Sensitivity analysis of bi-metal stacked-gate-oxide hetero-juncture tunnel fet with Si0.6Ge0.4 source biosensor considering non-ideal factors.

This article provides insights in designing a dielectrically modulated biosensor by adopting high-k stacked gate oxide proposition in a bi-metal hetero-juncture Tunnel Field Effect Transistor (BM-SO-HTFET) with Si0.6Ge0.4 source. The integrated effect of heterojunction and stacked gate oxide leads to enhanced electrical performance of the proposed device in terms of carrier mobility and suppressed leakage current. Nano-cavity engraved beneath the bi-metal gate structure across the source/channel end acts the binding site of the biomolecules to be detected. This Configuration leads to improved control of biomolecules over source/channel tunnelling rate and the same is reflected in the sensing ability of the device while extracting the ON current sensitivity (SON) of the sensor. The reported biosensor is simulated using Silvaco ATLAS calibrated simulation framework. The analysis of the device sensitivity is carried out varying dielectric constants (k) of various biomolecules, both neutral as well as charged. Our study reveals that BM-SO-HTFET with Ge mole fraction composition x = 0.4 exhibits sensitivity as high as 4.1 × 1010 for neutral biomolecules and 3.2 × 1011 for positively charged biomolecules with k = 12. Furthermore, a transient response profile for the drain current with various biomolecules is explored to determine the varying settling time. From the simulation results, it is noted that BM-SO-HTFET exhibits ON current sensitivity of 4.1 × 1010 and 3.2 × 1011 for neutral and charged biomolecules respectively. In addition to this, for highly sensitive and real time detection of biomolecules, the impact of temperature and certain non-ideal factors drifting from ideal case of fully filled cavity have also been considered to analyze its optimum sensing performance.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation.

The statewide consumer transportation demand model analyzes consumers' transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety. This study proposes an approach named PODE that utilizes federated learning (FL) to train the deep neural network to predict the truck destination state, and in the context of origin-destination (OD) estimation, sensitive individual location information is preserved as the model is trained locally on each device. FL allows the training of our DL model across decentralized devices or servers without exchanging raw data. The primary components of this study are a customized deep neural network based on federated learning, with two clients and a server, and the key preprocessing procedures. We reduce the number of target labels from 51 to 11 for efficient learning. The proposed methodology employs two clients and one-server architecture, where the two clients train their local models using their respective data and send the model updates to the server. The server aggregates the updates and returns the global model to the clients. This architecture helps reduce the server's computational burden and allows for distributed training. Results reveal that the PODE achieves an accuracy of 93.20% on the server side.

Electrochemical Sensors for Heavy Metal Ion Detection in Aqueous Medium: A Systematic Review.

Heavy metal ions (HMIs) are very harmful to the ecosystem when they are present in excess of the recommended limits. They are carcinogenic in nature and can cause serious health issues. So, it is important to detect the metal ions quickly and accurately. The metal ions arsenic (As3+), cadmium (Cd2+), chromium (Cr3+), lead (Pb2+), and mercury (Hg2+) are considered to be very toxic among other metal ions. Standard analytical methods like atomic absorption spectroscopy, atomic fluorescence spectroscopy, and X-ray fluorescence spectroscopy are used to detect HMIs. But these methods necessitate highly technical equipment and lengthy procedures with skilled personnel. So, electrochemical sensing methods are considered to be more advantageous because of their quick analysis with precision and simplicity to operate. They can detect a wide range of heavy metals providing real-time monitoring and are cost-effective and enable multiparametric detection. Various sensing applications necessitate severe regulation regarding the modification of electrode surfaces. Numerous nanomaterials such as graphene, carbon nanotubes, and metal nanoparticles have been extensively explored as interface materials in electrode modifiers. These nanoparticles offer excellent electrical conductivity, distinctive catalytic properties, and high surface area resulting in enhanced electrochemical performance. This review examines different HMI detection methods in an aqueous medium by an electrochemical sensing approach and studies the recent developments in interface materials for altering the electrodes.

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence.

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M. The simulation results for design examples outperform the techniques in the available literature in terms of amplitude and aliasing distortion, reaching distortion around 2.4489 × 10-4 and 3.4907 × 10-9, respectively. This optimization algorithm's usefulness is further demonstrated with the sub-band coding of ECG signals. Implementing optimal prototype filters has tangible real-world effects, especially in critical sectors like healthcare and communications, improving diagnostics accuracy, data transmission efficiency, and overall performance.

Enhancing underwater target localization through proximity-driven recurrent neural networks.

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions. This paper presents a novel approach to employing RNNs to increase the accuracy of underwater target localization by exploiting the temporal dynamics of proximity-informed data. This method uses an RNN architecture to track changes in audio emissions from underwater targets sensed by a microphone network. Using the temporal correlations represented in the data, the RNN learns patterns indicative of target localization quickly and correctly. Furthermore, the addition of proximity-based features increases the model's ability to understand the relative distances between hydrophone nodes and the target, resulting in more accurate localization estimates. To evaluate the suggested methodology, thorough simulations and practical experiments were carried out in a variety of underwater environments. The results show that the RNN-based strategy beats conventional methods and works effectively even in difficult settings. The utility of the proximity-aware RNN model is demonstrated, in particular, by considerable reductions in the mean estimate error (MEE), an important performance measure.

Improving prediction of maternal health risks using PCA features and TreeNet model.

Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother's health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women's safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance.

Background: Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. Methods: In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. Results: The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption. ML algorithms, however, require a large amount of data to be adequate. There may be privacy violations due to collecting this data. To tackle this problem, this study proposes a federated deep learning (FDL) architecture developed around a deep neural network (DNN) paradigm. The study employs the ASHRAE RP-884 standard dataset for experimentation and analysis, which is available to the general public. The data is normalized using the min-max normalization approach, and the Synthetic Minority Over-sampling Technique (SMOTE) is used to enhance the minority class's interpretation. The DNN model is trained separately on the dataset after obtaining modifications from two clients. Each client assesses the data greatly to reduce the over-fitting impact. The test result demonstrates the efficiency of the proposed FDL by reaching 82.40% accuracy while securing the data.

White blood cells classification using multi-fold pre-processing and optimized CNN model.

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.

The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.

Effect of Lactobacillus plantarum LMT1-48 on Body Fat in Overweight Subjects: A Randomized, Double-Blind, Placebo-Controlled Trial.

BACKGROUND: We investigated whether Lactobacillus plantarum strain LMT1-48, isolated from Korean fermented foods and newborn feces, is a suitable probiotic supplement to treat overweight subjects. METHODS: In this randomized, double-blind, placebo-controlled clinical trial, 100 volunteers with a body mass index of 25 to 30 kg/m2 were assigned randomly (1:1) to receive 2×1010 colony forming units of LMT1-48 or to a placebo treatment group. Body composition was measured by dual-energy X-ray absorptiometry, and abdominal visceral fat area (VFA) and subcutaneous fat area were measured by computed tomography scanning. Changes in body fat, VFA, anthropometric parameters, and biomarkers were compared between the two treatment groups (ClinicalTrials.gov number: NCT03759743). RESULTS: After 12 weeks of treatment, the body weight decreased significantly from 76.6±9.4 to 75.7±9.2 kg in the LMT1-48 group but did not change in the placebo group (P=0.022 between groups). A similar pattern was found in abdominal VFA between the two groups (P=0.041). Serum insulin levels, the corresponding homeostasis model assessment of insulin resistance, and leptin levels decreased in the LMT1-48 group but increased in the placebo group (all P<0.05). Decrease in body weight and body mass index by treatment with LMT1-48 was correlated with increase in Lactobacillus levels significantly. LMT1-48 also increased Oscillibacter levels significantly, which were negatively correlated with triglyceride and alanine transaminase levels. CONCLUSION: Administration of LMT1-48 decreased body weight, abdominal VFA, insulin resistance, and leptin levels in these subjects with overweight, suggesting its anti-obesogenic therapeutic potential.

Internet of Things Framework for Oxygen Saturation Monitoring in COVID-19 Environment.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like normal flu; asymptomatic version of COVID-19 has become a critical issue. Therefore, as a precautionary measurement, the oxygen level needs to be monitored by every individual if no other critical condition is found. It is not the only parameter for COVID-19 detection but, as per the suggestions by different medical organizations such as the World Health Organization, it is better to use oximeter to monitor the oxygen level in probable patients as a precaution. People are using the oximeters personally; however, not having any clue or guidance regarding the measurements obtained. Therefore, in this article, we have shown a framework of oxygen level monitoring and severity calculation and probabilistic decision of being a COVID-19 patient. This framework is also able to maintain the privacy of patient information and uses probabilistic classification to measure the severity. Results are measured based on latency of blockchain creation and overall response, throughput, detection, and severity accuracy. The analysis finds the solution efficient and significant in the Internet of Things framework for the present health hazard in our world.

Improved ECG Watermarking Technique Using Curvelet Transform.

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient's data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient's data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance-even when the hidden messages were large size.

Lactobacillus plantarum LMT1-48 exerts anti-obesity effect in high-fat diet-induced obese mice by regulating expression of lipogenic genes.

Obesity is a major health problem and is known to be closely associated with metabolic diseases. Abnormal hepatic accumulation of fat causes fatty liver or hepatic steatosis, and long-term consumption of a high-fat diet is known to be a key obesity-causing factor. Recent studies have demonstrated that probiotics such as Lactobacillus strains, exert an anti-obesity effect by regulating adipogenesis. However, it is still unknown how the consumption of probiotics can reduce abdominal fat volume by regulating the hepatic expression of lipogenic genes. Therefore, we evaluated the effect of long-term ingestion of L. plantarum LMT1-48 on the expression of lipogenic genes in high-fat diet (HFD)-fed mice. We observed that treatment of 3T3-L1 adipocytes with L. plantarum LMT1-48 extract inhibited their differentiation and lipid accumulation by downregulating lipogenic genes, namely, PPARγ, C/EBPα, FAS, and FABP4. Interestingly, administration of L. plantarum LMT1-48 reduced liver weight and liver triglycerides concurrently with the downregulation of the lipogenic genes PPARγ, HSL, SCD-1, and FAT/CD36 in the liver, resulting in the reduction of body weight and fat volume in HFD-fed obese mice. Notably, we also observed that the administration of at least 106 CFU of L. plantarum LMT1-48 significantly lowered body weight and abdominal fat volume in modified diet-fed mouse models. Collectively, these data suggest that L. plantarum LMT1-48 is a potential healthy food for obese people.

Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks.

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.e. unknown nodes with direct communication, and with indirect communication for energy efficiency. In direct communication, source beacon nodes forward the information directly to the unknown nodes, but a common beacon node is selected for communication which reduces overall energy consumption during transmission in indirect communication. Finally, a correction factor is also introduced, and localized unknown nodes are upgraded into helper nodes for reducing the localization error. To analyze the efficiency and effectiveness of the proposed algorithm, various simulations are conducted and compared with the existing algorithms.

Antiobesity Effects of Lactobacillus plantarum LMT1-48 Accompanied by Inhibition of Enterobacter cloacae in the Intestine of Diet-Induced Obese Mice.

The gut microbiota is the most important environmental factor that plays a role in inducing obesity. The gram-negative bacteria, Enterobacter cloacae strains, recently identified in obese mice are considered to be pathogenic bacteria in the gut. Probiotics are important members of the gut microbiota and exert beneficial effects, including inhibiting the growth of potential pathogenic bacteria. Therefore, we isolated a total of 230 lactic acid bacteria from traditional, Korean fermented foods and fecal samples from newborn infants, including Lactobacillus plantarum LMT1-48, which exhibited maximal antimicrobial activity against E. cloacae. We next investigated the functional antiobesity effects of L. plantarum LMT1-48 in an E. cloacae-induced high-fat diet (HFD)-fed animal obesity model. To this end, the L. plantarum LMT1-48 showed antiobesity effects, including body weight loss and reduction of abdominal fat volume, which was accompanied by a decrease in leptin and total cholesterol levels in E. cloacae-induced HFD-fed mice. Notably, gut microbiota diversity also increased after long-term ingestion of L. plantarum LMT1-48, resulting in amelioration of obesity in E. cloacae-induced HFD-fed mice. Accordingly, results suggest that dietary intake of L. plantarum LMT1-48 protects against the onset of E. cloacae-induced obesity.

Improved Location Estimation in Wireless Sensor Networks Using a Vector-Based Swarm Optimized Connected Dominating Set.

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations.

New DoS Defense Method Based on Strong Designated Verifier Signatures.

We present a novel technique for source authentication of a packet stream in a network, which intends to give guarantees that a specific network flow really comes from a claimed origin. This mechanism, named packet level authentication (PLA), can be an essential tool for addressing Denial of Service (DoS) attacks. Based on designated verifier signature schemes, our proposal is an appropriate and unprecedented solution applying digital signatures for DoS prevention. Our scheme does not rely on an expensive public-key infrastructure and makes use of light cryptography machinery that is suitable in the context of the Internet of Things (IoT). We analyze our proposed scheme as a defense measure considering known DoS attacks and present a formal proof of its resilience face to eventual adversaries. Furthermore, we compare our solution to already existent strategies, highlighting its advantages and drawbacks.

Clustering and Flow Conservation Monitoring Tool for Software Defined Networks.

Prediction systems present some challenges on two fronts: the relation between video quality and observed session features and on the other hand, dynamics changes on the video quality. Software Defined Networks (SDN) is a new concept of network architecture that provides the separation of control plane (controller) and data plane (switches) in network devices. Due to the existence of the southbound interface, it is possible to deploy monitoring tools to obtain the network status and retrieve a statistics collection. Therefore, achieving the most accurate statistics depends on a strategy of monitoring and information requests of network devices. In this paper, we propose an enhanced algorithm for requesting statistics to measure the traffic flow in SDN networks. Such an algorithm is based on grouping network switches in clusters focusing on their number of ports to apply different monitoring techniques. Such grouping occurs by avoiding monitoring queries in network switches with common characteristics and then, by omitting redundant information. In this way, the present proposal decreases the number of monitoring queries to switches, improving the network traffic and preventing the switching overload. We have tested our optimization in a video streaming simulation using different types of videos. The experiments and comparison with traditional monitoring techniques demonstrate the feasibility of our proposal maintaining similar values decreasing the number of queries to the switches.

Learning Perfectly Secure Cryptography to Protect Communications with Adversarial Neural Cryptography.

Researches in Artificial Intelligence (AI) have achieved many important breakthroughs, especially in recent years. In some cases, AI learns alone from scratch and performs human tasks faster and better than humans. With the recent advances in AI, it is natural to wonder whether Artificial Neural Networks will be used to successfully create or break cryptographic algorithms. Bibliographic review shows the main approach to this problem have been addressed throughout complex Neural Networks, but without understanding or proving the security of the generated model. This paper presents an analysis of the security of cryptographic algorithms generated by a new technique called Adversarial Neural Cryptography (ANC). Using the proposed network, we show limitations and directions to improve the current approach of ANC. Training the proposed Artificial Neural Network with the improved model of ANC, we show that artificially intelligent agents can learn the unbreakable One-Time Pad (OTP) algorithm, without human knowledge, to communicate securely through an insecure communication channel. This paper shows in which conditions an AI agent can learn a secure encryption scheme. However, it also shows that, without a stronger adversary, it is more likely to obtain an insecure one.