Mohd Suhaimi Sulaiman, Mohamad Faizal Abd Rahman, Aileen Farida Mohd Adam,

Water pollution is one of the most serious environmental problems in Malaysia. The most notable occurrence of pollution happened in Selangor. Currently, there are various water quality monitoring (WQM) methods to observe the quality of water. One of the methods used is the internet of things (IoT) for wireless sensor network technology to obtain real-time data measurement. In this study, the developed WQM system is equipped with a sensor that can measure total dissolved solid (TDS) and electrical conductivity (EC). Arduino UNO was used in this system as a microcontroller to interact with the sensor. The Wi-Fi module, ESP8266, was used to transfer the collected data to ThingSpeak, which acts as a cloud to store all the data. The results showed that both sample populations can be discriminated since the p-value is greater than 0.05 in the normality test, while in the paired sample t-test, the p-value is less than 0.05. In conclusion, this research provides an easier way to monitor water quality by taking up less time at less cost, as well as being reliable in giving real-time data reading.

Suha Suliman Mardan, Mounir Taha Hamood,

This paper presents an efficient fast Walsh–Hadamard–Hartley transform (FWHT) algorithm that incorporates the computation of the Walsh-Hadamard transform (WHT) with the discrete Hartley transform (DHT) into an orthogonal, unitary single fast transform possesses the block diagonal structure. The proposed algorithm is implemented in an integrated butterfly structure utilizing the sparse matrices factorization approach and the Kronecker (tensor) product technique, which proved a valuable and fast tool for developing and analyzing the proposed algorithm. The proposed approach was distinguished by ease of implementation and reduced computational complexity compared to previous algorithms, which were based on the concatenation of WHT and FHT by saving up to 3N-4 of real multiplication and 7.5N-10 of real addition.

Sheeba MaryJohn Rukmony, Suganthi Gnanamony,

The quick development of innovations and increasing use of the internet of things (IoT) in human life brings numerous challenges. It is because of the absence of adequate capacity resources and tremendous volumes of IoT information. This can be resolved by a cloud-based architecture. Consequently, a progression of challenging security and privacy concerns has emerged in the cloud based IoT context. In this paper, a novel approach to providing security in cloud based IoT environments is proposed. This approach mainly depends on the working of rough set rules for guaranteeing security during data sharing (rough set method-cloud IoT [RSM-CIoTD]). The proposed RSM-CIoTD conspire guarantees secure communication between the user and cloud service provider (CSP) in a cloud based IoT. To manage unauthorized users, an RSM-CIoTD scheme utilizes a registered authority which plays out a two-degree confirmation between the network substances. The security and privacy appraisal techniques utilize minimum and maximum trust benefits of past communication. The experiments show that our proposed system can productively and safely store the cloud service while outperforming other security methods.

Ashwan Abdulmunem, Noor Al-Shakarchy, Mais Safoq,

During the coronavirus disease 2019 (COVID-19) pandemic, monitoring for wearing masks obtains a crucial attention due to the effect of wearing masks to prevent the spread of coronavirus. This work introduces two deep learning models, the former based on pre-trained convolutional neural network (CNN) which called MobileNetv2, and the latter is a new CNN architecture. These two models have been used to detect masked face with three classes (correct, not correct, and no mask). The experiments conducted on benchmark dataset which is face mask detection dataset from Kaggle. Moreover, the comparison between two models is driven to evaluate the results of these two proposed models.

Shahirah Mohamed Hatim, Haryani Haron, Shamsul Jamel Elias, Nor Shahniza Kamal Bashah,

The innovative idea presented in this research is that advancements in automotive networks and embedded devices can be used to assess the impact of congestion control on throughput and packet delivery ratio (PDR), or so-called multimedia content delivery. Vehicle networking and the distribution of multimedia content have become essential factors in getting packets to their intended recipients due to the availability of bandwidth. Vehicle-to-infrastructure (V2I) communication systems are crucial in vehicular ad hoc networks (VANETs), which permit vehicles to connect by distributing and delivering traffic data and transmission packet schemes. High levels of mobility and changing network topology necessitate dispersed monitoring and execution for congestion control. The amount of traffic congestion for packet transfers could be reduced by enhancing congestion management in terms of throughput and PDR percentages. In a highway setting, the Taguchi approach has been used to optimize the parameters for congestion control. Based on throughput and PDR performance measures, this technique minimizes superfluous traffic information and lowers the likelihood of network congestion. The simulation results have shown that the proposed approach performs better since it increases network performance while effectively utilizing bandwidth. The effectiveness of the suggested technique is evaluated using a typical VANETs scenario for V2I communication while driving on a highway.

Md. Azad Hossain, Muhammad Asad Rahman, Abu Hena Murshed, Eisuke Nishiyama, Ichihiko Toyoda,

This paper proposes a simple microstrip patch antenna (MPA) that can reconfigure its polarization states from linear to circular polarization in real-time by means of a PIN diode. An antenna is fed by a 50 Ω coaxial cable through the substrate of Teflon with relative permittivity of 2.15. The proposed antenna possesses a simple patch with a one-sided corner truncated to achieve polarization reconfigurability. A PIN diode is loaded to connect the main patch with a truncated corner and further maintain dual polarization states such as linear polarization (LP) and circular polarization (CP). Advanced design system (ADS) was used as a simulator to simulate the antenna, and a good understanding was obtained between simulated and measured results. Measured results showed a good agreement with simulated results at all working frequencies of interest. It shows minimum reflection coefficient gain with -10 dB scattering bandwidth 100 MHz for LP states and 170 MHz for CP states. It also shows an axial ratio of 1.56 dB for CP, and the cross-polarization level is also in a satisfying range.

Muhammad A. Shihab, Wafaa M. Taha, Raad A. Hameed, Ali Jameel, Ibrahim Mohammed Sulaiman,

There are various linear and nonlinear one-dimensional partial differential equations that are the focus of this research. There are a large number of these equations that cannot be solved analytically or precisely. The evaluation of nonlinear partial differential equations, even if analytical solutions exist, may be problematic. Therefore, it may be necessary to use approximate analytical methodologies to solve these issues. As a result, a more effective and accurate approach must be investigated and analysed. It is shown in this study that the Lagrange multiplier may be used to get an ideal value for parameters in a functional form and then used to construct an iterative series solution. Linear and nonlinear partial differential equations may both be solved using the variational iteration method (VIM) method, thanks to its high computing power and high efficiency. Decoding and analysing possible Korteweg-De-Vries, Benjamin, and Airy equations demonstrates the method’s ability. With just a few iterations, the produced findings are very effective, precise, and convergent to the exact answer. As a result, solving nonlinear equations using VIM is regarded as a viable option.

Hussein Abdul Ameer Abbas Al-Khamees, Nabeel Al-A'araji, Eman Salih Al-Shamery,

The data stream is considered the backbone of many real-world applications. These applications are most effective when using modern techniques of machine learning like deep neural networks (DNNs). DNNs are very sensitive to set parameters, the most prominent one is the learning rate. Choosing an appropriate learning rate value is critical because it is able to control the overall network performance. This paper presents a new developing DNN model using a multi-layer perceptron (MLP) structure that includes network training based on the optimal learning rate. Thereupon, this model consists of three hidden layers and does not adopt the stability of the learning rate but has a non-constant value (varying over time) to obtain the optimal learning rate which is able to reduce the error in each iteration and increase the model accuracy. This is done by deriving a new parameter that is added to and subtracted from the learning rate. The proposed model is evaluated by three streaming datasets: Electricity, network security layer-knowledge discovery in database (NSL-KDD), and human gait database (HuGaDB) datasets. The results proved that the proposed model achieves better results than the constant model and outperforms previous models in terms of accuracy, where it achieved 88.16%, 98.67%, and 97.63% respectively.

Abd Majid Darsono, Toh Cheng Chuan, Nurulfajar Abd Manap, Nik Zarifie Hashim,

An alpha-divergence two-dimensional nonnegative matrix factorization (NMF2D) for biomedical signal separation is presented. NMF2D is a popular approach for retrieving low-rank approximations of nonnegative data such as image pixel, audio signal, data mining, pattern recognition and so on. In this paper, we concentrate on biomedical signal separation by using NMF2D with alpha-divergence family which decomposes a mixture into two-dimensional convolution factor matrices that represent temporal code and the spectral basis. The proposed iterative estimation algorithm (alpha-divergence algorithm) is initialized with random values, and it updated using multiplicative update rules until the values converge. Simulation experiments were carried out by comparing the original and estimated signal in term of signal-to-distortion ratio (SDR). The performances have been evaluated by including and excluding the sparseness constraint which sparseness is favored by penalizing nonzero gains. As a result, the proposed algorithm improved the iteration speed and sparseness constraints produce slight improvement of SDR.

Hayder Ali Al-Fatlawi, Hassan Jassim Motlak,

Ports are currently competing fiercely for capital and global investments in order to improve revenues, mostly by improving performance and lowering labor costs. Smart ports are a fantastic approach to realize these elements since they integrate information and communication technologies within smart applications, ultimately contributing to port management improvement. This leads to greater performance and lower operational expenses. As a result, several ports in Europe, Asia, Australia, and North America have gone smart. However, there are a lot of critical factors to consider when automating port operations, such as greenhouse gas emissions, which have reached alarming proportions. The purpose of this study is to define the most essential tasks conducted by smart ports, such as the smart ship industry, smart gantry and quayside container cranes, transport automation, smart containers, and energy efficiency. Furthermore, it gives a model of the smart port concept and highlights the critical current technologies on which the ports are based. Each technology’s most significant contributions to its development are noted. This technology is compared to more traditional technologies. It is hoped that this effort would pique the curiosity of fresh researchers in this sector.

Zakaria M'barki, Kaoutar Senhaji Rhazi, Youssef Mejdoub,

This research provides a new control technique for mitigating conducted electromagnetic interference (EMI) in a buck converter designed for solar applications. Indeed, hard-switching direct current to direct current (DC-DC) converters, commonly used in industrial applications, pose a significant risk to the surrounding environment regarding electromagnetic compatibility (EMC). Usually, the fast-switching phase induces abrupt changes in current and voltage, which adds to substantial electromagnetic interference in both conducted and radiated modes and excessive auditory noise. An architecture based on the duality of soft-switching topology and fuzzy logic control technology is developed to address these issues. On the one hand, resonant circuit topologies are used to induce switches to achieve soft switching conditions, which subsequently lessen the effects of EMI. On the other hand, the adoption of fuzzy logic control technology is interesting since it can reduce electrical stresses during switching. Furthermore, the simulation results show that zero current switching (ZCS) soft-switching closed-loop fuzzy logic converters outperform typical open-loop converters and soft-switching closed-loop converters with proportional integral (PI) control in terms of EMC requirements.

Amina Samih, Abderrahim Ghadi, Abdelhadi Fennan,

Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.

Etna Vianita, Muhammad Sam'an, A. Nafis Haikal, Ina Salamatul Mufaricha, Redemtus Heru Tjahjana, Titi Udjiani,

The fuzzy time series (FTS) is a forecasting model based on linguistic values. This forecasting method was developed in recent years after the existing ones were insufficiently accurate. Furthermore, this research modified the accuracy of existing methods for determining and the partitioning universe of discourse, fuzzy logic relationship (FLR), and variation historical data using intervals ratio, cross association relationship, and rubber production Indonesia data, respectively. The modifed steps start with the intervals ratio to partition the determined universe discourse. Then the triangular fuzzy sets were built, allowing fuzzification. After this, the FLR are built based on the cross association relationship, leading to defuzzification. The average forecasting error rate (AFER) was used to compare the modified results and the existing methods. Additionally, the simulations were conducted using rubber production Indonesia data from 2000-2020. With an AFER result of 4.77%<10%, the modification accuracy has a smaller error than previous methods, indicating  very good forecasting criteria. In addition, the coefficient values of D1 and D2 were automatically obtained from the intervals ratio algorithm. The future works modified the partitioning of the universe of discourse using frequency density to eliminate unused partition intervals.

Kristoko Dwi Hartomo, April Firman Daru, Hindriyanto Dwi Purnomo,

The development of the internet of things (IoT), which functions as servers, device monitors, and controllers of several peripherals inside the smart home, eased workload in many sectors. Most devices are accessible through the internet because they communicate with wired or wireless interfaces. However, this feature makes them prone to the risk of being exposed to the public. The exposed devices are an easy target for the third party to launch a flooding attack through the network. This attack overloads the system due to the low processing capability, thereby interrupting any running process and harming the device. Therefore, this study proposed a scalable network capturing model that utilized multiple Raspberry Pi boards in parallel to monitor the network traffics simultaneously. An isolated experiment was used for evaluation by running simultaneous flooding attacks on each device. The result showed that the model consumed 30.44% more memory with 14.66% lower central processing unite (CPU) usage and 3.63% faster execution time. This means that this model is better in terms of performance and effectiveness than the single capture model.

Ali Kadhum Abd, Jamal Mohammed Rasool,

An octa-band frequency-reconfigurable antenna (28×14×1.5 mm3) with a broad tuning range is shown. Antenna mode1 (4.31 GHz) works in one single-band mode and two dual-band in modes 2 and 3 (i.e., 3.91 and 5.9 GHz) as well as one tri-band in mode 4 (i.e., 3.09, 5.65, and 7.92 GHz) based on the switching situation of the antenna. Changing capacitance for frequency reconfigurability is accomplished with the use of lumped components. The antenna’s observed tuning spans from 3.09 GHz to 7.92 GHz. for all the resonant bands, the suggested antenna has a voltage standing waves ratio (VSWR)<1.45 except for one band with a VSWR<1.85. From 70.57% to 97.93%, the suggested structure’s radiation efficiency may be calculated. For a better understanding proposed antenna’s far field and scattering characteristics, we used CST Microwave Studio 2021. We may conclude that our suggested antenna is suitable for today’s wireless applications, which need multiband and multimode small antennas. Using a small stainless-steel wire as a switch, a prototype of the antenna design is built and tested to verify the simulation findings. The suggested reconfigurable antenna’s strong concordance between simulated and measured findings.