Genetic biofortification: advancing crop nutrition to tackle hidden hunger.

Malnutrition, often termed "hidden hunger," represents a pervasive global issue carrying significant implications for health, development, and socioeconomic conditions. Addressing the challenge of inadequate essential nutrients, despite sufficient caloric intake, is crucial. Biofortification emerges as a promising solution by enhance the presence of vital nutrients like iron, zinc, iodine, and vitamin A in edible parts of different crop plants. Crop biofortification can be attained through either agronomic methods or genetic breeding techniques. Agronomic strategies for biofortification encompass the application of mineral fertilizers through foliar or soil methods, as well as leveraging microbe-mediated mechanisms to enhance nutrient uptake. On the other hand, genetic biofortification involves the strategic crossing of plants to achieve a desired combination of genes, promoting balanced nutrient uptake and bioavailability. Additionally, genetic biofortification encompasses innovative methods such as speed breeding, transgenic approaches, genome editing techniques, and integrated omics approaches. These diverse strategies collectively contribute to enhancing the nutritional profile of crops. This review highlights the above-said genetic biofortification strategies and it also covers the aspect of reduction in antinutritional components in food through genetic biofortification.

Solid-State Cholesteric Liquid Crystals as an Emerging Platform for the Development of Optical Photonic Sensors.

Over the past decade, solid-state cholesteric liquid crystals (CLCsolid ) have emerged as a promising photonic material, heralding new opportunities for the advancement of optical photonic biosensors and actuators. The periodic helical structure of CLCsolid s gives rise to their distinctive capability of selectively reflecting incident radiation, rendering them highly promising contenders for a wide spectrum of photonic applications. Extensive research is conducted on utilizing CLCsolid 's optical characteristics to create optical sensors for bioassays, diagnostics, and environmental monitoring. This review provides an overview of emerging technologies in the field of interpenetrating polymeric network-CLCsolid (IPN) and CLCsolid -based optical sensors, including their structural designs, processing, essential materials, working principles, and fabrication methodologies. The review concludes with a forward-looking perspective, addressing current challenges and potential trajectories for future research.

Impact of Ce doping on the optoelectronic and structural properties of a CsPbIBr2 perovskite solar cell.

This paper provides a detailed analysis of pure CsPbIBr2 and 4% Ce-doped CsPbIBr2 perovskite films, emphasizing their structural, optical and photovoltaic properties. X-ray diffraction confirms a predominant cubic perovskite phase in both samples, with Ce doping leading to the increased crystal size (21 nm to 32 nm). UV-vis spectroscopy reveals a reduced bandgap energy (2.2 eV to 2.1 eV) with Ce doping. Dielectric constant analysis indicates the enhanced permittivity of the Ce-doped sample, crucial for solar-cell light trapping. Energy band structure analysis demonstrates improved photovoltaic cell performance with Ce doping, yielding higher open-circuit voltage, short-circuit current, and efficiency (9.71%) compared to pure CsPbIBr2 (8.02%). Ce doping mitigates electron-hole recombination, enhancing cell stability, electron affinity, and power output. This research underscores the potential of cost-effective, efficient, and stable CsPbIBr2 perovskite solar cells.

The structural, optical and photovoltaic properties of Zn-doped MAPbI2Br perovskite solar cells.

The spin coating method was used to deposit MAPbI2Br films on FTO-glass substrates. Zn2+ (zinc) doping was used for these films at intensity rates of 2% and 4%, respectively. XRD analysis proved that MAPbI2Br films had a cubic structure and a crystalline character. 2% Zn doping into the MAPbI2Br film had a modest large grain size (38.09 nm), Eg (1.95 eV), high refractive index (2.66), and low extinction coefficient (1.67), according to XRD and UV-vis analyses. To facilitate and enhance carrier transit, at contacts as well as throughout the bulk material, the perovskite's trap-state densities decreased. The predicted MAPbI2Br valence and conduction band edges are -5.44 and -3.52, respectively. The conduction band (CB) edge of the film that was exposed to Zn atoms has been pressed towards the lower value, assembly it a better material for solar cells. EIS is particularly useful for understanding charge carrier transport, recombination mechanisms, and the influence of different interfaces within the device structure. Jsc is 11.09 mA cm-2, Voc is 1.09, PCE is 9.372% and FF is 0.777. The cell made with the 2% Zn doped into the MAPbI2Br film demonstrated a superior device.

Harnessing plant extracts for eco-friendly synthesis of iron nanoparticle (Fe-NPs): Characterization and their potential applications for ameliorating environmental pollutants.

Iron-nanoparticles (Fe-NPs) are increasingly been utilized in environmental applications due to their efficacy and strong catalytic activities. The novelty of nanoparticle science had attracted many researchers and especially for their green synthesis, which can effectively reuse biological resources during the polymerization reactions. Thus, the synthesis of Fe-NPs utilizing plant extracts could be considered as the eco-friendly, simple, rapid, energy-efficient, sustainable, and cost-effective. The green synthesis route can be recognized as a practical, valuable, and economically effective alternative for large-scale production. During the production process, some biomolecules present in the extracts undergo metal salts reduction, which can serve as both a capping and reducing mechanism, enhancing the reactivity and stability of green-synthesized Fe-NPs. The diversity of species provided a wide range of potential sources for green synthesis of Fe-NPs. With improved understanding of the specific biomolecules involved in the bioreduction and stabilization processes, it will become easier to identify and utilize new, potential plant materials for Fe-NPs synthesis. Newly synthesized Fe-NPs require different characterization techniques such as transmission electron microscope, ultraviolet-visible spectrophotometry, and X-ray absorption fine structure, etc, for the determination of size, composition, and structure. This review described and assessed the recent advancements in understanding green-synthesized Fe-NPs derived from plant-based material. Detailed information on various plant materials suitable of yielding valuable biomolecules with potential diverse applications in environmental safety. Additionally, this review examined the characterization techniques employed to analyze Fe-NPs, their stability, accumulation, mobility, and fate in the environment. Holistically, the review assessed the applications of Fe-NPs in remediating wastewaters, organic residues, and inorganic contaminants. The toxicity of Fe-NPs was also addressed; emphasizing the need to refine the synthesis of green Fe-NPs to ensure safety and environmental friendliness. Moving forward, the future challenges and opportunities associated with the green synthesis of Fe-NPs would motivate novel research about nanoparticles in new directions.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Nitric oxide confers cadmium tolerance in fragrant rice by modulating physio-biochemical processes, yield attributes, and grain quality traits.

Cadmium (Cd) stress causes serious disruptions in plant metabolism, physio-biochemical processes, crop yield, and quality characters. Nitric oxide (NO) improves the quality features and nutritional contents of fruit plants. However, how NO confers Cd toxicity in fragrant rice plants, is sparse. Hence, the present study investigated the effects of 50 µM NO donor sodium nitroprusside (SNP) on physio-biochemical processes, plant growth attributes, grain yield, and quality traits of fragrant rice under Cd stress (100 mg kg-1 soil). The results revealed that Cd stress diminished rice plant growth, impaired photosynthetic apparatus and antioxidant defense system, and deteriorated the grain quality traits. However, foliar application of SNP mitigated Cd stress by improving plant growth and gas exchange attributes. Higher electrolyte leakage (EL) was accompanied with elevated levels of malondialdehyde (MDA) and hydrogen peroxide (H2O2) under Cd stress; however, exogenous application of SNP reduced them. The activities and relative expression levels of enzymatic antioxidants; superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and ascorbate peroxidase (APX) and non-enzymatic antioxidants, glutathione (GSH) contents were reduced by Cd stress, while SNP application regulated their activity and transcript abundances. SNP application improved fragrant rice grain yield and 2-acetyl-1-pyrroline content by 57.68 % and 75.54 % respectively, which is concomitant with higher biomass accumulation, photosynthetic efficiency, photosynthetic pigment contents, and an improved antioxidant defense system. Collectively, our results concluded that SNP application regulated the fragrant rice plant physio-biochemical processes, yield traits and grain quality characters under Cd-affected soil.

Exogenously applied melatonin enhanced the tolerance of Brassica napus against cobalt toxicity by modulating antioxidant defense, osmotic adjustment, and expression of stress response genes.

The excessive accumulation of cobalt (Co) in plant tissues severely impairs plant growth that ultimately reduces the yield. However, melatonin (MT) has been known to mediate the abiotic stress tolerance in plants. The present study aimed at investigating the protective mechanisms of exogenously applied MT (0, 50 and 100 µM) under Co (0, 100, 200 and 300 µM) stress by focusing on morpho-physiological, biochemical and cellular characterizations of Brassica napus plants. Cobalt (300 µM) alone treatment drastically inhibited the stomatal conductance, plant height (45%), leaf area (30%), free amino acid (139%), relative electrolyte leakage (109%), and total soluble sugars (71%), compared with the control. However, the exogenous supply of MT notably minimized the oxidative damage, lipid peroxidation and maintained the membrane integrity under Co-toxicity by restricting the overproduction of ROS (H2O2 and O2•), and MDA in leaves and roots. Melatonin significantly enhanced the activities of ROS-scavenging antioxidant enzymes, secondary metabolism-related phenylalanine ammonia lyase (PAL), polyphenol oxidase (PPO), stress-responsive genes (heat shock protein as HSP-90, methyl transferase as MT) and regulated the Co-transporters, especially in roots. These findings indicated that an exogenous supply of MT improve the plant morphology, photosynthetic apparatus, osmotic adjustments, and antioxidant defense systems by enhancing the Co-detoxification in B. napus plants.

Alternate wetting and drying modulated physio-biochemical attributes, grain yield, quality, and aroma volatile in fragrant rice.

Alternate wetting and drying (AWD) has been recognized as a water-saving technology in rice production systems; however, pre- and post-flowering AWD could induce changes in yield, quality and aroma biosynthesis in fragrant rice. In the present study, two fragrant rice cultivars (Guixiangzhan and Nongxiang-18) were subjected to AWD till soil water potential reached -25 to -30 kPa during vegetative stage (VS), reproductive stage (RS), and both stages (VS + RS). The AWD did not affect net photosynthesis and gas exchange significantly, while malondialdehyde (MDA), H2 O2 and electrolyte leakage (EL) were higher than in control plants. The AWD treatments variably affected soluble sugars, proline and protein accumulation as well as the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX), and reduced glutathione (GSH) contents. Moreover, filled grain percentage and 1000-grain weight in AWD treatments were found to be statistically similar (p > 0.05) to control, except grains panicle-1 under AWD-VS + RS that was reduced by 11% and 14% for Guixiangzhan and Nongxiang-18, respectively. On average, yield and related attributes in Guixiangzhan remained higher than in Nongxiang-18. In addition, the grain aroma volatile (2-acetyl-1-pyrroline, 2-AP) content increased by 8.79%, 14.45%, and 6.87% and 7.95%, 14.02%, and 5.04% under AWD-VS, AWD-RS, and AWD-VS + RS treatments, for Guixiangzhan and Nongxiang-18, respectively. Overall, AWD treatments, either at VS or RS, could promote rice aroma in terms of accumulation of 2AP, which might be linked with enhanced endogenous proline contents (a precursor for 2AP biosynthesis) without any severe consequences on rice yield and quality.

Biochar application for remediation of organic toxic pollutants in contaminated soils; An update.

Bioremediation of organic contaminants has become a major environmental concern in the last few years, due to its bio-resistance and potential to accumulate in the environment. The use of diverse technologies, involving chemical and physical principles, and passive uptake utilizing sorption using ecofriendly substrates have drawn a lot of interest. Biochar has got attention mainly due to its simplicity of manufacturing, treatment, and disposal, as it is a less expensive and more efficient material, and has a lot of potential for the remediation of organic contaminants. This review highlighted the adverse impact of persistent organic pollutants on the environment and soil biota. The utilization of biochar to remediate soil and contaminated compounds i.e., pesticides, polycyclic aromatic hydrocarbons, antibiotics, and organic dyes has also been discussed. The soil application of biochar has a significant impact on the biodegradation, leaching, and sorption/desorption of organic contaminants. The sorption/desorption of organic contaminants is influenced by chemical composition and structure, porosity, surface area, pH, and elemental ratios, and surface functional groups of biochar. All the above biochar characteristics depend on the type of feedstock and pyrolysis conditions. However, the concentration and nature of organic pollutants significantly alters the sorption capability of biochar. Therefore, the physicochemical properties of biochar and soils/wastewater, and the nature of organic contaminants, should be evaluated before biochar application to soil and wastewater. Future initiatives, however, are needed to develop biochars with better adsorption capacity, and long-term sustainability for use in the xenobiotic/organic contaminant remediation strategy.

Biochar application for the remediation of trace metals in contaminated soils: Implications for stress tolerance and crop production.

In modern agriculture and globalization, the release of trace metals from manufacturing effluents hinders crop productivity by polluting the atmosphere and degrading food quality. Sustaining food safety in polluted soils is critical to ensure global food demands. This review describes the negative effects of trace metals stress on plant growth, physiology, and yield. Furthermore, also explains the potential of biochar in the remediation of trace metal's contaminations in plants by adoption of various mechanisms such as reduction, ion exchange, electrostatic forces of attraction, precipitation, and complexation. Biochar application enhances the overall productivity, accumulation of biomass, and photosynthetic activity of plants through the regulation of various biochemical and physiological mechanisms of plants cultivated under trace metals contaminated soil. Moreover, biochar scavenges the formation of reactive oxygen species, by activating antioxidant enzyme production i.e., ascorbate peroxidase, catalase, superoxide dismutase, peroxidase, etc. The application of biochar also improves the synthesis of stressed proteins and proline contents in plants thus maintaining the osmoprotectant and osmotic potential of the plant under contaminates stress. Integrated application of biochar with other amendments i.e., microorganisms and plant nutrients to improve trace metal remediation potential of biochar and improving crop production was also highlighted in this review. Moreover, future research needs regarding the application of biochar have also been addressed.

A Comprehensive Survey on Signcryption Security Mechanisms in Wireless Body Area Networks.

WBANs (Wireless Body Area Networks) are frequently depicted as a paradigm shift in healthcare from traditional to modern E-Healthcare. The vitals of the patient signs by the sensors are highly sensitive, secret, and vulnerable to numerous adversarial attacks. Since WBANs is a real-world application of the healthcare system, it's vital to ensure that the data acquired by the WBANs sensors is secure and not accessible to unauthorized parties or security hazards. As a result, effective signcryption security solutions are required for the WBANs' success and widespread use. Over the last two decades, researchers have proposed a slew of signcryption security solutions to achieve this goal. The lack of a clear and unified study in terms of signcryption solutions can offer a bird's eye view of WBANs. Based on the most recent signcryption papers, we analyzed WBAN's communication architecture, security requirements, and the primary problems in WBANs to meet the aforementioned objectives. This survey also includes the most up to date signcryption security techniques in WBANs environments. By identifying and comparing all available signcryption techniques in the WBANs sector, the study will aid the academic community in understanding security problems and causes. The goal of this survey is to provide a comparative review of the existing signcryption security solutions and to analyze the previously indicated solution given for WBANs. A multi-criteria decision-making approach is used for a comparative examination of the existing signcryption solutions. Furthermore, the survey also highlights some of the public research issues that researchers must face to develop the security features of WBANs.

A Computationally Efficient Online/Offline Signature Scheme for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) have emerged as the most widely used wireless network infrastructure in many applications. Sensing nodes are frequently deployed in hostile aquatic environments in order to collect data on resources that are severely limited in terms of transmission time and bandwidth. Since underwater information is very sensitive and unique, the authentication of users is very important to access the data and information. UWSNs have unique communication and computation needs that are not met by the existing digital signature techniques. As a result, a lightweight signature scheme is required to meet the communication and computation requirements. In this research, we present a Certificateless Online/Offline Signature (COOS) mechanism for UWSNs. The proposed scheme is based on the concept of a hyperelliptic curves cryptosystem, which offers the same degree of security as RSA, bilinear pairing, and elliptic curve cryptosystems (ECC) but with a smaller key size. In addition, the proposed scheme was proven secure in the random oracle model under the hyperelliptic curve discrete logarithm problem. A security analysis was also carried out, as well as comparisons with appropriate current online/offline signature schemes. The comparison demonstrated that the proposed scheme is superior to the existing schemes in terms of both security and efficiency. Additionally, we also employed the fuzzy-based Evaluation-based Distance from Average Solutions (EDAS) technique to demonstrate the effectiveness of the proposed scheme.

Anomaly Detection in Traffic Surveillance Videos Using Deep Learning.

In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abnormal events or incidents regarding traffic on different roads and highways, such as traffic jams, traffic congestion, and vehicle accidents. Mostly in accidents, people are helpless and some die due to the unavailability of emergency treatment on long highways and those places that are far from cities. This research proposes a methodology for detecting accidents automatically through surveillance videos. A review of the literature suggests that convolutional neural networks (CNNs), which are a specialized deep learning approach pioneered to work with grid-like data, are effective in image and video analysis. This research uses CNNs to find anomalies (accidents) from videos captured by the VTSS and implement a rolling prediction algorithm to achieve high accuracy. In the training of the CNN model, a vehicle accident image dataset (VAID), composed of images with anomalies, was constructed and used. For testing the proposed methodology, the trained CNN model was checked on multiple videos, and the results were collected and analyzed. The results of this research show the successful detection of traffic accident events with an accuracy of 82% in the traffic surveillance system videos.

Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers.

Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.

Malware Detection in Internet of Things (IoT) Devices Using Deep Learning.

Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is preprocessed using scaling, normalization, and de-noising, whereas in the second step, features are selected and one hot encoding is applied followed by the ensemble classifier based on CNN and LSTM outputs for detection of malware. We have compared results with the state-of-the-art methods and our proposed method outperforms the existing methods on standard datasets with an average accuracy of 99.5%.

An Ensemble Learning Method for Emotion Charting Using Multimodal Physiological Signals.

Emotion charting using multimodal signals has gained great demand for stroke-affected patients, for psychiatrists while examining patients, and for neuromarketing applications. Multimodal signals for emotion charting include electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, and galvanic skin response (GSR) signals. EEG, ECG, and GSR are also known as physiological signals, which can be used for identification of human emotions. Due to the unbiased nature of physiological signals, this field has become a great motivation in recent research as physiological signals are generated autonomously from human central nervous system. Researchers have developed multiple methods for the classification of these signals for emotion detection. However, due to the non-linear nature of these signals and the inclusion of noise, while recording, accurate classification of physiological signals is a challenge for emotion charting. Valence and arousal are two important states for emotion detection; therefore, this paper presents a novel ensemble learning method based on deep learning for the classification of four different emotional states including high valence and high arousal (HVHA), low valence and low arousal (LVLA), high valence and low arousal (HVLA) and low valence high arousal (LVHA). In the proposed method, multimodal signals (EEG, ECG, and GSR) are preprocessed using bandpass filtering and independent components analysis (ICA) for noise removal in EEG signals followed by discrete wavelet transform for time domain to frequency domain conversion. Discrete wavelet transform results in spectrograms of the physiological signal and then features are extracted using stacked autoencoders from those spectrograms. A feature vector is obtained from the bottleneck layer of the autoencoder and is fed to three classifiers SVM (support vector machine), RF (random forest), and LSTM (long short-term memory) followed by majority voting as ensemble classification. The proposed system is trained and tested on the AMIGOS dataset with k-fold cross-validation. The proposed system obtained the highest accuracy of 94.5% and shows improved results of the proposed method compared with other state-of-the-art methods.

An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications.

Traditional advertising techniques seek to govern the consumer's opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers' actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction.

A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network's security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network's integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain.

In this paper, a secure energy trading mechanism based on blockchain technology is proposed. The proposed model deals with energy trading problems such as insecure energy trading and inefficient charging mechanisms for electric vehicles (EVs) in a vehicular energy network (VEN). EVs face two major problems: finding an optimal charging station and calculating the exact amount of energy required to reach the selected charging station. Moreover, in traditional trading approaches, centralized parties are involved in energy trading, which leads to various issues such as increased computational cost, increased computational delay, data tempering and a single point of failure. Furthermore, EVs face various energy challenges, such as imbalanced load supply and fluctuations in voltage level. Therefore, a demand-response (DR) pricing strategy enables EV users to flatten load curves and efficiently adjust electricity usage. In this work, communication between EVs and aggregators is efficiently performed through blockchain. Moreover, a branching concept is involved in the proposed system, which divides EV data into two different branches: a Fraud Chain (F-chain) and an Integrity Chain (I-chain). The proposed branching mechanism helps solve the storage problem and reduces computational time. Moreover, an attacker model is designed to check the robustness of the proposed system against double-spending and replay attacks. Security analysis of the proposed smart contract is also given in this paper. Simulation results show that the proposed work efficiently reduces the charging cost and time in a VEN.