Detection of <i>Escherichia coli</i>and Harmful Enteric Bacterial Pathogens in Domestic Hand-Dug Wells in the Cuvelai Etosha Basin of Namibia

The Cuvelai Etosha Basin of Namibia is characterised by complex aquifer systems with multi-layered aquifers and various water qualities. Some parts of the basin have been covered with a pipeline system that supplies purified surface water from the Kunene River. Locations that lack a pipeline system utilise hand-dug wells as a source of drinking water. These wells draw water from shallow perched aquifers and are not protected from surface contamination nor is the water quality monitored. Sanitised water supply is relevant for the growth and development of societies and is a priority of the United Nations Millennium Development Goals. A bacteriological water quality study aimed at investigating the presence and seasonal variation of;Citrobacter, Escherichia, Klebsiella, Enterobacter, Proteus, Salmonella, Shigella, and Pseudomonas species was conducted on 44 hand-dug wells in the Ohangwena and Omusati regions of the Cuvelai Etosha Basin. Samples were collected from both the wet and dry seasons. Results disclosed the presence of Salmonella, Shigella, Citrobacter, Escherichia, Klebsiella, Enterobacter, Proteus, and Pseudomonas species. Chi-square confirmed a significant seasonal variation in Salmonella (P Shigella (P Citrobacter (P > 0.05), Escherichia (P > 0.05), Klebsiella (P > 0.05), Entero-bacter (P > 0.05), Proteus (P > 0.05) and Pseudomonas (P > 0.05) species. Water from these hand-dug wells is not safe for drinking unless it is subjected to appropriate treatment. It is recommended that hand-dug wells should be properly constructed at safe distances from contaminating structures such as pit latrines and routinely assessed for pathogens, and the water should be sanitized prior to consumption.

Growth Performance of <i>Moringa oleifera</i>and <i>Moringa ovalifolia</i>in Central Namibia Semi-Arid Rangeland Environment

The objective of this study was to compare the field growth performance of Moringa oleifera and Moringa ovalifolia in semi-arid environment of central Namibia rangeland. This part of Namibia has both arid and semi-arid climates. These climates require the growing of drought-resistant fodder trees to aid in the provision of animal feed or supplement. This is paramount to livestock farmers who are striving to meet the feed demand of their animals especially during winter and drought periods. It is upon this background that both Moringa species were grown to evaluate their field growth performances. Moringa oleifera grew faster with 224.9 cm and 281.45 cm heights than Moringa ovalifolia that had 77.025 cm and 113.2 cm heights in 2014/2015 summer season (October 2014 to April 2015) and 2015/2016 summer season (October 2015 to April 2016), respectively, although Moringa ovalifolia is native to Namibia. In Namibia, summer usually starts October and ends April the follow year after which winter follows. Moringa oleifera grew significantly higher (P Moringa ovalifolia, though they belong to the Moringaceae family and were grown under the parallel conditions. Therefore, Moringa oleifera would serve as a better alternative for improving rangelands’ productivity under these adverse climatic and environmental conditions since it can grow faster than Moringa ovalifolia, whose characteristic leads to the rapid establishment of trees and large quantity of leaf-biomass production.

Equivalence of String Classical and Quantum Energy beside Equivalence of Wave Packet and Relativistic Velocity in Eucleadian and Curved Space

Plank quantum and classical string energy relations seem to be uncorrelated. This work correlated them. The relativistic energy-momentum relation has been used together with plank and de Brogglie hypothesis to prove that the wave group velocity is equal to the particle velocity in both ordinary and curved space. The plank energy relation is shown also to be related to the classical energy relation of an oscillating string. Starting from plank energy relation for n photons and performing integration, the expression of classical string energy was obtained. This means that one can treat electromagnetic waves as a collection of continuous photons having frequencies ranging from zero to w. Conversely, starting from classical string energy relation by differentiating it with respect to angular frequency, the plank quantum energy for n photons has been found. This means that the quanta results from separation of electromagnetic waves to single isolated waves. Each wave consists of n photons or quanta.

Assessment of Spatial Distribution and Temporal Trends of Precipitation and Its Extremes over Nigeria

This study analyzed the spatial distribution and temporal trends of precipitation and its extremes over Nigeria from 1979-2013 using climate indices, in order to assess climatic extremes in the country. Daily precipitation data used in this study were obtained from Nigeria Meteorological Agency (NIMET), Lagos. The study used climate indices developed by the Expert Team on Climate Change Detection (ETCCDI) for assessing extreme precipitation. Sen’s slope estimator and Mann-Kendall trend test were employed in data analysis. Results revealed that precipitation and its extremes varied spatially across Nigeria. Significant negative trends were observed in most of the precipitation indices for the period under study. Furthermore, significant downward trends were observed in the CWD (Consecutive Wet Day) while the CDD (Consecutive Dry Day) showed significant upward trends in all the regions. These spatial and temporal changes indicate that Nigeria’s climate is trending towards a warmer and drier condition, which could be attributed to global warming-induced climate change;which altered historical rainfall patterns thereby leading to extreme events. The findings of this study have provided useful information in understanding the extreme events that are assumed by the general populace to be normal recurrent events in Nigeria. The results of the analysis of yearly and decadal changes in precipitation totals and extreme values for the last 35 years (1979-2013) suggest the likelihood of severe impacts on water resources, agriculture, and water-sensitive economic activities

Occupancy Factor Model for Exposure to Natural Radionuclides along the Coastline of Erongo Region, Namibia

In this study, an occupancy factor model was developed and used to calculate the average time spent for outdoor and indoor activities along the coastline of the Erongo region of Namibia. A closed ended questionnaire was developed and administered to 800 respondents who visited the coastline for leisure, occupational and other activities. The mean time allocated for leisure activities ranges from 13.00 to 1.00 h, occupational mean time between 10.18 to 9.06 h and the values of other activities range from 16.66 to 11.00 h. The average computed time spent outdoor was found to be 11.46 h and indoor calculated to be 12.54 h. This shows an outdoor factor of 0.48 and indoor factor of 0.52 respectively. From the results obtained, the value of the absorbed dose rate ranged from 93.27 to 105.95 nGy·hǃ and the annual effective dose rate ranged from 121.01 to 176.61 μSv·yǃ (UNSCEAR factor) and 292.60 to 413.63 μSv·yǃ (present factor). The values obtained for annual effective dose are higher than the acceptable limit. However, from this study, we can conclude that the use of the UNSCEAR outdoor factor in the coastline will lead to underestimation of effective dose by 24% based on the present factor.

Hill Matrix and Radix-64 Bit Algorithm to Preserve Data Confidentiality

There are many cloud data security techniques and algorithms available that can be used to detect attacks on cloud data,but these techniques and algorithms cannot be used to protect data from an attacker.Cloud cryptography is the best way to transmit data in a secure and reliable format.Various researchers have developed various mechanisms to transfer data securely,which can convert data from readable to unreadable,but these algorithms are not sufficient to provide complete data security.Each algorithm has some data security issues.If some effective data protection techniques are used,the attacker will not be able to decipher the encrypted data,and even if the attacker tries to tamper with the data,the attacker will not have access to the original data.In this paper,various data security techniques are developed,which can be used to protect the data from attackers completely.First,a customized American Standard Code for Information Interchange(ASCII)table is developed.The value of each Index is defined in a customized ASCII table.When an attacker tries to decrypt the data,the attacker always tries to apply the predefined ASCII table on the Ciphertext,which in a way,can be helpful for the attacker to decrypt the data.After that,a radix 64-bit encryption mechanism is used,with the help of which the number of cipher data is doubled from the original data.When the number of cipher values is double the original data,the attacker tries to decrypt each value.Instead of getting the original data,the attacker gets such data that has no relation to the original data.After that,a Hill Matrix algorithm is created,with the help of which a key is generated that is used in the exact plain text for which it is created,and this Key cannot be used in any other plain text.The boundaries of each Hill text work up to that text.The techniques used in this paper are compared with those used in various papers and discussed that how far the current algorithm is better than all other algorithms.Then,the Kasiski test is used to verify the validity of the proposed algorithm and found that,if the proposed algorithm is used for data encryption,so an attacker cannot break the proposed algorithm security using any technique or algorithm.

An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

Optimal Deep Learning-based Cyberattack Detection and Classification Technique on Social Networks

Cyberbullying(CB)is a distressing online behavior that disturbs mental health significantly.Earlier studies have employed statistical and Machine Learning(ML)techniques for CB detection.With this motivation,the current paper presents an Optimal Deep Learning-based Cyberbullying Detection and Classification(ODL-CDC)technique for CB detection in social networks.The proposed ODL-CDC technique involves different processes such as pre-processing,prediction,and hyperparameter optimization.In addition,GloVe approach is employed in the generation of word embedding.Besides,the pre-processed data is fed into BidirectionalGated Recurrent Neural Network(BiGRNN)model for prediction.Moreover,hyperparameter tuning of BiGRNN model is carried out with the help of Search and Rescue Optimization(SRO)algorithm.In order to validate the improved classification performance of ODL-CDC technique,a comprehensive experimental analysis was carried out upon benchmark dataset and the results were inspected under varying aspects.A detailed comparative study portrayed the superiority of the proposed ODL-CDC technique over recent techniques,in terms of performance,with the maximum accuracy of 92.45%.

Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

Artificial Intelligence Based Clustering with Routing Protocol for Internet of Vehicles

With recent advances made in Internet of Vehicles(IoV)and Cloud Computing(CC),the Intelligent Transportation Systems(ITS)find it advantageous in terms of improvement in quality and interactivity of urban transportation service,mitigation of costs incurred,reduction in resource utilization,and improvement in traffic management capabilities.Many trafficrelated problems in future smart cities can be sorted out with the incorporation of IoV in transportation.IoV communication enables the collection and distribution of real-time essential data regarding road network condition.In this scenario,energy-efficient and reliable intercommunication routes are essential among vehicular nodes in sustainable urban computing.With this motivation,the current research article presents a new Artificial Intelligence-based Energy Efficient Clustering with Routing(AI-EECR)Protocol for IoV in urban computing.The proposed AI-EECR protocol operates under three stages namely,network initialization,Cluster Head(CH)selection,and routing protocol.The presented AI-EECR protocol determines the CHs from vehicles with the help of Quantum Chemical Reaction Optimization(QCRO)algorithm.QCROalgorithmderives a fitness function with the help of vehicle speed,trust level,and energy level of the vehicle.In order to make appropriate routing decisions,a set of relay nodeswas selected usingGroup Teaching Optimization Algorithm(GTOA).The performance of the presented AI-EECR model,in terms of energy efficiency,was validated against different aspects and a brief comparative analysis was conducted.The experimental outcomes established that AI-EECR model outperformed the existing methods under different measures.

Integration of Fog Computing for Health Record Management Using Blockchain Technology

Internet of Medical Things (IoMT) is a breakthrough technologyin the transfer of medical data via a communication system. Wearable sensordevices collect patient data and transfer them through mobile internet, thatis, the IoMT. Recently, the shift in paradigm from manual data storage toelectronic health recording on fog, edge, and cloud computing has been noted.These advanced computing technologies have facilitated medical services withminimum cost and available conditions. However, the IoMT raises a highconcern on network security and patient data privacy in the health caresystem. The main issue is the transmission of health data with high security inthe fog computing model. In today’s market, the best solution is blockchaintechnology. This technology provides high-end security and authenticationin storing and transferring data. In this research, a blockchain-based fogcomputing model is proposed for the IoMT. The proposed technique embedsa block chain with the yet another consensus (YAC) protocol building securityinfrastructure into fog computing for storing and transferring IoMT data inthe network. YAC is a consensus protocol that authenticates the input datain the block chain. In this scenario, the patients and their family membersare allowed to access the data. The empirical outcome of the proposedtechnique indicates high reliability and security against dangerous threats.The major advantages of using the blockchain model are high transparency,good traceability, and high processing speed. The technique also exhibitshigh reliability and efficiency in accessing data with secure transmission. Theproposed technique achieves 95% reliability in transferring a large number offiles up to 10,000.

Detection of Lung Tumor Using ASPP-Unet with Whale Optimization Algorithm

The unstructured growth of abnormal cells in the lung tissue creates tumor.The early detection of lung tumor helps the patients avoiding the death rate and gives better treatment.Various medical image modalities can help the physicians in the diagnosis of disease.Many research works have been proposed for the early detection of lung tumor.High computation time and misidentification of tumor are the prevailing issues.In order to overcome these issues,this paper has proposed a hybrid classifier of Atrous Spatial Pyramid Pooling(ASPP)-Unet architecture withWhale Optimization Algorithm(ASPP-Unet-WOA).To get a fine tuning detection of tumor in the Computed Tomography(CT)of lung image,this model needs pre-processing using Gabor filter.Secondly,feature segmentation is done using Guaranteed Convergence Particle Swarm Optimization.Thirdly,feature selection is done using Binary Grasshopper Optimization Algorithm.This proposed(ASPPUnet-WOA)is implemented in the dataset of National Cancer Institute(NCI)Lung Cancer Database Consortium.Various performance metric measures are evaluated and compared to the existing classifiers.The accuracy of Deep Convolutional Neural Network(DCNN)is 93.45%,Convolutional Neural Network(CNN)is 91.67%,UNet obtains 95.75%and ASPP-UNet-WOA obtains 98.68%.compared to the other techniques.

Modified Harris Hawks Optimization Based Test Case Prioritization for Software Testing

Generally,software testing is considered as a proficient technique to achieve improvement in quality and reliability of the software.But,the quality of test cases has a considerable influence on fault revealing capability of software testing activity.Test Case Prioritization(TCP)remains a challenging issue since prioritizing test cases is unsatisfactory in terms of Average Percentage of Faults Detected(APFD)and time spent upon execution results.TCP ismainly intended to design a collection of test cases that can accomplish early optimization using preferred characteristics.The studies conducted earlier focused on prioritizing the available test cases in accelerating fault detection rate during software testing.In this aspect,the current study designs aModified Harris Hawks Optimization based TCP(MHHO-TCP)technique for software testing.The aim of the proposed MHHO-TCP technique is to maximize APFD and minimize the overall execution time.In addition,MHHO algorithm is designed to boost the exploration and exploitation abilities of conventional HHO algorithm.In order to validate the enhanced efficiency of MHHO-TCP technique,a wide range of simulations was conducted on different benchmark programs and the results were examined under several aspects.The experimental outcomes highlight the improved efficiency of MHHO-TCP technique over recent approaches under different measures.

Measurement of Noise Pollution in Northern Industrial Areas of Windhoek, Namibia

The increase in industrial activities and vehicular movement along the northern industrial area of Windhoek has vastly increased the amount of traffic noise and other noise pollution in the area. Noise pollution has an adverse health effect to human population, when exposed for a long period. Residence in proximate communities along the north industrial area and those working in the various industries located in the area may be affected, when the noise pollution level exceed the permissible standard for human exposure. A sound level meter was used to measure the amount of noise pollution at the streets of the northern industrial area. The measurements were done during the daytime, at a time interval of 2 hours, from 08:00 am - 06:00 pm. The amount of noise pollution obtained from the study ranges from (64 - 72) dB (A), with a maximum of 72 dB (A) in Bonsmara Street, (67.4 - 75.3) dB (A), with a maximum of 75.3 dB (A) in New Castle Street, (60.5 - 81.0) dB (A), with a maximum of 72.3 dB (A) in Braham Street. (62.5 - 72.3) dB (A), with a maximum of 82.3 dB (A) in Hosea Kutako Street, (66.0 - 82.3) dB (A), with a maximum of 76.8 dB (A) in Simmentaler Street and (65.1 - 76.8) dB (A), with a maximum of 76.8 dB (A) in Dortmund Street. The variation of noise level index L10, L50, L90 and Leq, Noise Climate (NC) and Traffic Noise index (TNI) were calculated. The maximum noise pollution values obtained from the study were higher than the WHO recommended limit of 70 dB (A).

Assessment of Heavy Metals Concentration in Shore Sediments from the Bank of River Benue, North-Central Nigeria

Heavy metals are elements, whose density is greater than water. They are generated from our environment. Rocks, sediments, plants, water and aerosol particles represent the carriers of heavy metals. An accumulated amount of heavy metal in the body, either by inhalation, food or drinking water, can cause an adverse health effect to human. The Benue river passed through the town of makurdi, was high population of the inhabitant of Benue State dwells. The industrial and agricultural activities carried out in this region, increase the concentration of heavy metals. This may result to adverse health effect on the inhabitant of Makurdi. The objectives of this work were to determine the heavy metal concentration and its site contaminations along the bank of river Benue, Makurdi. An inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine the heavy metals concentration. The metals concentrations (Iron, Copper, Manganese, Lead, Zinc, Chromium, Arsenic, and Cadmium) of the three stations were found. This ranges from 3.55 - 9454.0 mg/kg, 0.20 - 8928.0 mg/kg and 2.80 - 13,657 mg/kg for stations 1, 2 and 3. With Fe recorded as the highest concentration in the sediment, this value is compared with the World Health Organisation (WHO) and World Surface Rock Average (WSRA) standard. The assessment on contamination status of heavy metals in the riverbank, showed low degree of contamination in stations 1 and 2, and moderate degree in station 3. The degree of enrichment to heavy metals in all the stations is deficient to minimal. The evaluation of the results of pollution load index (PLI) from this present study indicated PLI 1 in stations 1 and 3. Hence stations 1 and 3 are polluted, while station 2 is not polluted with heavy metals.

Assessment of Radionuclide Content of Shore Sediments Collected from the Bank of River Benue, North-Central Nigeria

Ionizing Radiation emitted from radionuclide has an adverse effect on human health. A continuing population exposure to naturally occurring radioactive materials (NORMS) found in our environment is one of the major scientific subjects that attract public attention. The assessment of radionuclide content of shore sediments of river Benue-North Central Nigeria was carried out using gamma-ray spectrometry. The activity concentrations of U-238, Th-232 and K-40 were found to have an average concentration of 1.17, 3.31 and 405.95 Bq·kg-1 respectively. The values gotten from present study were compared with the world average values. World average values of U-238, Th-232 and K-40 are 50 Bq·kg-1, 50 Bq·kg-1 and 500 Bq·kg-1 respectively. The mean concentration of daughter radionuclides generated from U-238, Th-232 was 4.32, 10.37, 5.24, 3.86, 11.87, and 6.52 for Bi-212, Pb-212, Bi-214, Pb-214, Ra-226 and Ac-228, respectively. The mean of the absorbed dose, Annual effective dose equivalent, for radium equivalent activity, and external hazard index, were evaluated to be 19.45 nGy·h-1, 23.82 μSv·y-1, 37.16 Bq·kg-1, 0.10 mSv·y1, below the permissible limit of 57 nGy·h-1, 70 μSv·y-1, 370 Bq·kg-1 and 1 mSv·y-1 respectively. These shore sediments from river Benue are therefore, radiologically safe for construction and other domestic and industrial purposes.

Evaluating the Potential Effectiveness of Moringa oleifera Seeds Biomass as an Adsorbent in the Removal of Copper (Cu) in Water

This study was based on the evaluation of the potential effectiveness of Moringa oleifera seeds biomass as a biosorbent in the removal of copper (Cu) in water which was justified by the level of toxicity, environmental unfriendliness and costly nature of chemical coagulants presently used. Fourier transform infrared (FTIR) analysis was used to identify the Moringa oleifera seeds biomass functional groups present in the adsorption of metal ions and found to be the carboxylic acid and amine functional groups (-COOH and -NH). The effects of contact time, adsorbent dosage, metal ion concentration and pH were studied. The maximum adsorption capacity at pH 5, room temperature and 0.8 g dosage was 70% for Cu(II). The adsorption data fitted better to the Langmuir than the Freundlich models as the sorption capacity (qm) of Moringa oleifera seeds biomass for Cu(II) was 3.64 mg/g. The separation factor (RL) was within the range of 0 and 1 which showed that the Cu(II) biosorption processes were favourable for Moringa oleifera biosorbent. The results showed that Moringa oleifera seed biomass is an effective adsorbent in the removal of the studied heavy metals in water. The effective pH for the Cu(II) removal was 5.0 as equilibrium was achieved practically in 35 min. The quantitative analysis of defatted Moringa oleifera should be studied in order to have a fair mixing ratio between Moringa oleifera seeds biomass and the adsorbate. There is also the ardent need to work on environmentally friendly disposal of adsorbent after saturation of adsorbent by analyte to avoid secondary pollution.

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence(XAI),a process that explains how prediction is done in AI models.Biomedical mental disorder,i.e.,Autism Spectral Disorder(ASD)needs to be identified and classified at early stage itself in order to reduce health crisis.With this background,the current paper presents XAI-based ASD diagnosis(XAI-ASD)model to detect and classify ASD precisely.The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization(BFO)-based Feature Selection(FS)technique.In addition,Whale Optimization Algorithm(WOA)with Deep Belief Network(DBN)model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA.In order to ensure a better ASD diagnostic outcome,a series of simulation process was conducted on ASD dataset.

Novel Image Encryption and Compression Scheme for IoT Environment

Latest advancements made in the processing abilities of smartdevices have resulted in the designing of Intelligent Internet of Things (IoT)environment. This advanced environment enables the nodes to connect, collect, perceive, and examine useful data from its surroundings. Wireless Multimedia Surveillance Networks (WMSNs) form a vital part in IoT-assistedenvironment since it contains visual sensors that examine the surroundingsfrom a number of overlapping views by capturing the images incessantly.Since IoT devices generate a massive quantity of digital media, it is thereforerequired to save the media, especially images, in a secure way. In order toachieve security, encryption techniques as well as compression techniques areemployed to reduce the amount of digital data, being communicated overthe network. Encryption Then Compression (ETC) techniques pave a wayfor secure and compact transmission of the available data to prevent unauthorized access. With this background, the current research paper presentsa new ETC technique to accomplish image security in IoT environment.The proposed model involves three major processes namely, IoT-based imageacquisition, encryption, and compression. The presented model involves optimal Signcryption Technique with Whale Optimization Algorithm (NMWOA)abbreviated as ST-NMWOA. The optimal key generation of signcryptiontechnique takes place with the help of NMWOA. Besides, the presented modelalso uses Discrete Fourier Transform (DFT) and Matrix Minimization (MM)algorithm-based compression technique. Extensive set of experimental analysis was conducted to validate the effective performance of the proposed model.The obtained values infer that the presented model is superior in terms of bothcompression efficiency and data secrecy in resource-limited IoT environment.

Securing Arabic Contents Algorithm for Smart Detecting of Illegal Tampering Attacks

Themost common digital media exchanged via the Internet is in text form.The Arabic language is considered one of themost sensitive languages of content modification due to the presence of diacritics that can cause a change in the meaning.In this paper,an intelligent scheme is proposed for improving the reliability and security of the text exchanged via the Internet.The core mechanism of the proposed scheme depends on integrating the hidden Markov model and zero text watermarking techniques.The watermark key will be generated by utilizing the extracted features of the text analysis process using the third order and word level of the Markov model.The Embedding and detection processes of the proposed scheme will be performed logically without the effect of the original text.The proposed scheme is implemented using PHP with VS code IDE.The simulation results,using varying sizes of standard datasets,show that the proposed scheme can obtain high reliability and provide better accuracy of the common illegal tampering attacks.Comparison results with other baseline techniques show the added value of the proposed scheme.