The emergence of the Internet of Things (IoT) technology has brought about tremendous possibilities, but at the same time, it has opened up new vulnerabilities and attack vectors that could compromise the confidentiality, integrity, and availability of connected systems. Developing a secure IoT ecosystem is a daunting challenge that requires a systematic and holistic approach to identify and mitigate potential security threats. Cybersecurity research considerations play a critical role in this regard, as they provide the foundation for designing and implementing security measures that can address emerging risks. To achieve a secure IoT ecosystem, scientists and engineers must first define rigorous security specifications that serve as the foundation for developing secure devices, chipsets, and networks. Developing such specifications requires an interdisciplinary approach that involves multiple stakeholders, including cybersecurity experts, network architects, system designers, and domain experts. The primary challenge in IoT security is ensuring the system can defend against both known and unknown attacks. To date, the IoT research community has identified several key security concerns related to the architecture of IoT systems. These concerns include issues related to connectivity, communication, and management protocols. This research paper provides an all-inclusive and lucid review of the current state of anomalies and security concepts related to the IoT. We classify and analyze prevalent security distresses regarding IoT’s layered architecture, including connectivity, communication, and management protocols. We establish the foundation of IoT security by examining the current attacks, threats, and cutting-edge solutions. Furthermore, we set security goals that will serve as the benchmark for assessing whether a solution satisfies the specific IoT use cases.

/pdf/a-critical-cybersecurity-analysis-and-future-research-1f7y31dr.pdf

A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review

/pdf/towards-electric-digital-twin-grid-technology-and-framework-2rz8mf5m.pdf

Towards electric digital twin grid: Technology and framework review

Medical image processing is typically performed to diagnose a patient's brain tumor prior to surgery. In this study, a technique in denoising and segmentation was developed to improve medical image processing. The proposed approach employs multiple modules. In the first module, the noisy brain tumor image is transformed into multiple low‐ and high‐pass tetrolet coefficients. In the second module, multiple low‐pass tetrolet coefficients are applied through a modified transform‐based gamma correction method. Generalized cross‐validation is used on multiple high‐pass tetrolet coefficients to obtain the best threshold value. In the third module, all enhanced coefficients are applied to the partial differential equation method. In the final module, the denoised image is applied to Atanassov's intuitionistic fuzzy set histon‐based fuzzy clustering method with centroid optimization using an elephant herding method. Accordingly, the tumor part is segmented from the nontumor part in the magnetic resonance imaging brain images. The method was assessed in terms of peak signal‐to‐noise ratio, mean square error, specificity, sensitivity, and accuracy. The experimental results showed that the suggested method is superior to traditional methods.

Modified transform‐based gamma correction for MRI tumor image denoising and segmentation by optimized histon‐based elephant herding algorithm

In recent years, online reviews are considered as the most significant resource for consumers to make a decision regarding the purchase of a particular product. The reviews can either encourage or relegate a product; therefore posting fake reviews has turned into a money‐spinning business in the modern period. The detection of fake reviews has become a center of attraction for various business people. This research study aims in detecting fake product reviews using four significant phases namely the data pre‐processing, feature extraction, feature selection, and classification. The features obtained in the pre‐processing phase are extracted and selected using chi‐squared technique to obtain a delegate subset among all data and to reduce the complication issues. Then a CNNLSTM‐FABC approach classifies and detects the review as fake or real. Finally, the performance evaluation and the comparative analysis are carried out to determine the effectiveness of the proposed approach. The results reveal that the proposed approach performs well irrespective of the product type and sentiment polarity.

Fuzzy artificial bee colony-based CNN-LSTM and semantic feature for fake product review classification

A novel diffusivity function-based image denoising for MRI medical images

Brain tumor MRI image segmentation using an optimized multi-kernel FCM method with a pre-processing stage

ATMs are vulnerable to a wide variety of assaults and fraud because of the money and personal information available on it. In response, today’s ATMs feature enhanced hardware security systems are capable of identifying specific forms of fraud and manipulation. However, there is no defense in place for future attacks that can’t be anticipated during design. It shows how automated teller machines (ATMs) can be secured against theft without the need for extra hardware. The goal is to employ automatic techniques of model generation to learn normal behavior patterns from the status information of the standard de vices that make up an ATM, with a significant divergence from the taught behavior indicating a fraud attempt. Preprocessing, feature selection, and model training are all parts of the proposed method. Cleaning, integrating, and deduplicating data are all parts of data preprocessing. BOA is employed in feature selection and C-LSTM is used for model training. In C-LSTM, a LSTM recurrent neural network is used to obtain the sentence representation after CNN is used to extract a sequence of higher-level phrase representations. C-LSTM can learn the global and temporal sentence semantics in addition to the local aspects of phrases. When compared to LSTM and CNN, the proposed method fares very well. 

Intelligent System for ATM Fraud Detection System using C-LSTM Approach

Diabetes is a type of disease and is much more dangerous. Normal cells in our body divide in orderly and balanced fashion. Diabetes occurs when certain cells begin to grow out of control. Diabetes cells can also spread to other parts of the body through the blood, nerves or lymph system, in a process called metastasis. Diabetes of the cell in the outer most layer of skin called the epidermis. The epidermis has three layers an upper and middle layer made up of squamous cells, and a bottom layer made up of melanocytes and basal cells. Besides lung diabetes, diabetes is one of the most preventable types. This is due to the fact that the major risk factor is ultraviolet (UV) radiation. The amount of UV exposure depends on the Strength of the light, how long the skin was exposed, and whether the skin was coverd with clothing or sunscreen. The disease can be cured, when it is diagnosed in its early stage. The early diagnosis is difficult using conventional dermatological methods. Non-invasive diagnosis melanoma is possible by image processing technique. Accurate segmentation of skin wounds using dermoscopy images is a challenging task. This thesis proposes an efficient image segmentation algorithm using radial search technique to extract true border of dermoscopy skin images containing wounds. The segmentation algorithm is based on thresholding and finds edges in the image using radial search. The radial search method is a semiautomatic methods and needs manual initialization of search origin. Asymmetry, border, and color are the feature extracted from segmented image. The result shows that this method can be further developed as an assistive tool for detection and classification of diabetes wound using dermoscopy images.

Image processing based wound assessment system for patients with diabetes using six classification algorithms

Integrating Fuzzy C-Means Algorithm with Level Set Methods for Segmentation of Injured Human spinal Cord in Computed Tomography Images

Eliminating products’ fake reviews using network parameters and geo location

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