International Journal of Intelligent Engineering and Systems

19

Authors: Ashraf Ali SK, Prasad M.V.D, Hima Bindu G, P. Praveen Kumar P, Anil Kumar D, Kishore P.V.V

Sign language recognition in real time has been leveraged by the continuously varying hand movements in both shape and orientation across Spatio-Temporal dimensions. This is accomplished by either independent view or shared view feature learning. However, information movement between views is neither total nor restricted to achieve view insensitivity during testing where all views are needed. The objective is to perform pose based multi view sign language recognition by applying triplet loss on a pair of specific and shared view features. Shared view features are obtained using view compatibility matrix which maps within class between view features and between class within view features. This mapping helps in increasing information flow between views from the same class and restricting it between classes thereby making highly discriminative feature representation for all views. Subsequently, metric learning enables to build a view invariant feature embedding by stacking view specific and shared features from different layers for training deep models. In the end, a blended view feature representation is obtained per class. Experiments were designed on our multi view skeletal sign language video dataset and three benchmark action datasets. The results of the experimentation have shown that the performance of the classifier has improved by 8% over the linear view combiners such as Laplacian eigenmaps. Further, the proposed model is useful in constructing a view invariant feature for recognition of multi view sign language.

48

Authors: Md. Irshad Hussain B., Mohamed Rafi

Now-a-days biometrics is used to recognize an individual, and is thought of a more secure way to authorize an individual. Face is the most important factor to recognize an individual. In order to enforce security features, many verification systems use face recognition to identify a person, such as - automobile security, security of home, smartphones owner recognition, attendance system, identification of criminals, etc. Face biometric works in two phases –face detection and face recognition. In this paper we are proposing a hybrid model that works in two phases, face detection is the first phase which includes two stages, in first stage it detects the face from the acquired image and then in the second stage acquired image is subjected to intensity equalization. The second phase is of Face recognition, where finally the individual is recognized as authenticated user or not. We are using Viola-Jones algorithm to detect the face, histogram stretching for intensity equalization and principal component analysis (PCA) algorithm for image recognition. The effectiveness of the suggested facial recognition system is shown through experimental findings using the face dataset constituting of 100 subjects using VTU-BEC-DB multimodal biometrics database with constraints of pose and illumination in the face images, without the intermediate step of intensity equalisation, we had a recognition rate of 93.60%. However, when the intermediate step of intensity equalisation was included, a novelty was added to the hybrid algorithm, and we achieved a recognition rate of 98.20%, which is higher recognition rate than the state-of-the-art methods.

75

Authors: D. Shankar, G. Victo Sudha George, N kanya

Security is considered one of the major challenges in the network due to the increase in network services. Many systems linked to the network play a major role in business and other applications that provide network services. Therefore, it is necessary to identify effective ways to secure the system. One of the significant information security technologies is the intrusion detection system (IDS) that utilizes various deep learning (DL) and machine learning (ML) algorithms to identify network issues. As a result, a novel DL technique is used to classify and detect attacks in the network. To perform this approach, pre-processing is initially done for the collected data using Z-score normalization (ZsN). Then, feature selection is performed using the hybrid pattern search whale optimization algorithm (HPS-WOA). Followed by, network attacks are detected and classified using the optimum bi-directional long short-term memory with the gated recurrent unit (OptiBiNet_GRU). Finally, the hyper parameters such as loss and weight of the proposed DL model are optimized using the pelican optimization algorithm (POA). The effectiveness of the proposed method is validated using two datasets on various performance metrics with some existing baselines. The results show that the proposed method outperforms other methods in terms of accuracy of 99%, precision of 95%, f1-score of 97%, specificity of 99%, and recall of 98% for UNSW-NB15 dataset, whereas accuracy of 99%, precision of 95%, f1-score by 99%, specificity of 99%, and recall of 99% for CICIDS 2017 dataset, respectively.

105

Authors: Gopalsamy Vanitha, Palaniswamy Amudha, Subramaniam Pillai Sivakumari

WSN has been widely used in many sensitive applications and it also has novel possibilities for laying the groundwork for using ubiquitous and pervasive computing, but it has also presented a number of issues and challenges, such as a dynamic network topology and a congestion problem that hinders not only network bandwidth utilisation but also performance. Proficient rate control and fair bandwidth allocation (PRC-FBA) was one of the schemes in the literature to solve issues of WSN by combining the ideas of traffic class priority and bandwidth fairness. However, because of the nature of WSN, the energy of nodes near the sink node is diminished when packets move from lowly congested nodes to highly congested nodes. This paper proposes a proficient rate control with data aggregation and fair bandwidth allocation (PRCDA-FBA) to address this problem by using an effective data aggregation approach for reducing the number of transmissions. In the proposed method, fair bandwidth allocation is simplified by an artificial intelligence-based bandwidth prediction method. Thus, PRCDA-FBA increases the network's durability. Despite having lower bandwidth utilizations, energy-critical sensor nodes require careful power management to avoid being eavesdropped upon. Along with data aggregation and fair bandwidth allocation, the effects of overhearing packets by energy-critical nodes are mitigated through network-wide route adjustments based on the energy level of nodes. Thus, in the proposed method, data aggregation is scheduled based on the availability of bandwidth, energy, queue size and packet priority. The proposed method is named as energy-aware proficient rate control with data aggregation and fair bandwidth allocation (EPRCDA-FBA). The proposed algorithms have been deployed on the Network Simulator 2.35 platform, and a comparative analysis has been performed using several metrics, including throughput, packet loss, End-to-End (E2E) delay and energy utilization. The EPRCDA-FBA method archives highest throughput which is 9.17%, 5.48%, 4.68% and 2.45% higher than congestion control strategies like discrete-time sliding mode congestion controller (DSMC), weighted priority based fair queue gradient rate control (WPFQGRC), PRC-FBA and rate adjustment-based congestion control (RACC).

126

Authors: Zuhair A. Ahmed, Safanah M. Raafat

Accurate localization and mapping are essential for autonomous navigation systems. The simultaneous localization and mapping (SLAM) algorithm, gmapping, is widely used for creating two-dimensional occupancy grid maps (2D-OGMs) due to its low cost and effectiveness in indoor environments. According to the SLAM interaction between localization and mapping terms, the evaluation of map accuracy implicitly means the evaluation of localization. This study focuses on improving the accuracy of the estimated 2D-OGMs by fine-tuning the 32 initialization parameters of gmapping using a turtlebot3-burger robot. An improved experimental procedure was developed, incorporating image registration and similarity measurement to evaluate the map accuracy. The results show a substantial improvement in map accuracy, from 78.84% to 94.18%. The study highlights the importance of fine-tuning the SLAM algorithm for improved map accuracy and provides valuable insights for developing autonomous navigation systems. The key contribution of this paper lies in the systematic classification, fine-tuning, and evaluation of the Gmapping initialization parameters.

151

Authors: Ujjwala S Rawandale, Sanjay R. Ganorkar, Mahesh T. Kolte

In recent days, one of the most serious health problems in humans is hearing loss. For helping the hearing impairment, the Hearing Aid (HA) system is used, but it may lack in various aspects such as delay, matching error, power consumption, low complexity and unwanted noise. Many techniques were introduced to overcome these issues, but they still have problems. Therefore, this research uses a novel Aquila-based Adaptive Filter (AbAF) for improving the HA system with the optimized parameters of filter. Here, the incorporation of the Aquila optimizer along with the adaptive filter is considered as the key novelty of this present work. The main motive of this proposed model is to filter the noisy features more possible range for improve the HA performance. Hence, the Aquila agent removes the unwanted noise by updating the adaptive processor's filter coefficient and optimizing the filter parameters such as matching error, delay, complexity, and bandwidth and power consumption. Moreover, the suggested model is designed and executed in the MATLAB platform. Finally, the performance parameters were estimated and compared with the past studies to validate the performance improvement. The presented model scored the reduced Matching error as 0.3% and reduced power consumption as 10%, which quite lower than the compared models.

175

Authors: Ahmed R. Kadhim, Furkan Rabee

Cloud computing has become an essential technology in many industries due to its scalability and cost-effectiveness. The dynamic nature of cloud computing, including elasticity, on-demand provisioning, diverse resource types, and varied pricing model, presents a significant challenge in scheduling tasks for cloud-based systems, especially when considering user quality of service (QoS) constraints such as deadlines and budgets. Therefore, in order to optimize the performance of the cloud systems and end-user satisfaction, an efficient budget and deadline-aware scheduling model is necessary. Game theory provides a framework for modeling and analyzing the strategic interactions between self-interested entities, which makes it an ideal tool for task scheduling in cloud computing. Additionally, the versatility of game models enables the analysis of various cloud computing architectures. This paper proposes a dynamic Stackelberg (leader-follower) game model for modeling the interactions between tasks, scheduler, and cloud resources to find an equilibrium for the game under both budget and deadline constraints. The proposed dynamic task scheduling based on Stackelberg game (DTSSG) model is assisted by the pricing model and satisfaction factors to select the optimal virtual machine for processing the user task. To achieve high average resource utilization, the utilization factor of the cloud resources is considered in the proposed work. Experimental results show that the Stackelberg model equilibrium has been very effective in scheduling the user tasks across the data center resources by selecting the optimal virtual machines. The results demonstrate improved execution efficiency in terms of decreased makespan by 30%, reduced number of deadline violations by 52%, decreased total gain cost by 27.13% and increased provider profit by 19.15 % on average as compared to existing deadline budget scheduling (DBS), genetic algorithm, and MAX–MIN methods. Also, the results show the effectiveness of the proposed work in terms of increased throughput by 59.4 % and decreased makespan by 27.95% using Google cloud jobs dataset (GoCJ) as compared to existing gradient-based optimization (GBO), multi-verse optimizer (MVO), enhanced multi-verse optimizer (EMVO).

201

Authors: Abbas M. Ali Al-muqarm, Naseer Ali Hussien

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile crowd sensing (MCS) applications like sensors, and smartphones requires processing power and rapid responses in a specific time frame (deadline), and user cost, i.e., budget. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important is task scheduling. Nevertheless, the most of earlier scheduling algorithms have focused on only one evaluation parameter such as makespan or cost and so on, as the scheduling process goal. Optimizing a single metric may not ensure an improvement in the cloud performance in general, so the need may arise for algorithms that concentrate more broadly on enhancing more than one parameter. Therefore, this paper presents a deadline-budget multi-objective dynamic scheduling scheme (DB-MODS) to execute user tasks on VMs within QoS limits in order to finish the task within the deadline and budget if possible. DB-MODS using K-Means based on task length and deadline for clustering incoming tasks into groups and categories VMs based on capacity thresholds. In addition, the task belonging to every cluster is assigned to a suitable VM in VMs groups. based on objective functions, which can be one for user request depending on (deadline and budget) and the second is for system depending on (execution time and cost). This paper used CloudSim plus to simulate and evaluate our approach. The DB-MODS approach performance was compared to scheduling algorithms from the previous literature. Both random and Google cloud jobs (GOCJ) workloads are used to evaluate the efficiency of DB-MODS. The results show that DB-MODS outperforms the other algorithms by minimizing makespan by 41%, energy by 46%, and increasing success ratio by 35%, in comparison to existing load balancing and scheduling methods: EEVS, LAS, Greedy-R, DTS, Random, and Greedy-P in the first scenario. Minimizing makespan by 46%, cost by 44%, and maximize throughput by 80% when compared with MOCS, Max-Min, HABC-LJF, FCFS, MOPSO, Q-learning, MOABCQ-FCFS, and MOABCQ-LJF in a second scenario, and minimizing makespan by 46%, and increase throughput by 39% when compared with HESGA, GA, and ACO in a third scenario in average. In addition to the possibility of applying and employing DB-MODS for scheduling deadline-critical tasks in a heterogeneous cloud system.

231

Authors: Mohamed Cherradi, Anass El Haddadi

The evolution of the huge amount of heterogeneous data sources introduces the emergence of several fresh new concepts. Among one of the most well-known concepts emerging as a recent and trending topic in big data is the data lake. Which represents a central repository that stores heterogeneous data sources in its native format without any predefined schema. In the absence of any enforced schema, effective metadata management based on metadata models remains an active research topic to address the data lake issues knowing by a data swamp. However, the examination of existing metadata models shows that none of them proposes a complete model. In this article, we propose a generic and extensible metadata model that supports high flexibility and better scalability in the integration of metadata. With these capabilities, EMEMODL enables comprehensive metadata management for data lakes. We show EMEMODL’s feasibility through a prototypical implementation based on TPC-H datasets of different sizes to prove the scaling feature, using MySQL and Neo4J. The findings of these experiments revealed encouraging results for big data queries that give us a graph database, which shows an efficient ability to manage and process large amounts of data compared to a relational database in terms of retrieval time queries and resource consumption, including cpu and memory usage.

258

Authors: Feby Artwodini Muqtadiroh, Tsuyoshi Usagawa, Riris Diana Rachmayanti, Supeno Mardi Susiki Nugroho, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

Classification of uncertain conditions requires computational modeling to obtain exact non-vague results for making the right decision, such as opening and closing school cases during a pandemic. We cannot rely solely on normative and textual government regulations because of numerous constraints and uncertainty in implementation. Unsupervised classification techniques can deal with such issues without needing prior references that contain definitive hesitancy. This motivates us to use a fuzzy system based on knowledge-based composition rules for complex problems such as the dynamics of COVID-19 because of its ability to adapt to changes and uncertainties. Therefore, we construct rules based on knowledge about COVID-19 to the issue of opening/closing schools using three fuzzy approaches: conventional fuzzy, intuitionistic fuzzy system (IFS), and fuzzy c-means (FCM). We can demonstrate a correlation between the number of school openings and the COVID-19 dynamics by utilizing the fuzzy approach to reduce the degree of hesitance. Experiments on available public time-series datasets demonstrate that the IFS is more efficient in forming rigidly distinct two classes. The results indicate that the accuracy of IFS is 99.47%, FCM is 91.28, and conventional FS is 84.33%, including the IFS silhouette score, which is higher than the others, at 0.91 or closer to 1, indicating excellent classification results. IFS is less superior in running time, while FCM is the fastest. This is because there are multiple stages in the IFS by considering non-membership functions.

280

Authors: Mohammad Osman, Kaleem Fatima, P. Naveen Kumar

Security and privacy are the major concerns in the internet of things (IoT) which are uncertain and unpredictable. Trust aware routing is one of the recent and effective strategies which ensure better resilience for IoT nodes from different security threats. Towards such concern, this paper proposes a new strategy called independent onlooker withstanding trust aware routing (IOWTAR) for IoT. IOWTAR introduced a new compound trust metric by combining three individual metrics namely independent trust, onlooker trust, and withstanding trust (a combination of resilient trust and immovability trust). Independent trust and onlooker trust are assessed based on direct and indirect experiences of nodes about their neighbor nodes. Withstanding trust is assessed based on the stability and resilience of nodes towards dynamic topological changes and network failures respectively. Further, this work adapted the Firefly algorithm (FFA) to optimize the weights of individual trusts and establishes a secure path. Simulation experiments carried out over the proposed method had shown its superiority in terms of packet delivery ratio, delay, and throughput. The proposed method has gained an average improvement in the throughput is of 23.71%, 20.18%, 17.27%, and 2.88% from PSO, GSA, WOA, and CBBMOR-TSM-IOT methods respectively.

302

Authors: Nadia A. Khalaf, Hazim G. Daway, Baida M Ahmed

Improvements in aerial images have an important role, given the increase in dust storms due to global warming and climatic changes. Apart from improving aerial images, image enhancement is important in many applications, such as tracking and monitoring studies during wars and for the environment. However, aerial images often suffer from a lack of contrast due to dust and pollutants. In this study, we propose a perceptual dark channel prior (PDCP). In this method, the light value (Y) is improved by using stretch and perceptual color space, and then the chromatic compounds (CbCr) are improved by dark channel prior (DCP). The quality measures lightness order error (LOE), entropy, and color-fullness metrics (CM) and then compares these with the improved methods (DCPMV, YCBCR, MLA, SEA, DCP, and IEIF) to determine the method efficiency. Results show that the proposed PDCP effectively enhances the aerial images better than other methods, because owning it the average are LOE (194.096), EN (7.361), and CM (39.403).

322

Authors: Ernando Rizki Dalimunthe, Erwin Susanto, Kharisma Bani Adam

The most frequently used maximum power point tracking (MPPT) technique is the perturb & observe (P&O) algorithm as a power tracking tool in PV system. The P&O algorithm is easy to compute and implement, but the algorithm is prone to oscillations at the maximum power point. Hence, the power becomes inaccurate due to a lot of power loss. This study utilizes the deep q- network (DQN) algorithm to improve P&O performance by correcting the output value algorithm using various step sizes by DQN. The proposed method increased the tracking speed when receiving the same value at different times by 33.3%–50%, and the oscillation rate was successfully reduced by 73.99%–83.5%. The advantages of increasing tracking speed and decreasing oscillation rate are accompanied by tracked power with averages of 95%, which is better than the P&O and DQN algorithms. It shows that the proposed method can work optimally regarding efficiency and oscillation rate and be the fastest in tracking maximum power from previous related works.

345

Authors: Usman Ependi, Adian Fatchur Rochim, Adi Wibowo

This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority over-sampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

378

Authors: P. Muthukumar, M. V. Ramesh, Ponnam Venkata Kishore Babu, P. Rohinikumar, S. V. Satyanarayana

Most electrical distribution systems (EDS) are radially structured so that primary and secondary protection control devices can work together well. Because they have a high R/X ratio in their design, they also have a bad voltage profile, high distribution losses, and less stability margins. As a result, integrating reactive power compensation devices like the distribution-static VAr compensator (D-SVC) can solve these issues. Yet, the situation can get worse if D-SVCs are placed and rated incorrectly in radial distribution networks (RDNs). Determining the optimum locations and their ratings simultaneously requires an updated version of the Firefly algorithm (FA) with adaptive parameters, which is introduced in this paper as the adaptive firefly algorithm (AFA). The multi-objective function that has been presented relates to improving loadability, voltage stability, and reducing active power loss. On the IEEE 69-bus, simulations are run for three different VAr compensation levels. In comparison to the base case, the losses are reduced by 34.04% and 33.14% with 50% and 75% VAr compensation, respectively. But for the optimal VAr compensation of 73.14 percent by AFA, the losses are reduced by 35.29 percent, which is higher than both under and over compensation cases. Similarly, the loadability margin is increased to 3.099 p.u. with optimal VAr compensation, but it is observed as only 2.833 p.u. and 2.939 p.u. with 50% and 75% VAr compensation, respectively. On the other hand, the findings produced with APF demonstrate its efficiency for resolving complex optimization issues and outperform those obtained with previous research. Also, the proposed D-SVCs allocation has improved RDN's overall performance, demonstrating how well it adapts to real-time applications.

405

Authors: Vivek Kadam, Surendra Bhosale

Inflated communication traffic and expanding radar applications result in frequency congestion and require extra spectrum resources. Dual-functioning multi input multi output radar benefits both sensing and communication operations via real-time cooperation. This decongests the radio frequency environment and allows a single hardware platform for both functionalities. Analog beamforming fails to provide multiplexing gain and digital beamforming is not preferred practically because of heavy power consumption. A reliable hybrid beamforming design is an inherent requirement due to the concerns related to limited RF chains, interference and the cost of fully-digital beamforming. In this paper, Enhanced-Social Ski Driver’s algorithm is presented to investigate the performance of hybrid beamforming in dual-functioning MIMO radar. This study investigates location-based direction sensing for Rayleigh and Rician fading channels. This amendment provides improved performance with a minimal bit error rate, maximum gain, and significant normalized array power distribution. The simulation results show 24% reduction in bit error rate, 15% improvement in gain and persistent array power than existing social ski driver’s algorithm.

428

Authors: Asif Abdullah, Omkar S Powar, Krishnan Chemmangat

Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively.

455

Authors: Md. Irshad Hussain B., Mohamed Rafi

In India, online digital transaction has become prominent means of financial transaction in everyone’s day-to-day life as per reserve bank of India's (RBI) reports. Presently, Indian banks protect users' online transactions with only username and password, CAPTCHA, and dynamic 6 or 8-digit one time passwords (OTPs), which are not very secure. According to literature, this straightforward method of authentication is easily vulnerable to numerous threats. The time offered to enter the OTP with numerical pattern is more than 100 seconds, which is in defiance of RBI guidelines and can be used by fraudsters to commit fraud. In this paper we are suggesting a novel 3-Level authentication which is in-line with RBI authentication guidelines along with the authenticator assurance standards of National Institute of Standards and Technology of the United States and the European regulation for electronic payment services, for securing the users online transactions aided by username and password, OTPs and biometrics. At Level-3 user is verified with biometrics or by OTP. For biometric authentication, we have chosen facial trait, a hybrid algorithm for face detection and recognition is designed, implemented and tested on a standard dataset of VTU-BEC-DB. Suitable OTP and time constraints were identified with the implementation of proposed algorithm being tested by conducting series of trials involving 200 participants with age group of 22 to 55 years. Based on online-survey, we are suggesting for the use of fall-back authentication technique powered by security question, in-case the authentication is only on the basis of OTP. We have achieved 98.20% recognition success rate for facial recognition. The significant time to enter dynamic 6 digit OTP and dynamic 8 character alpha-numeric OTP with time limits of 45 and 60 at Level 2 and 3 is 33.821.10 and 45.310.92, respectively.

482

Authors: Purba Daru Kusuma, Ashri Dinimaharawati

A new metaheuristic can be developed by constructing from scratch, modifying the existing metaheuristics, or hybridizing some metaheuristics. This work presents a new metaheuristic: extended stochastic coati optimizer (ESCO). ESCO is developed by expanding the shortcoming coati optimization algorithm (COA). ESCO expands the number of searches and references used in COA. ESCO also implements a stochastic process for each unit to choose the searches that will perform. It differs from COA, which splits the population into two fixed groups, each performing its strategy. ESCO implements three sequential phases in every iteration. Two options can be chosen in every phase. ESCO has three references in its guided search: the global best unit, a randomly selected unit, and a randomized unit within the search space. In this work, ESCO is challenged to solve 23 classic functions and benchmarked with five shortcoming metaheuristics: guided pelican algorithm (GPA), puzzle optimization algorithm (POA), average subtraction-based optimizer (ASBO), and coati optimization algorithm (COA). The result presents the superiority of ESCO among five shortcoming metaheuristics by outperforming the GPA, POA, GSO, ASBO, and COA in solving 13, 21, 23, 16, and 13 functions, respectively. Through investigation, the multiple search approach is more effective than the single search approach.

509

Authors: Seema Swamy Gowda, Ambika Ramalingappa

Underwater wireless sensor networks (UWSNs) have a huge amount of sensors located underwater to collect data from the underwater scenario. UWSN is considered a promising method for monitoring and exploring an underwater scenario. Energy-efficient and reliable data broadcasting are considered challenging tasks, because of the limited energy source of sensors. To address this issue, an energy-efficient cluster head (CH) selection and multi-hop routing are developed in UWSN. The multi objective energy based improved jellyfish swarm optimization (MOEIJSO) is proposed to select optimal CHs from normal sensors. The input parameters of MOEIJSO are residual energy, neighbor node distance, sink distance, and CH balancing factor. Next, multi-hop routing is developed by using ant colony optimization (ACO) for delivering the data packets. Therefore, the proposed MOEIJSO-ACO method is used to improve energy efficiency while increasing data delivery. The proposed MOEIJSO-ACO method is evaluated by using the alive and dead nodes, total energy consumption, data received in base station (BS), and life expectancy. The existing researches such as metaheuristics-based clustering with a routing (MCR) for UWSN, modified low energy adaptive clustering hierarchy (MLEACH) and cooperative energy-efficient routing (CEER) approach are used to compare the MOEIJSO-ACO method. The alive nodes of the MOEIJSO-ACO are 399 for 800 rounds, which is high when compared to the MCR-UWSN.

532

Authors: Shalini Sharma, Syed Zeeshan Hussain

A mobile Ad Hoc network (MANET) is a dynamic wireless network developed using wireless nodes without using any infrastructures. The wireless nature of mobile networks causes several challenges in secured communication which makes it susceptible to malicious attacks. Therefore, security is a very essential factor in facilitating a secured message transmission among mobile nodes in the wireless medium. The energy and trust aware - multi objective african vultures optimization (ETA-MAVO) method is used in this paper to provide high security for the data packets, thereby preventing malicious attacks. Using the ETA-MAVO algorithm, the network nodes' energy efficiency and trust value are calculated. For choosing the best cluster heads (CHs), ETA-MAVO considers fitness functions, namely: trust value, energy ratio, communication cost, the total number of gateway hops, and network load. To discover malicious nodes in the network and minimize packet loss, the trust value of a node is calculated from its neighbours. Because of trust value, updating routing information is simpler and increases network throughput. The results obtained from the simulation show that the proposed ETA-MAVO achieved better performance with respect to parameters such as delay, energy consumption, throughput, and detection rate when compared with the existing cat slap single-play algorithm (C-SSA) and data-driven zone based routing protocol (DD-ZRP), angular based energy proficient trusted routing (ACEPTR) protocol and aquila optimizer (AO) and puzzle optimization algorithm (POA). The proposed method achieved a detection rate of 93% while the existing C-SSA, DD-ZRP, ACEPTR, AO and POA achieved a detection rate of 90%, 68%, 72%, 91% and 90% respectively.

552

Authors: R. Sridevi, V. Sinthu Janita Prakash

Clustering is one of the important functionalities of networking, which has a dominant impact on the performance in terms of Throughput, packet delivery ratio and communication delays. The challenge in performing the clustering process is more challenging for heterogeneous networks. The divergence between the member nodes is highly variable for a network that contains IoT nodes. As the result of vast availability of different IoT hardware, organizing them under one roof is a critical task which is very vital too. This work is intended to develop a new hierarchical based clustering to improve the performance and the lifetime of the IoT / Wireless sensor network nodes. Three legacy functional blocks such as idiosyncratic fuzzy energy estimator, cumulative fuzzy energy hierarchy builder and energy based cluster head exchanger are integrated in this work and submitted here as “Idiosyncratic Fuzzy Low-Energy Adaptive Clustering Hierarchy (IFLEACH)”. An industrial standard network simulator is used to evaluate the performance of IFLEACH in terms of throughput, packet delivery rate, latency, end-to-end delay and energy consumption by simulating the real-worlds IoT network environment replica. An achievement around 11 kbps throughput and 4 % security level are obtained by the proposed method in high density network.

579

Authors: Meeradevi, Monica R. Mundada

Agriculture is been majorly contributing to the country’s GDP. So, it is foremost important to improve the production quality and quantity to improve the agricultural economy. Using modern technologies like IoT, big data, blockchain, robotics, 5G technologies, etc., farming can be improved by replacing humans in agricultural operations. The proposed work focuses on forecasting of yield and prices of agro-products which will be useful for farmers to increase their productivity and which in-turn increase their economy. Data science techniques can be used to perform this by predicting which crop can be grown for given environmental features and what is the outcome before harvesting period. The proposed model uses computational data-driven approach for crop yield prediction and price forecasting of agro-products. The study uses hybrid approach for crop yield prediction which integrates LSTM and genetic algorithm based evolutionary algorithm known as enhanced long short-term memory (ELSTM). The weight assignment plays a major role in learning the behaviour of the network. The gradient value with respect to weight is calculated for each epoch, and weight is updated to compute the new weight of the network to minimize error. The model accurately predicts the crop yield for the dataset which comprises of features like soil data, rainfall, history of production, fertilizers etc., the accuracy of the model is measured using metric root mean square error (RMSE) and mean absolute error (MAE). In the proposed study, the computation model is used to enhance the knowledge about agricultural yield before the crop sowing period. The proposed model will help farmers and government agencies to improve production. The proposed ELSTM model is compared with other machine learning models such as naïve bayes, decision tree, which shows accuracy between 75% to 80% and the experimental result show the proposed ELSTM model is performing better with 85% accuracy. The price forecasting model is necessary to identify the commodity prices well in advance. The agro-product price forecasting is made using auto-regressive integrated moving average (ARIMA) which uses dataset collected from various agricultural produce market committee (APMC). Auto correlation function (ACF) and partial auto correlation function (PACF) time series plots have been used to assess the proposed model's performance in predicting prices of crops.

604

Authors: Nelli Sreevidya, Penamakuru Siva Jyothi, Gaddam Sumalatha, Shanthi Pannala

Breast cancer is considered one of the main reasons for the mortality of females around the world, therefore the recognition and classification of the initial stage of breast cancer are essential to help the patients to perform the required action. However, the precise classification of breast cancer is a difficult task in medical imaging because of the complexity of breast tissues. In this paper, Otsu's thresholding based segmentation (OTS) is used for segmenting the cancer regions from the images of mammographic image analysis society (MIAS) dataset. Subsequently, feature extraction using Alexnet is done for extracting the features. The feature selection using multi-objective improved ant colony optimization (MIACO) is proposed in this paper to select the optimal features from the overall features acquired using Alexnet. The MIACO is mainly selects the features based on correlation coefficient to choose the features with higher correlation. Further, the stacked autoencoder (SAE) is used to perform an effective breast cancer classification using the features selected by MIACO. The performance of MIACO-SAE method is analyzed by means of accuracy, precision, recall, characteristic stability index (CSI) and F-measure. The existing research namely threshold-based and trainable segmentation (TTS) with machine learning (ML), infinite feature selection with genetic algorithm (IFS-GA) and deep convolutional neural networks (DCNN) are used to evaluate the MIACO-SAE method. The classification accuracy of MIACO-SAE is 99.36%, which is high when compared to the TTS-ML, IFS-GA and DCNN.

625

Authors: Mahmoud Zaki Iskandarani

The objective is to investigate effect of slot probability and its effectiveness in vehicular communication. The work carries out MATLAB simulation covering three different levels of slot probability; low, medium, and high. The goal behind such simulation is to uncover the process that leads to high reduction in throughput. The normal explanation for the reduction is high levels of collisions and re-transmission of data through new attempts. However, this work shows that such process can be characterized and controlled as the work shows through sinusoidal regression, the oscillatory behavior caused by collisions, attempts, retransmission, as a function of slot probability. The simulation results, which covers 20 vehicles at various slot probability levels, shows that there is a change in the data traffic-throughput shape function. This is due to the effect of slot probability and reflects on the level of obtained oscillations. The work also discovered an oscillation pattern that infiltrate and affect communication channels at different levels as a function of collisions and data delivery attempts, which is related to slot probability and data traffic. Such oscillatory pattern should be investigated further and the conditions for such pattern should be eliminated, in order to achieve better utilization of communication channel and higher throughput, with lower attempts and less collisions. This is also related to both slot probability and number of available slots. The obtained results in this work showed that at the upper part of the mid slot probability range and all the way to through the high slot probability part, a noticeable oscillatory behaviour is observed, with almost constant values of average data traffic and throughput values. These effects are accompanied by large acceleration and accumulation of collisions. Thus such new approach, and in conjunction with NP-CSMA, can be used at both the design stage of communication channels and vehicular networks, and during operations. This will enable adaptive slot allocation and dynamic transmission of messages, which will result in collisions reduction and better channel utilization.

654

Authors: Ali H. Mhmood, Hazem I. Ali, Amer B. Rakan, Mohammed R. Subhi, Yaseen Kh. Yaseen, Hameed A. Mohammed

This study proposes a new approach for controlling COVID-19 through vaccination, where an adequately descriptive mathematical model is created for COVID-19 using the susceptible exposed infectious recovered (SEIR) model of epidemic diseases. The presented control approach is synthesized using a combination of feedback linearization and H_∞ control, and incorporates model reference control to achieve optimal time responses with the aid of the black hole optimization (BHO) algorithm. The effectiveness of the designed control law is evaluated using data from the lombardy region of Italy. The results of the simulation show that the proposed control approach is able to effectively control the COVID-19 outbreak by accurately implementing the desirable vaccination, while effectively addressing nonlinearity and uncertainty in the COVID-19 system with a desirable control action. The control method has achieved the required immunity of 6.6 million individuals after approximately 25 days with a transmission rate reduced to zero in a short time, and a vaccination rate of 170 thousand people per day.

677

Authors: Karnam Sreenu, Sreelatha Malempati

From the past few decades, the cloud computing has been arisen as an extensively used platform to provide storage, compute and analytics services to organizations and end users on the basis of pay-as-you-use. This enabled the organizations as well as individuals to access the larger set of resources without establishing a costly and high performance computing platform. However, the major issue in cloud computing is the task scheduling which is declared as NP-hard problem and most of the researchers applied meta-heuristic algorithms to solve it. However, they experienced a slower convergence speed which has a direct impact on the efficiency of cloud computing environment. To achieve a faster convergence along with efficient quality of Service, this paper proposes a simple and effective task scheduling mechanism based on multiple resource attributes and conditional logic. This method considers totally four resource attributes such as Resource reaction time resource location, resource availability and resource reliability rate. Based on these four attributes, the proposed method constructs an index called as task scheduling index (TSI) and assigns tasks for resources based on their TSI value. The TSI is constructed through the proposed conditional logic and high priority is given for resources those have higher TSI. For experimental validation, we used two benchmark datasets such as GOCJ and synthetic dataset. Three performance metrics namely Makespan, Throughput and convergence speed are measured and compared with state-of-the-art methods like PSO, GA and GWO. On an average, the reduced Makespan of proposed method is 48.22% and 46.87% for GOCJ and Synthetic datasets respectively.

709

Authors: Muntasser A. Wahsh, Zainab M. Hussain

This paper presents a novel method for optimizing rectangular boundaries in stitched images using a combination of deep learning and genetic algorithms. The proposed method utilizes an enhanced structure of convolutional neural network (CNN) in order to extract features for residual regression and a genetic algorithm to tune CNN parameters. The performance of the proposed method was evaluated using various metrics, including mean squared error, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Fréchet inception distance (FID). The outcomes from the conducted experiment demonstrated that the suggested method achieved high accuracy in rectangular boundary optimization, even for images with complex shapes and orientations. The method was also tested with different parameters, and results indicated that increasing the number of generations and population size led to improved performance. Also, the combination of deep learning algorithm specifically (CNN) and optimization algorithm specifically (genetic algorithm (GA)) led to an increase in the accuracy of the rectified images where the average PSNR reached 23.98, SSIM is 0.8066, and FID is 18.72. The proposed method has potential applications in various areas of study, such as image cropping, object detection, and image segmentation.