which improves the gain and efficiency of the antenna by choosing the optimal design parameters. Further to enhance the accuracy of our optimal design technique, we have used Cauchy's mutation in our work. The measured and simulated results were compared with the conventional algorithms, which show that our proposed MSSO based design technique provide better results in terms of antenna gain and so on.
  Keywords: Microstrip MIMO antenna; Modified Shark Smell Optimization; Optimal parameter selection; Cauchy’s mutation; Mutual coupling; Return loss; Antenna Efficiency.
  DOI: 10.1504/IJAACS.2025.10063295
   
  
• Cyber Security Automation and Managing Cyber Threats in Network through Smart Techniques: An Intelligent Approach for Future Gen. Systems  
  by Rohit Rastogi, Vaibhav Sharma, Tushar Gupta, Vaibhav Gupta 
  Abstract: Cybersecurity has become a major concern in this digital era. Since, the cyberattacks and their types are increasing at an immense rate, it is not humanly possible to monitor, identify and take actions against the attacks. With the current automation systems majorly relying on supervised learning algorithms where they have already seen the type of attacks to monitor and manage the attacks, these systems have been rendered inefficient by zero day attacks. The immense potential of AI and utilise it to its full potential in the field of cybersecurity. If correctly applied, Artificial Intelligence can help to detect and deal with the cyberattacks more efficiently and can help protect users that are not very security conscious and are not aware about the dangers of these security breaches. The authors have decided to utilise machine learning algorithms like decision trees and knowledge discovery in database (KDD) to detect zero day attacks as well as handle other common cyberattacks.
  Keywords: Supervised Learning; Unsupervised Learning; KDD (Knowledge Discovery in Database); phishing; smashing; DDoS.
  DOI: 10.1504/IJAACS.2025.10063962
   
  
• DDoS attack detection and prevention model using Pipit Flying Fox optimization-based Deep Neural network  
  by Anuja Sharma, Parul Saxena 
  Abstract: The software-defined network (SDN) remains the futuristic model that helps to satisfy the new application demands of future networks. However, the control panel of SDN is the prime target of destructive attacks, especially distributed denial of service (DDoS). The restrictions in the conventional techniques such as reliability to network topology, low accuracy, and hardware dependencies manifest the need for effective DDoS detection. Hence, the research develops a DDoS attack recognition and prevention model aid with an optimised deep learning network. The significance relies on the pipit flying fox (PPF) optimisation, which selects the optimal hyperparameters, minimises the errors, and accelerates the learning speed. The experimental results are reported as the specificity, sensitivity, and accuracy of 98.5551%, 92.4951%, and 98.4951% respectively for 80% of training. Further, the values are obtained as 98.6397%, 86.0997%, and 98.09972% for specificity, sensitivity, and accuracy respectively at K-fold 10 which exceeds other competent techniques.
  Keywords: SDN; DDoS attack; security; attack detection; Deep learning; optimization.
  DOI: 10.1504/IJAACS.2025.10064035
   
  
• Joint 5G NR Polar Code-Convolutional Code design for Massive MIMO-UFMC system  
  by Smita Jolania, Ravi Sindal, Ankit Saxena 
  Abstract: Polar codes (PC) are the major contender in fifth generation-New Radio (5G-NR) for error control in the physical downlink control channel (PDCCH) The work proposes a novel concatenated error correction technique of PC with convolutional codes (CC) and is experimented under 5G simulation constraints. This research paper develops a simulation model of Universal Filtered Multicarrier (UFMC) modulation based massive multiple-input multiple output (MIMO) technique targeting for short burst transmissions. The UFMC uses sub-band filtering with reduced out of band emission (OOBE) and enhanced spectral efficiency. An analytical framework of the novel PC-CC-UFMC system to effectively correlate the flexible design parameters for different wireless channels is implemented to enhance Bit Error Rate (BER) performance. The results shown in paper, a gain in the required Signal to Noise Ratio (SNR) for same BER is reduced by approximately 5dB for increase in antenna from 64 to 256.
  Keywords: Polar codes; New Radio; convolutional codes; Massive MIMO; UFMC.
  DOI: 10.1504/IJAACS.2025.10064049
   
  
• Deepfake Detection Based on Single-Domain Data Augmentation  
  by Qian Feng, Zhifeng Xu 
  Abstract: Deepfake has posed a serious threat to personal privacy and social stability The related research on deepfake detection has gained sufficient high accuracy on various datasets, while the generalisation performance is still insufficient Most of the existing methods are aimed at analysing and detecting specific traces and distortions generated by a specific forgery algorithm. However, these detection algorithms typically experience a significant decline in accuracy when detecting forgery videos generated by other algorithms This paper proposed a Deepfake detection scheme based on Single-Domain Data Augmentation, and considered the most difficult situation in the deepfake detection generalization problem: How to generalise to a variety of unknown forgery data when only the real data is known We proposed the Universal Forgery Generation (UFG) and Adversarial Style transfer algorithm (AST) to augment forgery data and improve generalisation ability The experimental results show that our scheme is superior to many existing schemes.
  Keywords: Deepfake detection; Domain generalisation; Style transfer.
  DOI: 10.1504/IJAACS.2025.10064478
   
  
• ADMET Property Prediction Model Based on Feature Selection and Data Mining Techniques  
  by Gu Junlin, Xu Yihan, Sun Juan, Liu Weiwei 
  Abstract: Breast cancer has posed a significant threat to women's health in recent years, and the search for compounds that can antagonize ER? activity will play an important role in breast cancer treatment. ADMET properties are important indicators of compound efficacy, and existing research has used machine learning techniques to fit collected data, but with some performance limitations. In this paper, we use data mining techniques to establish a biologically active-ADMET property prediction model. Firstly, important features were obtained through feature selection techniques, and 23 feature variables that have an impact on ADMET properties were selected. Then, LightGBM and genetic algorithms were used for biological activity prediction tasks, and the R2 value on the validation set reached 0.75, achieving good performance. Finally, based on the BP neural network, the ADMET-UMLP model was constructed, proposing a U-shaped structure to fully utilize the underlying feature information. The model performed well on the validation set, with AUC values exceeding 0.9 in the classification prediction of Caco-2, CYP3A4, hERG, HOB, andMNproperties, and a prediction of 0.98 AUC value for Caco-2, demonstrating good predictive performance.
  Keywords: ADMET; LightGBM; machine learning; prediction.
  DOI: 10.1504/IJAACS.2025.10064499
   
  
• Dual-scale Dual-rate Video Compressive Sensing for Edge Surveillance Device  
  by Yue Lu, Zhang Xiang, Chengsheng Yuan 
  Abstract: Classic video compression method suffers from long encode time and requires large memories, making it hard to deploy on edge devices, thus video compressive sensing which requires less resources during encoding is getting more attention. We propose a dual-scale dual-rate video compressive sensing algorithm for surveillance video compression. Proposed method extracts and compresses foreground area and reference frame separately using dual-scale compressive sampling, then using reversible neural network to reconstruct original frames. Finally we test compressive sampling and ROI extraction network in proposed method on edge device and reconstruction network on server. The experiments show that proposed method can fast compresses frame and extracts foreground area on edge computing devices, achieves higher reconstruction quality.
  Keywords: Video compressive sensing; reversible neural network; surveillance video; siamese network; edge computing; neural processing unit; RK3399 Pro.
  DOI: 10.1504/IJAACS.2025.10064730
   
  
• Analysis of secrecy performance under double shadowed -  
  by Damepaia Lato, Rajkishur Mudoi 
  Abstract: In this paper, the physical layer security (PLS) under the double shadowed - fading channel is investigated. Being a composite fading channel model, it is a realistic representation of the propagation environment in which wireless signals experience a complex interplay of different phenomena. The mathematical statements of the secrecy outage probability (SOP) and the probability of non-zero secrecy capacity (PNSC) have been investigated by considering one legitimate receiver and one eavesdropper listening to the source transmitting confidential information. Based on the obtained mathematical expressions, the secrecy performance metrics are analysed and the results are plotted for both the SOP and the PNSC. It can be observed that as the signal-to-noise ratio (SNR) of the legitimate user increases, the SOP reduces and the secrecy capacity increases for the double shadowed - distributions.
  Keywords: Composite fading channel; Double shadowed ?-? fading channel; Secrecy capacity; Physical layer security; Secrecy outage probability.
  DOI: 10.1504/IJAACS.2025.10064742
   
  
• A Deep Neural Network for Fashion Retrieval Based on Multi-Attention Attribute Manipulation  
  by Qianyi Liu, Jiaohua Qin, Xuyu Xiang, Yun Tan 
  Abstract: The surge in online shopping has heightened the demand for interactive fashion design retrieval. Existing methods, however, exhibit imperfections in attribute segmentation, attributed to the specificity of clothing attributes. The attention region often encounters multiple attributes overlapping, causing changes in one attribute to affect irrelevant ones, resulting in poor retrieval accuracy. This paper addresses this challenge by proposing a deep neural network for fashion retrieval based on multi-attention attribute manipulation. In this approach, the feature extraction module sifts the extracted features to obtain an overall description of the clothing image by adding ESE-NAM combined attention modules to the VoVNet network block. The attribute decoding module utilizes one-hot coding and feature mapping to subdivide the attribute features, obtaining more independent local detail features for refined attribute image retrieval with a focus on details. Experimental results show that the proposed network surpasses exsiting networks with an overall accuracy increase of more than 4 percentage points, particularly with the feature extraction module demonstrating an accuracy boost of over 6 percentage points.
  Keywords: image retrieval; fashion design retrieval; interactive image retrieval; deep neural network; deep learning.
  DOI: 10.1504/IJAACS.2025.10065081
   
  
• Enhancing Malayalam Question Classification in Question Answering Systems: A Comparative Study of SVM, KNN, and Multinomial NB  
  by Bibin P. A, Ravisekhar R, Babu Anto P 
  Abstract: The method of question classification, involving the analysis and assignment of questions to specific categories, has gained momentum due to increased online activity, prompting interest in automating this process into predefined categories. The study focuses on developing a machine learning based model for classifying question types in a Malayalam Question Answering System (QAS). It begins with systematic preprocessing of the dataset and feature extraction, followed by partitioning into training and testing sets. Three machine learning algorithms including support vector machine (SVM), multinomial Naive Bayes (MNB), and K-nearest neighbour (KNN) are implemented and optimised using various hyper-parameters. The evaluation employs metrics like accuracy, precision, recall, F1-score, and confusion matrices to assess performance comprehensively. Results indicate that the SVM classifier achieves the highest accuracy among the models tested. The research underscores the effectiveness of machine learning techniques in automating question classification, especially in diverse linguistic contexts like Malayalam, facilitating more efficient question-answering systems.
  Keywords: Malayalam Question Answering; Multinomial Naïve Bayes; SVM; KNN; Question classification; Machine learning; TF-IDF; n-gram.
  DOI: 10.1504/IJAACS.2025.10065484
   
  
• Deep Learning based PSA Detection Model in Multi-user M-MIMO Networks  
  by Manju V.M, Ganesh R.S 
  Abstract: It is commonly known that MASSIVE MIMO (M-MIMO) is a key component for the forthcoming wireless networks. Base stations (BSs) in M-MIMO networks are fitted with an enormous number of antennas to provide several advantages over conventional MIMO, including easier power control, improved spectrum efficiency, and increased efficiency of energy. Since the estimated CSI might be contaminated by the eavesdrop interaction; M-MIMO systems are susceptible to pilot spoofing attacks (PSAs), which result in significant information leakage in the downstream transmission. To protectgenuine communications, this work introduces a new PSA detection model in multiuser M-MIMO (MU M-MIMO). Initially, signal transmission takes place and then the large scale fading factors are estimated. Further, PSA detection is done using Deep Neural Network (DNN) framework. Finally, the optimal channel estimation is done using Self Customized Black Widow Optimization Algorithm (SC-BWO). Moreover, analysis is performed on error probability, BER and so on.
  Keywords: M-MIMO; Pilot spoofing attacks; Multiuser; Deep Neural Network; SC-BWO Algorithm.
  DOI: 10.1504/IJAACS.2025.10067442