An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making themmore intelligent and connected. However, this advancement comes with challenges related to the effectiveness ofIoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensuretheir proper functionality. The success of smart systems relies on their seamless operation and ability to handlefaults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore,sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments.To address these concerns, various techniques and algorithms can be employed to enhance the performance ofIoT devices through effective fault detection. This paper conducted a thorough review of the existing literature andconducted a detailed analysis.This analysis effectively links sensor errors with a prominent fault detection techniquecapable of addressing them. This study is innovative because it paves theway for future researchers to explore errorsthat have not yet been tackled by existing fault detection methods. Significant, the paper, also highlights essentialfactors for selecting and adopting fault detection techniques, as well as the characteristics of datasets and theircorresponding recommended techniques. Additionally, the paper presents amethodical overview of fault detectiontechniques employed in smart devices, including themetrics used for evaluation. Furthermore, the paper examinesthe body of academic work related to sensor faults and fault detection techniques within the domain. This reflectsthe growing inclination and scholarly attention of researchers and academicians toward strategies for fault detectionwithin the realm of the Internet of Things.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Modified Black Widow Optimization-Based Enhanced Threshold Energy Detection Technique for Spectrum Sensing in Cognitive Radio Networks

This study develops an Enhanced Threshold Based Energy Detection approach(ETBED)for spectrum sensing in a cognitive radio network.The threshold identification method is implemented in the received signal at the secondary user based on the square law.The proposed method is implemented with the signal transmission of multiple outputs-orthogonal frequency division multiplexing.Additionally,the proposed method is considered the dynamic detection threshold adjustments and energy identification spectrum sensing technique in cognitive radio systems.In the dynamic threshold,the signal ratio-based threshold is fixed.The threshold is computed by considering the Modified Black Widow Optimization Algorithm(MBWO).So,the proposed methodology is a combination of dynamic threshold detection and MBWO.The general threshold-based detection technique has different limitations such as the inability optimal signal threshold for determining the presence of the primary user signal.These limitations undermine the sensing accuracy of the energy identification technique.Hence,the ETBED technique is developed to enhance the energy efficiency of cognitive radio networks.The projected approach is executed and analyzed with performance and comparison analysis.The proposed method is contrasted with the conventional techniques of theWhale Optimization Algorithm(WOA)and GreyWolf Optimization(GWO).It indicated superior results,achieving a high average throughput of 2.2 Mbps and an energy efficiency of 3.8,outperforming conventional techniques.

Research Progress on Detection Techniques of Fungicide Residues in Chinese Chives

Chinese chive is a kind of medicinal and edible plant,with many diseases,and chemical fungicides are usually used for control.In order to find out the risk of pesticide residues in Chinese chives,this paper summarized relevant literatures published in recent years,and sorted out and analyzed the types of pesticides used and detection techniques of common diseases in Chinese chives.

Relevance of Advanced Plant Disease Detection Techniques in Disease and Pest Management for Ensuring Food Security and Their Implication: A Review

Plant diseases and pests present significant challenges to global food security, leading to substantial losses in agricultural productivity and threatening environmental sustainability. As the world’s population grows, ensuring food availability becomes increasingly urgent. This review explores the significance of advanced plant disease detection techniques in disease and pest management for enhancing food security. Traditional plant disease detection methods often rely on visual inspection and are time-consuming and subjective. This leads to delayed interventions and ineffective control measures. However, recent advancements in remote sensing, imaging technologies, and molecular diagnostics offer powerful tools for early and precise disease detection. Big data analytics and machine learning play pivotal roles in analyzing vast and complex datasets, thus accurately identifying plant diseases and predicting disease occurrence and severity. We explore how prompt interventions employing advanced techniques enable more efficient disease control and concurrently minimize the environmental impact of conventional disease and pest management practices. Furthermore, we analyze and make future recommendations to improve the precision and sensitivity of current advanced detection techniques. We propose incorporating eco-evolutionary theories into research to enhance the understanding of pathogen spread in future climates and mitigate the risk of disease outbreaks. We highlight the need for a science-policy interface that works closely with scientists, policymakers, and relevant intergovernmental organizations to ensure coordination and collaboration among them, ultimately developing effective disease monitoring and management strategies needed for securing sustainable food production and environmental well-being.

Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique

The increasing number of security holes in the Internet of Things(IoT)networks creates a question about the reliability of existing network intrusion detection systems.This problem has led to the developing of a research area focused on improving network-based intrusion detection system(NIDS)technologies.According to the analysis of different businesses,most researchers focus on improving the classification results of NIDS datasets by combining machine learning and feature reduction techniques.However,these techniques are not suitable for every type of network.In light of this,whether the optimal algorithm and feature reduction techniques can be generalized across various datasets for IoT networks remains.The paper aims to analyze the methods used in this research and whether they can be generalized to other datasets.Six ML models were used in this study,namely,logistic regression(LR),decision trees(DT),Naive Bayes(NB),random forest(RF),K-nearest neighbors(KNN),and linear SVM.The primary detection algorithms used in this study,Principal Component(PCA)and Gini Impurity-Based Weighted Forest(GIWRF)evaluated against three global ToN-IoT datasets,UNSW-NB15,and Bot-IoT datasets.The optimal number of dimensions for each dataset was not studied by applying the PCA algorithm.It is stated in the paper that the selection of datasets affects the performance of the FE techniques and detection algorithms used.Increasing the efficiency of this research area requires a comprehensive standard feature set that can be used to improve quality over time.

Intelligent Intrusion Detection System for the Internet of Medical Things Based on Data-Driven Techniques

Introducing IoT devices to healthcare fields has made it possible to remotely monitor patients’information and provide a proper diagnosis as needed,resulting in the Internet of Medical Things(IoMT).However,obtaining good security features that ensure the integrity and confidentiality of patient’s information is a significant challenge.However,due to the computational resources being limited,an edge device may struggle to handle heavy detection tasks such as complex machine learning algorithms.Therefore,designing and developing a lightweight detection mechanism is crucial.To address the aforementioned challenges,a new lightweight IDS approach is developed to effectively combat a diverse range of cyberattacks in IoMT networks.The proposed anomaly-based IDS is divided into three steps:pre-processing,feature selection,and decision.In the pre-processing phase,data cleaning and normalization are performed.In the feature selection step,the proposed approach uses two data-driven kernel techniques:kernel principal component analysis and kernel partial least square techniques to reduce the dimension of extracted features and to ameliorate the detection results.Therefore,in decision step,in order to classify whether the traffic flow is normal or malicious the kernel extreme learning machine is used.To check the efficiency of the developed detection scheme,a modern IoMT dataset named WUSTL-EHMS-2020 is considered to evaluate and discuss the achieved results.The proposed method achieved 99.9%accuracy,99.8%specificity,100%Sensitivity,99.9 F-score.

Major allergens in 8 types of allergenic foods and allergen detection techniques

In recent years,the prevalence of allergens in food warning notices,both domestically and internationally,has become the second leading concern after microbial contamination.Among the various factors that threaten human health reported by the World Health Organization,food allergy ranks fourth,and food allergy has become a global security problem.As of now,no definitive treatment for food allergies exists,making the avoidance of allergen-containing foods the most effective prevention method.Consequently,labeling foods with allergen information serves as a crucial warning for allergic populations.Moreover,to enhance comprehension of food allergies,this article provides a brief overview of their definition and sensitization mechanisms.The main focus lies in highlighting the structure of primary allergens found in eight commonly allergenic foods and the resulting clinical symptoms they cause.Additionally,a summary of commonly employed allergen detection techniques is presented,with an analysis of their principles,advantages,and limitations.Looking ahead,the integration of diverse technological approaches to establish an efficient,accurate,and affordable allergen detection method remains a significant trend.This article has certain reference value for understanding the direction of food allergies.

Advanced Optimized Anomaly Detection System for IoT Cyberattacks Using Artificial Intelligence

While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.

Progress in non-invasive detection of liver fibrosis

Liver fibrosis is an important pathological precondition for hepatocellular carcinoma.The degree of hepatic fibrosis is positively correlated with liver cancer.Liver fibrosis is a series of pathological and physiological process related to liver cell necrosis and degeneration after chronic liver injury,which finally leads to extracellular matrix and collagen deposition.The early detection and precise staging of fibrosis and cirrhosis are very important for early diagnosis and timely initiation of appropriate therapeutic regimens.The risk of severe liver fibrosis finally progressing to liver carcinoma is＆gt;50%.It is known that biopsy is the gold standard for the diagnosis and staging of liver fibrosis.However,this method has some limitations,such as the potential for pain,sampling variability,and low patient acceptance.Furthermore,the necessity of obtaining a tissue diagnosis of liver fibrosis still remains controversial.An increasing number of reliable non-invasive approaches are now available that are widely applied in clinical practice,mostly in cases of viral hepatitis,resulting in a significantly decreased need for liver biopsy.In fact,the noninvasive detection and evaluation of liver cirrhosis now has good accuracy due to current serum markers,ultrasound imaging,and magnetic resonance imaging quantification techniques.A prominent advantage of the non-invasive detection and assessment of liver fibrosis is that liver fibrosis can be monitored repeatedly and easily in the same patient.Serum biomarkers have the advantages of high applicability（〉95%）and good reproducibility.However,their results can be influenced by different patient conditions because none of these markers are liver-specific.The most promising techniques appear to be transient elastography and magnetic resonance elastography because they provide reliable results for the detection of fibrosis in the advanced stages,and future developments promise to increase the reliability and accuracy of the staging of hepatic fibrosis.This article aims to describe the recent progress in the development of non-invasive assessment methods for the staging of liver fibrosis,with a special emphasize on computer-aided quantitative and deep learning methods.

Measuring Ca^(2+) influxes of TRPC1-dependent Ca^(2+) channels in HL-7702 cells with Non-invasive Micro-test Technique

AIM： To explore the possibility of using the Noninvasive Micro-test Technique （NMT） to investigate the role of Transient Receptor Potential Canonical 1 （TRPC1） in regulating Ca^2＋ influxes in HL-7702 cells, a normal human liver cell line.METHODS： Net Ca^2＋ fluxes were measured with NMT, a technology that can obtain dynamic information of specific/selective ionic/molecular activities on material surfaces, non-invasively. The expression levels of TRPCl were increased by liposomal transfection, whose effectiveness was evaluated by Western-blotting and single cell reverse transcription-polymerase chain reaction.RESULTS： Ca^2＋ influxes could be elicited by adding 1 mmol/L CaCl2 to the test solution of HL-7702 cells. They were enhanced by addition of 20 μmol/L noradrenalin and inhibited by 100 μmol/L LaCl3 （a non-selective Ca^2＋ channel blocker）; 5 μmol/L nifedipine did not induce any change. Overexpression of TRPCl caused increased Ca^2＋ influx. Five micromoles per liter nifedipine did not inhibit this elevation, whereas 100 μmol/L LaCI3 did.CONCLUSION： In HL-7702 cells, there is a type of TRPCl-dependent Ca^2＋ channel, which could be detected v/a NMT and inhibited by La^3＋.

Bubble counter based on photoelectric technique for leakage detection of cryogenic valves

In order to overcome the inconvenience of manual bubble counting, a bubble counter based on photoelectric technique aiming for automatically detecting and measuring minute gas leakage of cryogenic valves is proposed. Experiments have been conducted on a self-built apparatus, testing the performance with different gas inlet strategies (bottom gas-inlet strategy and side gas-inlet strategy) and the influence of gas pipe length (0, 1, 2, 4, 6, 8, 10 m) and leakage rate (around 10, 20, 30, 40 bubbles/min) on first bubble time and bubble rate. A buffer of 110 cm3 is inserted between leakage source and gas pipe to simulate the down- stream cavum adjacent to the valve clack. Based on analyzing the experimental data, experiential parameters have also been summarized to guide leakage detection and measurement for engineering applications. A practical system has already been suc- cessfully applied in a cryogenic testing apparatus for cryogenic valves.

Ultrasound for Detection of Ascites and for Guidance of the Paracentesis Procedure: Technique and Review of the Literature

Objective: To review the use of ultrasound (US) for the detection of free intraperitoneal fluid (ascites) and for the procedural guidance of the paracentesis procedure. Methods: Two clinical vignettes are presented to review the pertinent diagnostic, management and safety considerations associated with paracentesis. First, US techniques used for the identification of ascites and in the quantification of fluid pockets amenable to aspiration will be discussed. Next, the actual steps required for the performance of US-guided paracentesis will be covered. A review and analysis of the most current literature regarding US and paracentesis then follows. Conclusion: Current literature favors US-guided paracentesis over the traditional blind technique with a significant reduction in both the rate of unsuccessful aspiration of fluid and in the bleeding complications related to this procedure. Use of US for both the diagnostic and therapeutic management of ascites should be advocated as an essential skill for physicians and other health care providers caring for these patients.

New technologies and techniques to improve adenoma detection in colonoscopy

Adenoma detection rate(ADR) is a key component of colonoscopy quality assessment, with a direct link between itself and future mortality from colorectal cancer. There are a number of potential factors, both modifiable and non-modifiable that can impact upon ADR. As methods, understanding and technologies advance, so should our ability to improve ADRs, and thus, reduce colorectal cancer mortality. This article will review new technologies and techniques that improve ADR, both in terms of the endoscopes themselves and adjuncts to current systems. In particular it focuses on effective techniques and behaviours, developments in image enhancement, advancement in endoscope design and developments in accessories that may improve ADR. It also highlights the key role that continued medical education plays in improving the quality of colonoscopy and thus ADR. The review aims to present a balanced summary of the evidence currently available and does not propose to serve as a guideline.

Research Progress on Analysis and Detection Techniques of Veterinary Drug Residues in Animal Foods

As an important guarantee for the prevention and control of animal diseases,veterinary drugs have important functions in improving animal production performance and product quality and maintaining ecological balance.They are an important guarantee for the healthy development of animal husbandry,food safety and public health.However,the irrational use and abuse of veterinary drugs and feed pharmaceutical additives are widespread,causing harmful substances in animal foods and damage to human health,and threatening the sustainable development of the environment and animal husbandry as well.In order to ensure human health,it is urgent to develop a simple,rapid,high-sensitivity,high-throughput and low-cost veterinary drug residue detection technology.In this paper,the sample pretreatment methods and detection techniques for the analysis of veterinary drug residues in animal foods were reviewed.

Rapid <i>In-Vitro</i>and <i>In-Vitro</i>Detection of <i>Chalara fraxinea</i>by Means of Mass Spectrometric Techniques

For the first time, mass spectrometric (MS) techniques were employed to rapidly detect the pathogen Chalara fraxinea in-vitro and directly in-vivo in tissues of diseased ash trees caused by C. fraxinea, using a range of characteristic novel secondary metabolites of C. fraxinea as chemical markers for the presence of the pathogen. We have found an evident correlation between the presence and amount of these-only for C. fraxinea characteristic and novel-secondary metabolites (named chalarafraxinines) and the degree of disease of respective infected ash seedlings. As demonstrated in this work, the MS based high-throughput-screening approach constitute an alternative to the time consuming and expensive micro biological isolation procedures for detection of the pathogen C. fraxinea and furthermore, can be used to rapidly test ash genotypes for resistance / susceptibility to C. fraxinea infection.

Measurement of Ca^(2+) Flow in Cochlear Cells Using Non-Invasive Micro-Test Technique

Objective To test Calcium ion(Ca2+) flow at the head and end of outer hair cells(OHCs) in resting state and in response to Nimodipine treatment.Methods Non-invasive micro-test techniques were used to study Ca2+ in isolated OHCs in adult guinea pigs.Results Four types of Ca2+ transport were identified in OHCs on basilar membrane tissue fragments:influx at the head of with efflux at the bottom(type 1):efflux at the head of OHCs with influx at the bottom(type 2);influx at the both head and bottom(type 3);and efflux at the both head and bottom(type 4).However,only type 1 and type 3 of Ca2+ ion transport were detected in the cochlea.We propose that Ca2+ ion transport exists in adult guinea pig cochlear OHCs in resting state and is variable.Ca2 + flow in OHC can be inhibited by Nimodipine in resting state.

Tamper Detection and Localization for Quranic Text Watermarking Scheme Based on Hybrid Technique

The text of the Quran is principally dependent on the Arabic language.Therefore,improving the security and reliability of the Quran’s text when it is exchanged via internet networks has become one of the most difcult challenges that researchers face today.Consequently,the diacritical marks in the Holy Quran which represent Arabic vowels(i,j.s)known as the kashida(or“extended letters”)must be protected from changes.The cover text of the Quran and its watermarked text are different due to the low values of the Peak Signal to Noise Ratio(PSNR),and Normalized Cross-Correlation(NCC);thus,the location for tamper detection accuracy is low.The gap addressed in this paper to improve the security of Arabic text in the Holy Quran by using vowels with kashida.To enhance the watermarking scheme of the text of the Quran based on hybrid techniques(XOR and queuing techniques)of the purposed scheme.The methodology propose scheme consists of four phases:The rst phase is pre-processing.This is followed by the second phase where an embedding process takes place to hide the data after the vowel letters wherein if the secret bit is“1”,it inserts the kashida but does not insert the kashida if the bit is“0”.The third phase is an extraction process and the last phase is to evaluate the performance of the proposed scheme by using PSNR(for the imperceptibility),and NCC(for the security of the watermarking).Experiments were performed on three datasets of varying lengths under multiple random locations of insertion,reorder and deletion attacks.The experimental results were revealed the improvement of the NCC by 1.76%,PSNR by 9.6%compared to available current schemes.

Detection Technique of Software-Induced Rowhammer Attacks

Side-channel attacks have recently progressed into software-induced attacks.In particular,a rowhammer attack,which exploits the characteristics of dynamic random access memory(DRAM),can quickly and continuously access the cells as the cell density of DRAM increases,thereby generating a disturbance error affecting the neighboring cells,resulting in bit flips.Although a rowhammer attack is a highly sophisticated attack in which disturbance errors are deliberately generated into data bits,it has been reported that it can be exploited on various platforms such as mobile devices,web browsers,and virtual machines.Furthermore,there have been studies on bypassing the defense measures of DRAM manufacturers and the like to respond to rowhammer attacks.A rowhammer attack can control user access and compromise the integrity of sensitive data with attacks such as a privilege escalation and an alteration of the encryption keys.In an attempt to mitigate a rowhammer attack,various hardware-and software-based mitigation techniques are being studied,but there are limitations in that the research methods do not detect the rowhammer attack in advance,causing overhead or degradation of the system performance.Therefore,in this study,a rowhammer attack detection technique is proposed by extracting common features of rowhammer attack files through a static analysis of rowhammer attack codes.

Fast Semantic Duplicate Detection Techniques in Databases

Semantic duplicates in databases represent today an important data quality challenge which leads to bad decisions. In large databases, we sometimes find ourselves with tens of thousands of duplicates, which necessitates an automatic deduplication. For this, it is necessary to detect duplicates, with a fairly reliable method to find as many duplicates as possible and powerful enough to run in a reasonable time. This paper proposes and compares on real data effective duplicates detection methods for automatic deduplication of files based on names, working with French texts or English texts, and the names of people or places, in Africa or in the West. After conducting a more complete classification of semantic duplicates than the usual classifications, we introduce several methods for detecting duplicates whose average complexity observed is less than O(2n). Through a simple model, we highlight a global efficacy rate, combining precision and recall. We propose a new metric distance between records, as well as rules for automatic duplicate detection. Analyses made on a database containing real data for an administration in Central Africa, and on a known standard database containing names of restaurants in the USA, have shown better results than those of known methods, with a lesser complexity.