检索结果-内蒙古大学图书馆

LoRa Sense:Sensing and Optimization of LoRa Link Behavior Using Path-Loss Models in Open-Cast Mines

computer Modeling in engineering & sciences 2025年第1期142卷 425-466页

作者： Bhanu Pratap Reddy Bhavanam Prashanth Ragam School of Computer Science and Engineering VIT-AP UniversityAmaravati52237India

The Internet of Things(IoT)has orchestrated various domains in numerous applications,contributing significantly to the growth of the smart world,even in regions with low literacy rates,boosting socio-economic *** study provides valuable insights into optimizing wireless communication,paving the way for a more connected and productive future in the mining *** IoT revolution is advancing across industries,but harsh geometric environments,including open-pit mines,pose unique challenges for reliable *** advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency(RF)protocols such as Bluetooth,Wi-Fi,GSM/GPRS,Narrow Band(NB)-IoT,SigFox,ZigBee,and Long Range Wireless Area Network(LoRaWAN).This study addresses the optimization of network implementations by comparing two leading free-spreading IoT-based RF protocols such as ZigBee and *** field tests are conducted in various opencast mines to investigate coverage potential and signal *** is tested in the Tadicherla open-cast coal mine in ***,LoRaWAN field tests are conducted at one of the associated cement companies(ACC)in the limestone mine in Bargarh,India,covering both Indoor-toOutdoor(I2O)and Outdoor-to-Outdoor(O2O)environments.A robust framework of path-loss models,referred to as Free space,Egli,Okumura-Hata,Cost231-Hata and Ericsson models,combined with key performance metrics,is employed to evaluate the patterns of signal *** field testing and careful data analysis revealed that the Egli model is the most consistent path-loss model for the ZigBee protocol in an I2O environment,with a coefficient of determination(R^(2))of 0.907,balanced error metrics such as Normalized Root Mean Square Error(NRMSE)of 0.030,Mean Square Error(MSE)of 4.950,Mean Absolute Percentage Error(MapE)of 0.249 and Scatter Index(SI)of *** the O2O scenario,the Ericsson model

关键词： Internet of things long range wireless area network ZigBee mining environments path-loss models coefficient of determination mean square error

来源：评论

学校读者我要写书评

暂无评论

Enhancing multiple document summarisation with DNETCNN and BCHOA techniques

引用

International Journal of Business Intelligence and Data Mining 2025年第3-4期26卷 257-281页

作者： Mandava, Mamatha Vinta, Surendra Reddy School of Computer Science and Engineering VIT-AP University Amaravathi India

Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of information available online, it might be challenging to extract the needed information from an internet source these days. To generate the summary, we propose the binary chimp optimisation algorithm (BChOA) in this research. Several preprocessing techniques utilised to remove unwanted terms from the content. Then, for word embedding, FastText is used. The semantic and synthetic features are extracted using the DarkNet-53 and ConvNeXt methods. Using a darknet convolutional neural network (DNetCNN), the features derived from the syntactic and semantic features are concatenated. The Movie review dataset contains 2000 review files, and the BBC news dataset has 50 unique documents. Finally, the outcome demonstrates that our model compares to cutting-edge solutions in terms of semantics and syntactic structure. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

IANFIS: a machine learning–based optimized technique for the classification and segmentation of pancreatic cancer

引用

Research on Biomedical engineering 2024年第2期40卷 373-385页

作者： Dodda, Koteswaramma Muneeswari, G. School of Computer Science and Engineering VIT-AP University Amaravati India

Purpose: One of the most deadly diseases is pancreatic cancer, and it is challenging to find an early stage of cancer, and the cause of the "cancer" symptoms only appears during a difficult period. One helpful treatment for people with pancreatic cancer that lowers mortality is early detection. Due to many factors, including delayed diagnosis and the absence of early warning signs, accurate and early detection of pancreatic tumors is difficult. Methods: Machine learning (ML), which utilizes the benefits of algorithms, has steadily developed in disease prediction, supporting medical professionals in making decisions. This research proposes an intelligent machine learning–based improved adaptive neuro-fuzzy inference system (IANFIS) to classify pancreatic cancer. The IANFIS classifier’s hyper-parameters are optimized using the Bayesian hyper-parameter optimization (BHO) algorithm. Examining the affected portion from the tumor images is the primary goal of the proposed approach. Gabor filtering is used first to reduce noise during pre-processing. The optimal features are then selected by using the Artificial Gorilla Troops Optimizer (AGTO) algorithm. The enhanced red fox optimization algorithm (RFOA) segments the pancreas and the affected region of the tumor. Pancreatic cancer diagnosing accuracy is increased by our proposed optimized machine learning–based classifier. The public pancreatic cancer dataset is used for experimental evaluation. Results: Compared to recent existing methods, the proposed approach has higher diagnostic accuracy, according to the performance analysis results. The proposed research achieves 99.95% accuracy, 99.87% sensitivity, and 99.92% specificity from the experimental results. Conclusions: The machine learning–based system finds the optimum outcome among all input prediction models while considering performance criteria, which increases the system’s effectiveness and helps doctors and radiologists diagnose pancreatic cancer patients more ac

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

MAIPFE:An Efficient Multimodal approach Integrating Pre-Emptive Analysis,Personalized Feature Selection,and Explainable AI

引用

computers, Materials & Continua 2024年第5期79卷 2229-2251页

作者： Moshe Dayan Sirapangi S.Gopikrishnan School of Computer Science and Engineering VIT-AP UniversityAmaravathiAndhra Pradesh522241India

Medical Internet of Things(IoT)devices are becoming more and more common in *** has created a huge need for advanced predictive health modeling strategies that can make good use of the growing amount of multimodal data to find potential health risks early and help individuals in a personalized *** methods,while useful,have limitations in predictive accuracy,delay,personalization,and user interpretability,requiring a more comprehensive and efficient approach to harness modern medical IoT *** is a multimodal approach integrating pre-emptive analysis,personalized feature selection,and explainable AI for real-time health monitoring and disease *** using AI for early disease detection,personalized health recommendations,and transparency,healthcare will be *** Multimodal approach Integrating Pre-emptive Analysis,Personalized Feature Selection,and Explainable AI(MAIPFE)framework,which combines Firefly Optimizer,Recurrent Neural Network(RNN),Fuzzy C Means(FCM),and Explainable AI,improves disease detection precision over existing *** metrics show the model’s superiority in real-time health *** proposed framework outperformed existing models by 8.3%in disease detection classification precision,8.5%in accuracy,5.5%in recall,2.9%in specificity,4.5%in AUC(Area Under the Curve),and 4.9%in delay *** prediction precision increased by 4.5%,accuracy by 3.9%,recall by 2.5%,specificity by 3.5%,AUC by 1.9%,and delay levels decreased by 9.4%.MAIPFE can revolutionize healthcare with preemptive analysis,personalized health insights,and actionable *** research shows that this innovative approach improves patient outcomes and healthcare efficiency in the real world.

关键词： Predictive health modeling Medical Internet of Things explainable artificial intelligence personalized feature selection preemptive analysis

来源：评论

学校读者我要写书评

暂无评论

Artificial intelligence-based approach to detect malicious users using deep learning and optimization techniques

引用

Multimedia Tools and applications 2025年第8期84卷 3979-4001页

作者： Terumalasetti, Sailaja S R, Reeja School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

The pervasive existence of automated accounts on social networks has necessitated the development of sophisticated detecting systems. For cybersecurity professionals, determining malicious users in a computer system's benevolence is a difficult challenge. Malicious users conquered the probability of terminating the system by alluring in unconstitutional actions or larcenous hypersensitive data. The research scrutinizes the efficiency of a complex deep learning method that conglomerates RNN, CNN, and PSO in detecting bot accounts. The research ambitions to compare and scrutinize the efficacy and accurateness of several ML and DL models to determine the most effective strategy for detecting bots. AIMDS (Artificial Intelligence-based malicious user Detection system), an innovative methodology, is intended. A pioneering approach is the AIMDS technique, a state-of-the-art methodology for distinctive malicious users that incorporates deep learning algorithms and optimization techniques. Due to the mounting defenselessness of information transgress and cyber attackers, distinguishing malicious users is a momentous area in cybersecurity. The advocated technique pursues to accomplish inconsistent user patterns by considering immense extents of information produced by digital gadgets, predominantly in social networks as bot or the user. On a public dataset that encompass bot activity data, was scrutinized to expedient the performance of the proposed approach. The inferences exposed that, in assessment to traditional Artificial Intelligence-based algorithms the proposed technique had an enhanced accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bot (Internet)

来源：评论

学校读者我要写书评

暂无评论

RLeU-Net: Segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy Screening

引用

Multimedia Tools and applications 2025年第9期84卷 6113-6134页

作者： Addanki, Sathyavani Sumathi, D. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India

Diabetic Retinopathy (DR) is a primitive cause of blindness in diabetic patients. There are chances for DR in certain cases, due to the damage in the retinal vascular area. It may lead to blindness or loss of vision if DR is not detected. At the final stage, there is a threat of loss of vision. During the initial stage of this chronic disease, it has been observed that no symptoms are required to determine the loss of vision. Hence, there is a need for better and early diagnosis of the blood vessels to prevent vision loss in diabetic patients. The primary task of early diagnosis is to carry out a proper segmentation process. But, segmenting vasculture from retinal photographs is a vital laborious effort. Therefore, blood vessel segmentation of fundus images has grown in favor among researchers. There are many existing methods to process bio-medical retinal images, and most of them are computationally intensive and difficult to deploy in real-time. The objective of this work is to develop a novel and efficient deep learning framework to perform the segmentation with the U-Net as a primary module. The U-Net architecture has been modified so that the model would be lightweight and the model is cost efficient in terms of time and space complexity. The proposed method, DRLeU- Net, is evaluated using a publicly available color retinal fundus images dataset. The model was trained using the k-fold cross-validation technique. The proposed method shows a promising results of 0.988 AUC (Area Under the Curve) and 0.94 IOU (Intersection over Union when compared with the existing *** robust and efficient deep learning framework has been deployed to do the segmentation process efficiently and it has evauated with the expremental results by comparing with the existing methodologies. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blood vessels

来源：评论

学校读者我要写书评

暂无评论

Trusted Certified Auditor Using Cryptography for Secure Data Outsourcing and Privacy Preservation in Fog-Enabled VANETs

引用

computers, Materials & Continua 2024年第5期79卷 3089-3110页

作者： Nagaraju Pacharla K.Srinivasa Reddy School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522237India

With the recent technological developments,massive vehicular ad hoc networks(VANETs)have been established,enabling numerous vehicles and their respective Road Side Unit(RSU)components to communicate with *** best way to enhance traffic flow for vehicles and traffic management departments is to share thedata they *** needs to be more protection for the VANET *** effective and safe methodof outsourcing is suggested,which reduces computation costs by achieving data security using a homomorphicmapping based on the conjugate operation of *** research proposes a VANET-based data outsourcingsystem to fix the *** keep data outsourcing secure,the suggested model takes cryptography models *** will keep the generated keys for the purpose of vehicle *** controlling and overseeingthe outsourced data while preserving privacy,the suggested approach considers the Trusted Certified Auditor(TCA).Using the secret key,TCA can identify the genuine identity of VANETs when harmful messages *** proposed model develops a TCA-based unique static vehicle labeling system using cryptography(TCA-USVLC)for secure data outsourcing and privacy preservation in *** proposed model calculatesthe trust of vehicles in 16 ms for an average of 180 vehicles and achieves 98.6%accuracy for data encryption toprovide *** proposedmodel achieved 98.5%accuracy in data outsourcing and 98.6%accuracy in privacypreservation in fog-enabled *** curve cryptography models can be applied in the future for betterencryption and decryption rates with lightweight cryptography operations.

关键词： Vehicular ad-hoc networks data outsourcing privacy preservation cryptography keys trusted certified auditors data security

来源：评论

学校读者我要写书评

暂无评论

Intelligent optimal archimedes shooty tern deep network (OASTDN) for oral squamous cell carcinoma detection and classification in oral cancer

引用

Multimedia Tools and applications 2025年第15期84卷 15223-15247页

作者： Raj, Mikkili Pruthvi Muneeswari, G. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravathi India

Early detection at the premalignant stage is desirable to prevent Squamous cell carcinoma (SCC) tongue morbidity and death. An automated method for oral cancer identification was developed because conventional early detection and screening models for the disease heavily rely on expert knowledge. Automating the identification of premalignant an8d malignant squamous cell carcinoma (SCC) lesions may offer low-cost and early disease detection. In this manner, the proposed model is presented to improve the identification and recognition of oral cancer in this paper as the Optimal Archimedes Shooty Tern Deep Network (OASTDN).Correspodingly, the detection and recognition are achieved by tuning the weight of Deep Belief Network (DBN) using hybrid theArchimedes optimization algorithm (AOA) and the Shooty Tern Optimization (STO) method were assembled as a novel Archimedes Shooty Tern Optimization Algorithm (ASTOA). Following that, the detection and recognition are achieved by the weight of DBN using optimal tuning approach. Lastly, the proposed model is compared to existing techniques, demonstrating that OASTDN is superior at detecting and classifying squamous cell cancer and *** its early stages. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Optimization

来源：评论

学校读者我要写书评

暂无评论

Correlation Composition Awareness Model with Pair Collaborative Localization for IoT Authentication and Localization

引用

computers, Materials & Continua 2024年第4期79卷 943-961页

作者： Kranthi Alluri S.Gopikrishnan School of Computer Science and Engineering VIT-AP UniversityAmaravathiAndhra Pradesh522241India

Secure authentication and accurate localization among Internet of Things(IoT)sensors are pivotal for the functionality and integrity of IoT *** authentication and localization are intricate and symbiotic,impacting both the security and operational functionality of IoT ***,accurate localization and lightweight authentication on resource-constrained IoT devices pose several *** overcome these challenges,recent approaches have used encryption techniques with well-known key ***,these methods are inefficient due to the increasing number of data breaches in their localization *** proposed research efficiently integrates authentication and localization processes in such a way that they complement each other without compromising on security or *** proposed framework aims to detect active attacks within IoT networks,precisely localize malicious IoT devices participating in these attacks,and establish dynamic implicit authentication *** integrated framework proposes a Correlation Composition Awareness(CCA)model,which explores innovative approaches to device correlations,enhancing the accuracy of attack detection and ***,this framework introduces the Pair Collaborative Localization(PCL)technique,facilitating precise identification of the exact locations of malicious IoT *** address device authentication,a Behavior and Performance Measurement(BPM)scheme is developed,ensuring that only trusted devices gain access to the *** work has been evaluated across various environments and compared against existing *** results prove that the proposed methodology attains 96%attack detection accuracy,84%localization accuracy,and 98%device authentication accuracy.

关键词： Sensor localization IoT authentication network security data accuracy precise location access control security framework

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection Model Using Chaotic Map for Network Coding Enabled Mobile Small Cells

引用

computers, Materials & Continua 2024年第3期78卷 3151-3176页

作者： Chanumolu Kiran Kumar Nandhakumar Ramachandran School of Computer Science and Engineering VIT-AP UniversityAmaravatiAndhra Pradesh522241India

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions,vulnerabilities,and *** security systems,such as Intrusion Detection Systems(IDS),are essential due to the limitations of simpler security measures,such as cryptography and *** to their compact nature and low energy reserves,wireless networks present a significant challenge for security *** features of small cells can cause threats to the *** Coding(NC)enabled small cells are vulnerable to various types of *** attacks and performing secure“peer”to“peer”data transmission is a challenging task in small *** to the low power and memory requirements of the proposed model,it is well suited to use with constrained small *** attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code(HHMAC)hash between transmissions since the HMAC function is generated using the shared *** this research,a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure“peer”to“peer”data transmission model using lightweight H-MAC(1D-LM-P2P-LHHMAC)is proposed with accurate intrusion *** proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels,Key Pair Generation Time Levels,Chaotic Map Accuracy Levels,Intrusion Detection Accuracy Levels,and the results represent that the proposed model performance in chaotic map accuracy level is 98%and intrusion detection is 98.2%.The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high.

关键词： Network coding small cells data transmission intrusion detection model hashed message authentication code chaotic sequence mapping secure transmission

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：