Intelligent Intrusion Detection System for Industrial Internet of Things Environment

Rapid increase in the large quantity of industrial data,Industry 4.0/5.0 poses several challenging issues such as heterogeneous data generation,data sensing and collection,real-time data processing,and high request arrival rates.The classical intrusion detection system(IDS)is not a practical solution to the Industry 4.0 environment owing to the resource limitations and complexity.To resolve these issues,this paper designs a new Chaotic Cuckoo Search Optimiza-tion Algorithm(CCSOA)with optimal wavelet kernel extreme learning machine(OWKELM)named CCSOA-OWKELM technique for IDS on the Industry 4.0 platform.The CCSOA-OWKELM technique focuses on the design of feature selection with classification approach to achieve minimum computation complex-ity and maximum detection accuracy.The CCSOA-OWKELM technique involves the design of CCSOA based feature selection technique,which incorpo-rates the concepts of chaotic maps with CSOA.Besides,the OWKELM technique is applied for the intrusion detection and classification process.In addition,the OWKELM technique is derived by the hyperparameter tuning of the WKELM technique by the use of sunflower optimization(SFO)algorithm.The utilization of CCSOA for feature subset selection and SFO algorithm based hyperparameter tuning leads to better performance.In order to guarantee the supreme performance of the CCSOA-OWKELM technique,a wide range of experiments take place on two benchmark datasets and the experimental outcomes demonstrate the promis-ing performance of the CCSOA-OWKELM technique over the recent state of art techniques.

Improved Bat Algorithm with Deep Learning-Based Biomedical ECG Signal Classification Model

With new developments experienced in Internet of Things(IoT),wearable,and sensing technology,the value of healthcare services has enhanced.This evolution has brought significant changes from conventional medicine-based healthcare to real-time observation-based healthcare.Biomedical Electrocardiogram(ECG)signals are generally utilized in examination and diagnosis of Cardiovascular Diseases(CVDs)since it is quick and non-invasive in nature.Due to increasing number of patients in recent years,the classifier efficiency gets reduced due to high variances observed in ECG signal patterns obtained from patients.In such scenario computer-assisted automated diagnostic tools are important for classification of ECG signals.The current study devises an Improved Bat Algorithm with Deep Learning Based Biomedical ECGSignal Classification(IBADL-BECGC)approach.To accomplish this,the proposed IBADL-BECGC model initially pre-processes the input signals.Besides,IBADL-BECGC model applies NasNet model to derive the features from test ECG signals.In addition,Improved Bat Algorithm(IBA)is employed to optimally fine-tune the hyperparameters related to NasNet approach.Finally,Extreme Learning Machine(ELM)classification algorithm is executed to perform ECG classification method.The presented IBADL-BECGC model was experimentally validated utilizing benchmark dataset.The comparison study outcomes established the improved performance of IBADL-BECGC model over other existing methodologies since the former achieved a maximum accuracy of 97.49%.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common formof skin cancer.Since the earlier detection of melanoma reduces the mortality rate,it is essential to design reliable and automated disease diagnosis model using dermoscopic images.The recent advances in deep learning(DL)models find useful to examine the medical image and make proper decisions.In this study,an automated deep learning based melanoma detection and classification(ADL-MDC)model is presented.The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma.The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage.Besides,the k-means clustering technique is applied for the image segmentation process.In addition,Adagrad optimizer based Capsule Network(CapsNet)model is derived for effective feature extraction process.Lastly,crow search optimization(CSO)algorithm with sparse autoencoder(SAE)model is utilized for the melanoma classification process.The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance.A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects.The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.

Energy-Efficient Routing Using Novel Optimization with Tabu Techniques for Wireless Sensor Network

Wireless Sensor Network(WSN)consists of a group of limited energy source sensors that are installed in a particular region to collect data from the environment.Designing the energy-efficient data collection methods in largescale wireless sensor networks is considered to be a difficult area in the research.Sensor node clustering is a popular approach for WSN.Moreover,the sensor nodes are grouped to form clusters in a cluster-based WSN environment.The battery performance of the sensor nodes is likewise constrained.As a result,the energy efficiency of WSNs is critical.In specific,the energy usage is influenced by the loads on the sensor node as well as it ranges from the Base Station(BS).Therefore,energy efficiency and load balancing are very essential in WSN.In the proposed method,a novel Grey Wolf Improved Particle Swarm Optimization with Tabu Search Techniques(GW-IPSO-TS)was used.The selection of Cluster Heads(CHs)and routing path of every CH from the base station is enhanced by the proposed method.It provides the best routing path and increases the lifetime and energy efficiency of the network.End-to-end delay and packet loss rate have also been improved.The proposed GW-IPSO-TS method enhances the evaluation of alive nodes,dead nodes,network survival index,convergence rate,and standard deviation of sensor nodes.Compared to the existing algorithms,the proposed method outperforms better and improves the lifetime of the network.

Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and reliable.The RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy demands.Since energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power resources.Predicting energy consumption of RE and NRE sources using effective models becomes necessary.With this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power sources.Actual data may influence the prediction performance of the results in prediction approaches.The proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter optimization.In addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL models.The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Intelligent Deep Learning Model for Privacy Preserving IIoT on 6G Environment

In recent times,Industrial Internet of Things(IIoT)experiences a high risk of cyber attacks which needs to be resolved.Blockchain technology can be incorporated into IIoT system to help the entrepreneurs realize Industry 4.0 by overcoming such cyber attacks.Although blockchain-based IIoT network renders a significant support and meet the service requirements of next generation network,the performance arrived at,in existing studies still needs improvement.In this scenario,the current research paper develops a new Privacy-Preserving Blockchain with Deep Learning model for Industrial IoT(PPBDL-IIoT)on 6G environment.The proposed PPBDLIIoT technique aims at identifying the existence of intrusions in network.Further,PPBDL-IIoT technique also involves the design of Chaos Game Optimization(CGO)with Bidirectional Gated Recurrent Neural Network(BiGRNN)technique for both detection and classification of intrusions in the network.Besides,CGO technique is applied to fine tune the hyperparameters in BiGRNN model.CGO algorithm is applied to optimally adjust the learning rate,epoch count,and weight decay so as to considerably improve the intrusion detection performance of BiGRNN model.Moreover,Blockchain enabled Integrity Check(BEIC)scheme is also introduced to avoid the misrouting attacks that tamper the OpenFlow rules of SDN-based IIoT system.The performance of the proposed PPBDL-IIoT methodology was validated using Industrial Control System Cyber-attack(ICSCA)dataset and the outcomes were analysed under various measures.The experimental results highlight the supremacy of the presented PPBDL-IIoT technique than the recent state-of-the-art techniques with the higher accuracy of 91.50%.

A Reliable NLP Scheme for English Text Watermarking Based on Contents Interrelationship

In this paper,a combined approach CAZWNLP(a combined approach of zero-watermarking and natural language processing)has been developed for the tampering detection of English text exchanged through the Internet.The third gram of alphanumeric of the Markov model has been used with text-watermarking technologies to improve the performance and accuracy of tampering detection issues which are limited by the existing works reviewed in the literature of this study.The third-grade level of the Markov model has been used in this method as natural language processing technology to analyze an English text and extract the textual characteristics of the given contexts.Moreover,the extracted features have been utilized as watermark information and then validated with the attacked English text to detect any suspected tampering occurred on it.The embedding mechanism of CAZWNLP method will be achieved logically without effects or modifying the original text document to embed a watermark key.CAZWNLP has been implemented using VS code IDE with PHP.The experimental and simulation results using standard datasets of varying lengths show that the proposed approach can obtain high robustness and better detection accuracy of tampering common random insertion,reorder,and deletion attacks,e.g.,Comparison results with baseline approaches also show the advantages of the proposed approach.

Optimized Stacked Autoencoder for IoT Enabled Financial Crisis Prediction Model

Recently,Financial Technology(FinTech)has received more attention among financial sectors and researchers to derive effective solutions for any financial institution or firm.Financial crisis prediction(FCP)is an essential topic in business sector that finds it useful to identify the financial condition of a financial institution.At the same time,the development of the internet of things(IoT)has altered the mode of human interaction with the physical world.The IoT can be combined with the FCP model to examine the financial data from the users and perform decision making process.This paper presents a novel multi-objective squirrel search optimization algorithm with stacked autoencoder(MOSSA-SAE)model for FCP in IoT environment.The MOSSA-SAE model encompasses different subprocesses namely preprocessing,class imbalance handling,parameter tuning,and classification.Primarily,the MOSSA-SAE model allows the IoT devices such as smartphones,laptops,etc.,to collect the financial details of the users which are then transmitted to the cloud for further analysis.In addition,SMOTE technique is employed to handle class imbalance problems.The goal of MOSSA in SMOTE is to determine the oversampling rate and area of nearest neighbors of SMOTE.Besides,SAE model is utilized as a classification technique to determine the class label of the financial data.At the same time,the MOSSA is applied to appropriately select the‘weights’and‘bias’values of the SAE.An extensive experimental validation process is performed on the benchmark financial dataset and the results are examined under distinct aspects.The experimental values ensured the superior performance of the MOSSA-SAE model on the applied dataset.

Resource Assessment of Wind Energy Potential of Mokha in Yemen with Weibull Speed

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources in Yemen and other Arabic countries,and the absence of similar studies in the region.This study aims to examine the potential of wind energy in Mokha region.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a few wind turbines and determining the best.Weibull speed was verified as the closest to the average actual wind speed using the cube root,as this was verified using 3 criteria for performance analysis methods(R^(2)=0.9984,RMSE=0.0632,COE=1.028).The wind rose scheme was used to determine the appropriate direction for directing the wind turbines,the southerly direction was appropriate,as the winds blow from this direction for 227 days per year,and the average southerly wind velocity is 5.27 m/s at an altitude of 3 m.The turbine selected in this study has a tower height of 100m and a rated power of 3.45 MW.The capacitance factor was calculated for the three classes of wind turbines classified by the International Electrotechnical Commission(IEC)and compared,and the turbine of the first class was approved,and it is suitable for the study site,as it resists storms more than others.The daily and annual capacity of a single,first-class turbine has been assessed to meet the needs of 1,447 housing units in Mokha region.The amount of energy that could be supplied to each dwelling was around 19 kWh per day,which was adequate to power the basic loads in the home.

Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication technology.In this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed approach.The researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English text.Moreover,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any tampering.SFASCDW has been implemented using PHP with VS code IDE.The robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and deletion.The SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

Arabic Feature-Based Text Watermarking Technique for Sensitive Detecting Tampering Attack

In this article,a high-sensitive approach for detecting tampering attacks on transmitted Arabic-text over the Internet(HFDATAI)is proposed by integrating digital watermarking and hidden Markov model as a strategy for soft computing.The HFDATAI solution technically integrates and senses the watermark without modifying the original text.The alphanumeric mechanism order in the first stage focused on the Markov model key secret is incorporated into an automated,null-watermarking approach to enhance the proposed approach’s efficiency,accuracy,and intensity.The first-level order and alphanumeric Markov model technique have been used as a strategy for soft computing to analyze the text of the Arabic language.In addition,the features of the interrelationship among text contexts and characteristics of watermark information extraction that is used later validated for detecting any tampering of the Arabic-text attacked.The HFDATAI strategy was introduced based on PHP with included IDE of VS code.Experiments of four separate duration datasets in random sites illustrate the fragility,efficacy,and applicability of HFDATAI by using the three common tampering attacks i.e.,insertion,reorder,and deletion.The HFDATAI was found to be effective,applicable,and very sensitive for detecting any possible tampering on Arabic text.

An Optimized Algorithm for Resource Allocation for D2D in Heterogeneous Networks

With the emergence of 5G mobile multimedia services,end users’demand for high-speed,low-latency mobile communication network access is increasing.Among them,the device-to-device(D2D)communication is one of the considerable technology.In D2D communication,the data does not need to be relayed and forwarded by the base station,but under the control of the base station,a direct local link is allowed between two adjacent mobile devices.This flexible communicationmode reduces the processing bottlenecks and coverage blind spots of the base station,and can be widely used in dense user communication scenarios such as heterogeneous ultra-dense wireless networks.One of the important factors which affects the quality-of-service(QoS)of D2D communications is co-channel interference.In order to solve this problem of co-channel interference,this paper proposes a graph coloring based algorithm.The main idea is to utilize the weighted priority of spectrum resources and enables multiple D2D users to reuse the single cellular user resource.The proposed algorithm also provides simpler power control.The heterogeneous pattern of interference is determined using different types of interferences and UE and the priority of color is acquired.Simulation results show that the proposed algorithm effectively reduced the co-channel interference,power consumption and improved the system throughput as compared with existing algorithms.

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.

CryptoNight Mining Algorithm with YAC Consensus for Social Media Marketing Using Blockchain

Social media is a platform in which user can create,share and exchange the knowledge/information.Social media marketing is to identify the different consumer’s demands and engages them to create marketing resources.The popular social media platforms are Microsoft,Snapchat,Amazon,Flipkart,Google,eBay,Instagram,Facebook,Pin interest,and Twitter.The main aim of social media marketing deals with various business partners and build good relationship with millions of customers by satisfying their needs.Disruptive technology is replacing old approaches in the social media marketing to new technology-based marketing.However,this disruptive technology creates some issues like fake news,insecure,inconsistency,inaccuracy and so on.These issues contribute economic instability in the society,diminishing the level of trustworthy.To overcome these issues,this paper we present blockchain as disruptive technology for social media marketing.Blockchain plays a vital role on social media marketing by providing secure to the company page in the website.The properties of disruptive potential of blockchain on social media marketing is transparency,security,reliability and immutability.This paper presents a new framework for disruptive technology in blockchain social media marketing using fusion of CryptoNight mining algorithm with YAC consensus algorithm[BCDSMM-CNYAC].This mining algorithm provides high CPU efficiency,high dimensionality of secure and detecting falsifying data attack in the social media marketing.For the data analysis we proposed ANOVA analysis method regarding to the factors of age,time,frequency visiting times of social media platform.For reliability analysis of data Cronbach’s alpha tests are implemented.

Metaheuristic Based Data Gathering Scheme for Clustered UAVs in 6G Communication Network

The sixth-generation(6G)wireless communication networks are anticipated in integrating aerial,terrestrial,and maritime communication into a robust system to accomplish trustworthy,quick,and low latency needs.It enables to achieve maximum throughput and delay for several applications.Besides,the evolution of 6G leads to the design of unmanned aerial vehicles(UAVs)in providing inexpensive and effective solutions in various application areas such as healthcare,environment monitoring,and so on.In the UAV network,effective data collection with restricted energy capacity poses a major issue to achieving high quality network communication.It can be addressed by the use of clustering techniques forUAVs in 6G networks.In this aspect,this study develops a novel metaheuristic based energy efficient data gathering scheme for clustered unmanned aerial vehicles(MEEDG-CUAV).The proposed MEEDG-CUAV technique intends in partitioning the UAV networks into various clusters and assign a cluster head(CH)to reduce the overall energy utilization.Besides,the quantum chaotic butterfly optimization algorithm(QCBOA)with a fitness function is derived to choose CHs and construct clusters.The experimental validation of the MEEDG-CUAV technique occurs utilizing benchmark dataset and the experimental results highlighted the better performance over the other state of art techniques interms of different measures.

Design of Intelligent Alzheimer Disease Diagnosis Model on CIoT Environment

Presently,cognitive Internet of Things(CIoT)with cloud computing(CC)enabled intelligent healthcare models are developed,which enables communication with intelligent devices,sensor modules,and other stakeholders in the healthcare sector to avail effective decision making.On the other hand,Alzheimer disease(AD)is an advanced and degenerative illness which injures the brain cells,and its earlier detection is necessary for suitable interference by healthcare professional.In this aspect,this paper presents a new Oriented Features from Accelerated Segment Test(FAST)with Rotated Binary Robust Independent Elementary Features(BRIEF)Detector(ORB)with optimal artificial neural network(ORB-OANN)model for AD diagnosis and classification on the CIoT based smart healthcare system.For initial pre-processing,bilateral filtering(BLF)based noise removal and region of interest(RoI)detection processes are carried out.In addition,the ORBOANN model includes ORB based feature extractor and principal component analysis(PCA)based feature selector.Moreover,artificial neural network(ANN)model is utilized as a classifier and the parameters of the ANN are optimally chosen by the use of salp swarm algorithm(SSA).A comprehensive experimental analysis of the ORB-OANN model is carried out on the benchmark database and the obtained results pointed out the promising outcome of the ORB-OANN technique in terms of different measures.

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment.Technological revolution with Internet of Things(IoTs)has developed various applications in almost all domains from health care,education to entertainment with sensors and smart devices.One of the subsets of IoT is Internet of Medical things(IoMT)which connects medical devices,hardware and software applications through internet.IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data.With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data.This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT.The proposed Machine Learning based Block Chain Technology malware detection scheme(MLBCT-Mdetect)is implemented in three steps namely:feature extraction,Classification and blockchain.Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight.Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes.Furthermore,third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy.ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.

Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of pollution.Given the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra Island.This was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the best.The average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently available.The results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the year.The average wind speed in the five years was 7.95 m/s.The average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the year.This study provides useful information for developing wind energy and an efficient wind approach.

An Optimized Algorithm for D2D-MIMO 5G Wireless Networks

The device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G and beyond(B5G)communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as well.The device-to-device(D2D)networking technology is extended to the conventional cellular network to boost the communication efficiency of the entire network,forming a heterogeneous 5G communication network.D2D communication in a cellular cell will boost the efficiency of the spectrum,increase the ability of the device,and reduce the communication burden of base stations through the sharing of approved cell resources,causing serious interference as well.This paper proposes an efficient algorithm to minimize interference,based on the parity of the number of antennas,to resolve this issue.The primary concept is to generate the cellular connection precoding matrix by minimizing the power of interference from the base station to non-targeted receivers.Then through the criterion of maximum SINR,the interference suppression matrix of the cellular connection is obtained.Finally,by removing intra-interference through linear interference alignment,the maximum degree of freedom is obtained.The results of the simulation show that the proposed algorithm efficiently increases the performance of the spectrum,decreases interference,improves the degrees of freedom and energy efficiency compared to current algorithms.