International Journal of Information Technology最新文献

{"title":"Optimizing rocket trajectories: advanced mathematical modeling in MATLAB/simulink","authors":"Bobomurod Muxammadkarimovich Muxammedov, Andrey Anatolievich Sanko, Davron Aslonqulovich Juraev, Ebrahim E. Elsayed","doi":"10.1007/s41870-024-02162-9","DOIUrl":"https://doi.org/10.1007/s41870-024-02162-9","url":null,"abstract":"<p>The article presents a methodology for generating a simulation Simulink model of a rocket. The use of the MATLAB/Simulink environment for simulating the flight of a rocket and calculating its aerodynamic characteristics is described in detail. The principles of forming blocks for calculating the parameters of a standard atmosphere, aerodynamic characteristics, power plant thrust, flight angles, altitude and flight range are described. The results of numerical experiments carried out using the MATLAB/Simulink environment are presented.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced agricultural supply chain management: integrating blockchain and young’s double-slit experiment for enhanced security","authors":"Esakki Muthu Santhanam, Kartheeban kamatchi","doi":"10.1007/s41870-024-02180-7","DOIUrl":"https://doi.org/10.1007/s41870-024-02180-7","url":null,"abstract":"<p>In the agricultural supply and food, chain Ensuring product safety is important which includes monitoring the effective logistics management and advancements of agricultural products. An effective model that guarantees sufficient safety of the product is required because many issues have been raised regarding contamination risks and food safety. Thus, an efficacious model is introduced in this article referred to as Blockchain Based-Crossover Young’s double-slit (BC-CYD) algorithm, which enables securing agriculture-based data in supply chain management. The developed approach successfully executes the transactions in the traceability and tracking of products with high-level security for the agricultural supply chain. The developed method utilizes an authentication process in provenance tracking and product information storage. The developed BC-CYD method improves safety and efficiency by obtaining higher security, reliability, and integrity. Here, product transactions are stored in the blockchain ledger, thereby, the developed model offers high-level traceability and transparency in a capable manner in the supply chain management. The effectiveness of the proposed BC-CYD method is assayed, where evaluation parameters quantify the efficiency of the developed BC-CYD method. Based on the performance rates of Precision, ROC, accuracy, and Processing time, the developed BC-CYD method’s effectiveness is ascertained as higher. The suggested BC-CYD method yields a greater precision of 97.4% and its accuracy is 98.8% with lower processing time and training time.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142224071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid Sand Cat Swarm Optimization Algorithm-based reliable coverage optimization strategy for heterogeneous wireless sensor networks","authors":"J. David Sukeerthi Kumar, M. V. Subramanyam, A. P. Siva Kumar","doi":"10.1007/s41870-024-02163-8","DOIUrl":"https://doi.org/10.1007/s41870-024-02163-8","url":null,"abstract":"<p>Network coverage plays an indispensable role in determining the Heterogeneous Wireless Sensor Networks (HWSNs) potentiality towards the process of monitoring the physical world with maximized service quality. This HWSNs possesses the limitations of complex deployment environments, poor node reliability and restricted energy which directly influences the transmission and data collection process of sensor nodes and minimizes the network performance. An efficient network coverage controlling mechanism need to be devised and implemented for improving the network service quality, lifetime, reducing energy consumption, and achieve rational utilization of limited resources. In this paper, a Hybrid Sand Cat Swarm Optimization Algorithm-based Reliable Coverage Optimization Strategy (HSCOARCS) is proposed for preventing the issue of coverage redundancy and coverage blind areas, and maximally optimize the sensor node deployment location to achieve reliable sensing and monitoring of target area. This proposed HSCOARCS is implemented over a HWSN coverage mathematical model which represents a problem of combinatorial optimization. The hybridization of Sand Cat Swarm Optimization Algorithm (SCSOA) is achieved for enhancing the speed of the global convergence with the initial population achieved using the method of Gaussian distribution. It targets on the optimization objectives that aids in minimizing the network costs and improve its coverage. The simulation results of the proposed HSSCSOA confirmed better network reliability of 21.38%, network coverage of 19.76%, and minimized energy consumption of 17.92% with different number of sensor nodes on par with the benchmarked schemes used for comparison.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PBb-LMFO: a levy flight integrated MFO inspired ensemble model for cancer diagnosis","authors":"Sabita Rani Behera, Bibudhendu Pati, Sasmita Parida","doi":"10.1007/s41870-024-02122-3","DOIUrl":"https://doi.org/10.1007/s41870-024-02122-3","url":null,"abstract":"<p>To build a Cancer prediction model based on ML, one needs data of a certain sort, such as gene expression data or microarray data. To reduce the dataset's dimensionality, feature selection is proposed as an optimal solution to high dimensionality challenges and to deal with microarray data, this research work aims to perform the 2-stage feature selection. In the initial stage, the Particle Swarm Optimization (PSO) and Bare-bone PSO (BBPSO) are applied to the dataset separately. Then the common features selected by PSO and BBPSO are considered. Then Levy Flight Moth Flame Optimization (LFMFO) is applied to choose the final optimal set of features. Basic existing ML classifiers are used for the first prediction. Afterwards, the Majority Voting technique is applied to develop the ensemble technique. The proposed model is developed over four Cancer microarray datasets, including CNS, Lung Cancer, Ovarian Cancer, and Breast Cancer. The experimental analysis presents the proposed model obtains the highest accuracy of 98.81% for the Ovarian Cancer dataset.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"391 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid domain watermarking approach for authenticated data protection","authors":"N. Radha, K. Meenakshi","doi":"10.1007/s41870-024-02177-2","DOIUrl":"https://doi.org/10.1007/s41870-024-02177-2","url":null,"abstract":"<p>Image watermarking has developed as a prominent research area in the field of data protection. The authenticated data transmitted through the internet is not secure and can be pirated by unauthorized persons. To protect the valid data, Stationary Wavelet Transform (SWT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) based image watermarking algorithm is proposed in the hybrid domain. Initially the original and watermark images are decomposed into approximate (A), vertical (V), horizontal (H), and diagonal subbands (D) using SWT. The approximate band (A) is further decomposed into LL and detail (LH, HL, and HH) subbands using DWT. We calculated SVD for LL and HH subbands of original and watermark images to get the singular values. The singular values of the LL and HH subbands are modified to get the watermarked image. The performance of the proposed model is tested on a standard image dataset. The imperceptibility is evaluated using Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM) metrics, and the robustness is validated based on the effective extraction of watermarks from the attacked watermarked image in terms of Normalized Cross Correlation (NCC).</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"390 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating machine learning algorithms for robust content-based image retrieval","authors":"Maher Alrahhal, K. P. Supreethi","doi":"10.1007/s41870-024-02169-2","DOIUrl":"https://doi.org/10.1007/s41870-024-02169-2","url":null,"abstract":"<p>This study introduces a robust framework for enhancing Content-Based Image Retrieval (CBIR) systems through the integration of supervised and unsupervised machine learning algorithms. Supervised learning algorithms, such as K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and ensemble methods like Bagging and AdaBoost, are used with unsupervised learning techniques, including K-Means and K-Medoids clustering to improve the performance of CBIR. The core of the framework leverages advanced feature extraction methods, specifically ResNet-HOG Visual Word Fusion (RVWF) and ResNet-HOG Feature Fusion (RHFF), which utilize ResNet-50 for capturing high-level semantic information and Histogram of Oriented Gradients (HOG) for detailed texture analysis. A comparison was made between the similarity-based CBIR (standalone CBIR), classification-based CBIR, and clustering-based CBIR methods. The findings reveal that classification-based CBIR methods are superior to standalone and clustering-based CBIR methods in terms of retrieval accuracy and semantic interpretation. The proposed methods outperformed the state-of-the-art methods for different databases used in this study. The proposed frameworks demonstrated superior performance across multiple databases, including VisTex, Brodatz, Corel 10K, and Corel 1K. In the VisTex database, clustering using K-Medoids-based RVWF increased performance from 98.75% to 99.52%, while classification methods like Linear Discriminant or Bagging-based RVWF achieved 100% accuracy. Similarly, in the Brodatz database, K-Medoids-based RVWF clustering improved accuracy from 97.62% to 99.62%, with classification methods such as AdaBoost or Bagging-based RVWF reaching up to 100% accuracy. For the Corel 1K and Corel 10K databases, K-Medoids-based RVWF clustering enhanced results to 95.61% and 99.20% for RVW, respectively, while classification methods further increased accuracy to 98.20% for Corel 1K and 100% for Corel 10K. These results show that combining advanced feature extraction with machine learning algorithms can improve the performance of CBIR systems. CBIR based on clustering proved to outperform standalone CBIR systems, while classification-based CBIR systems offered the best results, making them the most suitable for accurate image retrieval.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved clustering techniques for paediatric cerebral palsy gait assessment during rehabilitation","authors":"Prateek Singhal, Rakesh Kumar Yadav","doi":"10.1007/s41870-024-02115-2","DOIUrl":"https://doi.org/10.1007/s41870-024-02115-2","url":null,"abstract":"<p>The gait abnormality may be the cause of various diseases like foot drop, lower back trembling, and osteoarthritis in the human body. The causes may affect body performance. The problem may be solved if we notice it between the ages of 2 and 20. Today's medical research struggles to identify normality and abnormality in children at a young age. The gait abnormalities depend on a complex neurological condition called cerebral palsy. The article proposes an improved fuzzy C-mean-PSO technique and also defines selection criteria for gait patterns, such as optimal number identification gait profiles, mean square error, silhouette coefficient, and Dunn index. The researcher used 156 patients’ data from the available O’Malley gait dataset for experimental purposes. We partitioned 156 patients into 5 different combinations. In the first two combinations, we applied conventional methods, and the next three employed proposed methods. Finally, we found the 91.6% CPI (Cluster Purity Index), that is greater than existing techniques. In the future, we can perform the proposed methods on various datasets. The findings indicate that employing clustering-based gait profiles improved fuzzy C-mean-PSO optimised using these methods can aid in measuring clinical rehabilitation for children with cerebral palsy.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyber-physical systems for hybrid braking control techniques in hybrid electric vehicles","authors":"Appalabathula Venkatesh, S. Phani Kumar, S. S. Kiran, K. Gurucharan","doi":"10.1007/s41870-024-02184-3","DOIUrl":"https://doi.org/10.1007/s41870-024-02184-3","url":null,"abstract":"<p>During the application of sudden braking in a transportation vehicle, safety is the most significant factor. When brakes are pressed suddenly, a vehicle’s wheels briefly lock, but the braking distance increases and steering control is ended up losing during that phase, but the antilock braking system (ABS) prevents the locking. As a result, a reliable and consistent control system is required to monitor the wheels and supply the adjustable pressure to the brakes. The integration of regenerative and mechanical braking is critical for the top-notch performance and trustworthy in safety features of the vehicle. In this context, cyber-physical systems (CPSs) can be used to develop hybrid control strategies for the braking system of HEVs. There are several braking control strategies available, with the slip control strategy outperforming the others in EV/HEV applications. The slip control strategy provides the variation between the measured motor speed with reference to the relative desired slip, which is internally controlled by the ABS control system. The current research work employs a variety of control strategies. To monitor the behavior of the implemented ABS, a test case is implemented with the HEV application.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Audio spectrogram analysis in IoT paradigm for the classification of psychological-emotional characteristics","authors":"Ankit Kumar, Sushil Kumar Singh, Indu Bhardwaj, Prakash Kumar Singh, Ashish Khanna, Biswajit Brahma","doi":"10.1007/s41870-024-02166-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02166-5","url":null,"abstract":"<p>Psychological activities have various dimensions in which they correlate with their respective behavior generated by the human body. Understanding the relationship of psychological events from external action units is one of the research subjects to explore various human behavior and their dependencies. The study of psychological analysis in the medical field is very time-consuming and costly. It requires constant monitoring of the patient for some time and various interrogation sessions to finalize the emotional severity of an individual. The challenges in exploring human emotions propel the requirement of computer vision techniques in this field. The proposed study explicitly evaluates the recognition of psychological-emotional activities with the help of an audio spectrogram of people fetched through an IoT (Internet of Things) device comprising a microphone to investigate its correlation with psychological events. The audio samples are collected in an asymmetric environment where the chances of the noise are random. Noise cancellation, low power consumption, and sensitivity controls are some of the prominent features of the microphone IoT that have been used to extract raw audio samples. The proposed system follows the extraction of features such as mel-frequency cepstral coefficients (MFCC), harmonic to noise rate (HNR), zero crossing rate (ZCR), and Generative Adversarial Networks (GAN) from the audio spectrogram. The study uses a deep learning-based model containing a convolutional neural network model to recognize and classify different psychological-emotional stages including happiness, anger, disgust, surprise, fear, and sadness from audio spectrogram features. The average accuracy of the classification model for the recognition of all emotions is found to be 99.42% in a maximum of 312 iterations. The model is found to be robust for various applications such as preventing suicidal cases, improving decision-making in the diagnosis of depression patients, improves the overall mental healthcare system.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic clustering based on Minkowski similarity for web services aggregation","authors":"Suad Kamil Ayfan, Dhiah Al-Shammary, Ahmed M. Mahdi, Fahim Sufi","doi":"10.1007/s41870-024-02174-5","DOIUrl":"https://doi.org/10.1007/s41870-024-02174-5","url":null,"abstract":"<p>This research, introduces a new dynamic clustering method offering a new approach utilizing Minkowski Distance methods for calculating similarity of xml messages to effectively compress and aggregate them. The increase in Web services utilization has led to bottlenecks and congestion on network links with limited bandwidth. Furthermore, Simple Object Access Protocol (SOAP) is an eXtensible Markup Language (XML) based messaging system often utilized on the internet. It leads to interoperability by facilitating connection both users and their service providers across various platforms. The large amount and huge size of the SOAP messages being exchanged lead to congestion and bottlenecks. Aggregation tools for SOAP messages can effectively decrease the significant amount that traffic generated. This has shown a notable enhancement in performance. Enhancements can be made by using similarity methods. These techniques group together multiple SOAP messages that share a significant level of similarity. Present techniques utilizing grouping for aggregating XML messages have demonstrated efficiency and compression ratio limitations. Practically, the proposed model groups messages into clusters based on minimum distance, supporting Huffman (variable-length) and (fixed-length) encoding compressing for aggregating multiple compressed XML web messages into a single compact message. Generally, the suggested model’s performance has been evaluated through a comparison with K-Means, Principle Component Analysis (PCA) with K-Means, Hilbert, and fractal self-similarity clustering models. Minkowski distance clustering model has shown excellent performance, especially in all message sizes like small, medium, large, V.large. Technically, the model achieved superior average Compression Ratio and it has outperformed all other models.</p>","PeriodicalId":14138,"journal":{"name":"International Journal of Information Technology","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142184710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}