A modified advanced encryption standard based on lora for monitoring and protecting distributed systems by Vineeth Vellora Veetil, Sophia Sudhir

“…In such an instance, system has the abilities to automatically cut and reconnect the electricity supply while also sending a notification to server. Using a network of IoT, energy consumptions are computed on its own, and the bill is modified online. …”
Get full text

A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity by Ali Khalili Fakhrabadi, Mehdi Jafari Shahbazzadeh, Nazanin Jalali, Mahdiyeh Eslami

“…Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. …”
Get full text

Detection of SSL/TLS protocol attacks based on flow spectrum theory by Shize GUO, Fan ZHANG, Zhuoxue SONG, Ziming ZHAO, Xinjie ZHAO, Xiaojuan WANG, Xiangyang LUO

“…Network attack detection plays a vital role in network security.Existing detection approaches focus on typical attack behaviors, such as Botnets and SQL injection.The widespread use of the SSL/TLS encryption protocol arises some emerging attack strategies against the SSL/TLS protocol.With the network traffic collection environment that built upon the implements of popular SSL/TLS attacks, a network traffic dataset including four SSL/TLS attacks, as well as benign flows was controlled.Considering the problems that limited observability of existing detection and limited separation of the original-flow spatiotemporal domains, a flow spectrum theory was proposed to map the threat behavior in the cyberspace from the original spatiotemporal domain to the transformed domain through the process of “potential change” and obtain the “potential variation spectrum”.The flow spectrum theory is based on a set of separable and observable feature representations to achieve efficient analysis of network flows.The key to the application of flow spectrum theory in actual cyberspace threat behavior detection is to find the potential basis matrix for a specific threat network flow under the condition of a given transformation operator.Since the SSL/TLS protocol has a strong timing relationship and state transition process in the handshake phase, and there are similarities between some SSL/TLS attacks, the detection of SSL/TLS attacks not only needs to consider timing context information, but also needs to consider the high-separation representation of TLS network flows.Based on the flow spectrum theory, the threat template idea was used to extract the potential basis matrix, and the potential basis mapping based on the long-short-term memory unit was used to map the SSL/TLS attack network flow to the flow spectrum domain space.On the self-built SSL/TLS attack network flow data set, the validity of the flow spectrum theory is verified by means of classification performance comparison, potential variation spectrum dimensionality reduction visualization, threat behavior feature weight evaluation, threat behavior spectrum division assessment, and potential variation base matrix heatmap visualization.…”
Get full text

Recognition of Visual Arabic Scripting News Ticker From Broadcast Stream by Moeen Tayyab, Ayyaz Hussain, Mohammed Ali Alshara, Shakir Khan, Reemiah Muneer Alotaibi, Abdul Rauf Baig

“…Moreover, our method is evaluated on a challenging Urdu Printed Text Images (UPTI) dataset that only provides ligature based annotations. …”
Get full text

Towards precision agriculture: metaheuristic model compression for enhanced pest recognition by Sana Parez, Norah Saleh Alghamdi, Tahir Mahmood, Waseem Ullah, Muhammad Attique Khan, Taha Houda, Naqqash Dilshad

Get full text

Comparative analysis of data transformation methods for detecting non-technical losses in electricity grids by Maria Gabriel Chuwa, Daniel Ngondya, Rukia Mwifunyi

“…Convolutional neural networks (CNN) have emerged as effective tools for automatically extracting features from raw data, but raw data often lacks the structure needed for optimal feature extraction. …”
Get full text

Anomaly Detection Dataset for Industrial Control Systems by Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador, Hans Hansson

“…Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitable datasets for evaluating ML algorithms is a challenge. Although a few commonly used datasets may not reflect realistic ICS network data, lack necessary features for effective anomaly detection, or be outdated. …”
Get full text

Graph learning based suicidal ideation detection via tree-drawing test by Ye Liu, Jiashuo Zheng, Yang Zeng, Fang Luo, Xuetao Tian, Xuetao Tian

“…To evaluate this method, a real dataset of 806 students from primary and secondary school in Shaanxi Province, China, is collected, and some metrics including macro-F1, G-mean, and false positive rate are used.ResultsThe results demonstrate that the proposed method significantly outperforms traditional machine learning and convolution neural network approaches. …”
Get full text

An Optimized Transformer–GAN–AE for Intrusion Detection in Edge and IIoT Systems: Experimental Insights from WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT Datasets by Ahmad Salehiyan, Pardis Sadatian Moghaddam, Masoud Kaveh

“…To enhance the training and convergence of the GAN component, we integrate an improved chimp optimization algorithm (IChOA) for hyperparameter tuning and feature refinement. The proposed method is evaluated using three recent and comprehensive benchmark datasets, WUSTL-IIoT-2021, EdgeIIoTset, and TON_IoT, widely recognized as standard testbeds for IIoT intrusion detection research. …”
Get full text

DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction by Guohua Huang, Guohua Huang, Runjuan Xiao, Runjuan Xiao, Chunying Peng, Jinyun Jiang, Weihong Chen

“…DeepRNAac4C integrates residual neural networks, convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and bidirectional gated recurrent units (BiGRU) to effectively capture both local and global sequence features. …”
Get full text

Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neura... by Mizuho Nishio, Osamu Sugiyama, Masahiro Yakami, Syoko Ueno, Takeshi Kubo, Tomohiro Kuroda, Kaori Togashi

“…We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. …”
Get full text

DynBlock: dynamic data encryption with Toffoli gate for IoT by Mubasher Haq, Ijaz Ali Shoukat, Alamgir Naushad, Mohsin Raza Jafri, Moid Sandhu, Abd Ullah Khan, Hyundong Shin

“…Abstract The emergence of 6G networks, coupled with the expansion of smart devices, necessitates robust and efficient cryptography solutions to ensure secure communication. …”
Get full text

Automated classification of chest X-rays: a deep learning approach with attention mechanisms by Burcu Oltu, Selda Güney, Seniha Esen Yuksel, Berna Dengiz

Get full text

Struct2SL: Synthetic lethality prediction based on AlphaFold2 structure information and Multilayer Perceptron by Yurui Huang, Ruzhe Yuan, Yaxuan Li, Zheming Xing, Junyi Li

“…By initiating at the protein feature stratum, Struct2SL offers a novel vantage point to refine the feature representation of gene interactions, thereby enabling more accurate predictions of prospective SL pairs. …”
Get full text

Integration of wing pattern morphology and deep learning to support Plusiinae (Lepidoptera: Noctuidae) pest identification by Karina M. Torres, Chenjiao Tan, Kayla A. Mollet, Allan H. Smith-Pardo, Rui Xu, Changying Li, Silvana V. Paula-Moraes

Get full text

Hybrid Channel Attention Regression U-Net (ARU-Net): An Enhanced Architecture for Brain Tumour Segmentation in Magnetic Resonance Imaging by Noman Ahmed Siddiqui, Muhammad Tahir Qadri, Zain Anwar Ali, Muhammad Ovais Akhter, Ahmed Alsanad, Areej Abdullah Alhogail, Abdu H. Gumaei

Get full text

A Systematic Survey of AI Models in Financial Market Forecasting for Profitability Analysis by Bilal Hassan Ahmed Khattak, Imran Shafi, Abdul Saboor Khan, Emmanuel Soriano Flores, Roberto Garcia Lara, Md. Abdus Samad, Imran Ashraf

Get full text

Dynamic and interpretable deep learning model for predicting respiratory failure following cardiac surgery by Man Xu, Hao Liu, Anran Dai, Qilian Tan, Xinlong Zhang, Rui Ding, Chen Chen, Jianjun Zou, Yongjun Li, Yanna Si

“…Five machine learning models, including logistic regression, multilayer perceptron, extreme gradient boosting, categorical boosting, and deep neural network (DNN), were trained using preoperative and intraoperative variables. …”
Get full text

Dynamic alterations of SEEG characteristics during peri-ictal period and localization of seizure onset zone by Lidao Xu, Yongxin Yang, Qinghua Tan, Hongping Tan, Yifan Wang, Zeliang Hou, Jingxian Shen, Rihui Li, Yuxi Luo, Lizhang Zeng, Qiang Guo, Xuchu Weng, Jiuxing Liang

Get full text

Sex hormones shape EEG-based functional connectivity in early-stage Parkinson’s disease patients by Matteo Conti, Roberta Bovenzi, Mariangela Pierantozzi, Clara Simonetta, Valerio Ferrari, Jacopo Bissacco, Rocco Cerroni, Claudio Liguori, Francesca Di Giuliano, Nicola Biagio Mercuri, Tommaso Schirinzi, Alessandro Stefani

“…PD exhibited a hypoconnected network at θ and α bands and a hyperconnected network at β band compared to HC. …”
Get full text