Volume 23 Issue 9
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Muhammad Umer Farooq;Mustafa Latif;Waseemullah;Mirza Adnan Baig;Muhammad Ali Akhtar;Nuzhat Sana 1
Demand prediction is an essential component of any business or supply chain. Large retailers need to keep track of tens of millions of items flows each day to ensure smooth operations and strong margins. The demand prediction is in the epicenter of this planning tornado. For business processes in retail companies that deal with a variety of products with short shelf life and foodstuffs, forecast accuracy is of the utmost importance due to the shifting demand pattern, which is impacted by an environment of dynamic and fast response. All sectors strive to produce the ideal quantity of goods at the ideal time, but for retailers, this issue is especially crucial as they also need to effectively manage perishable inventories. In light of this, this research aims to show how Machine Learning approaches can help with demand forecasting in retail and future sales predictions. This will be done in two steps. One by using historic data and another by using open data of weather conditions, fuel, Consumer Price Index (CPI), holidays, any specific events in that area etc. Several machine learning algorithms were applied and compared using the r-squared and mean absolute percentage error (MAPE) assessment metrics. The suggested method improves the effectiveness and quality of feature selection while using a small number of well-chosen features to increase demand prediction accuracy. The model is tested with a one-year weekly dataset after being trained with a two-year weekly dataset. The results show that the suggested expanded feature selection approach provides a very good MAPE range, a very respectable and encouraging value for anticipating retail demand in retail systems. -
Suguru Kuniyoshi;Rie Saotome;Shiho Oshiro;Tomohisa Wada 8
In Japan, high-speed ground transportation service using linear motors at speeds of 500 km/h is scheduled to begin in 2027. To accommodate 5G services in trains, a subcarrier spacing frequency of 30 kHz will be used instead of the typical 15 kHz subcarrier spacing to mitigate Doppler effects in such high-speed transport. Furthermore, to increase the cell size of the 5G mobile system, multiple base station antennas will transmit identical downlink (DL) signals to form an expanded cell size along the train rails. In this situation, the forward and backward antenna signals are Doppler-shifted in opposite directions, respectively, so the receiver in the train may suffer from estimating the exact Channel Transfer Function (CTF) for demodulation. In a previously published paper, we proposed a channel estimator based on Delay and Doppler Profiler (DDP) in a 5G SISO (Single Input Single Output) environment and successfully implemented it in a signal processing simulation system. In this paper, we extend it to 2×2 MIMO (Multiple Input Multiple Output) with spatial multiplexing environment and confirm that the delay and DDP based channel estimator is also effective in 2×2 MIMO environment. Its simulation performance is compared with that of a conventional time-domain linear interpolation estimator. The simulation results show that in a 2×2 MIMO environment, the conventional channel estimator can barely achieve QPSK modulation at speeds below 100 km/h and has poor CNR performance versus SISO. The performance degradation of CNR against DDP SISO is only 6dB to 7dB. And even under severe channel conditions such as 500km/h and 8-path inverse Doppler shift environment, the error rate can be reduced by combining the error with LDPC to reduce the error rate and improve the performance in 2×2 MIMO. QPSK modulation scheme in 2×2 MIMO can be used under severe channel conditions such as 500 km/h and 8-path inverse Doppler shift environment. -
Today Agriculture segment is a significant supporter of Indian economy as it represents 18% of India's Gross Domestic Product (GDP) and it gives work to half of the nation's work power. Farming segment are required to satisfy the expanding need of food because of increasing populace. Therefore, to cater the ever-increasing needs of people of nation yield prediction is done at prior. The farmers are also benefited from yield prediction as it will assist the farmers to predict the yield of crop prior to cultivating. There are various parameters that affect the yield of crop like rainfall, temperature, fertilizers, ph level and other atmospheric conditions. Thus, considering these factors the yield of crop is thus hard to predict and becomes a challenging task. Thus, motivated this work as in this work dataset of different states producing different crops in different seasons is prepared; which was further pre-processed and there after machine learning techniques Gradient Boosting Regressor, Random Forest Regressor, Decision Tree Regressor, Ridge Regression, Polynomial Regression, Linear Regression are applied and their results are compared using python programming.
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Atheer Alkhammash;Kawther Saeedi;Fatmah Baothman;Amal Babour 29
Blockchain is an emerging technology that is used to address ownership, centrality, and security issues in different fields. The blockchain technology has converted centralized applications into decentralized and distributed ones. In existing sharing economy applications, there are issues related to low efficiency and high complexity of services. However, blockchain technology can be adopted to overcome these issues by effectively opening up secure information channels of the sharing economy industry and other related parties, encouraging industry integration and improving the ability of sharing economy organizations to readily gain required information. This paper discusses blockchain technology to enhance the development of insurance services by proposing a five-layer decentralized model. The Najm for Insurance Services Company in Saudi Arabia was employed in a case study for applying the proposed model to effectively solve the issue of online underwriting, and to securely and efficiently enhance the verification and validation of transactions. The paper concludes with a review of the lessons learned and provides suggestions for blockchain application development process. -
Oksana Penkova;Oleksandr Zakharchuk;Ivan Blahun;Alina Berher;Veronika Nechytailo;Andrii Kharenko 37
The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024. -
For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. But previous researches had the flaws of low accuracy and overfitting problem. In this research, attention-based Conv1D and BiGRU model is designed for music emotion classification and comparative experimentation shows that the proposed model is classifying emotions more accurate. We have proposed a Conv1D and Bi-GRU with the attention-based model for emotion classification of our Bengali music dataset. The model integrates attention-based. Wav preprocessing makes use of MFCCs. To reduce the dimensionality of the feature space, contextual features were extracted from two Conv1D layers. In order to solve the overfitting problems, dropouts are utilized. Two bidirectional GRUs networks are used to update previous and future emotion representation of the output from the Conv1D layers. Two BiGRU layers are conntected to an attention mechanism to give various MFCC feature vectors more attention. Moreover, the attention mechanism has increased the accuracy of the proposed classification model. The vector is finally classified into four emotion classes: Angry, Happy, Relax, Sad; using a dense, fully connected layer with softmax activation. The proposed Conv1D+BiGRU+Attention model is efficient at classifying emotions in the Bengali music dataset than baseline methods. For our Bengali music dataset, the performance of our proposed model is 95%.
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The Internet of Things (IoT) is the combination of the internet and various sensing devices. IoT security has increasingly attracted extensive attention. However, significant losses appears due to malicious attacks. Therefore, intrusion detection, which detects malicious attacks and their behaviors in IoT devices plays a crucial role in IoT security. The intrusion detection system, namely IDS should be executed efficiently by conducting classification and efficient feature extraction techniques. To effectively perform Intrusion detection in IoT applications, a novel method based on a Conventional Neural Network (CNN) for classification and an improved Genetic Algorithm (GA) for extraction is proposed and implemented. Existing issues like failing to detect the few attacks from smaller samples are focused, and hence the proposed novel CNN is applied to detect almost all attacks from small to large samples. For that purpose, the feature selection is essential. Thus, the genetic algorithm is improved to identify the best fitness values to perform accurate feature selection. To evaluate the performance, the NSL-KDDCUP dataset is used, and two datasets such as KDDTEST21 and KDDTEST+ are chosen. The performance and results are compared and analyzed with other existing models. The experimental results show that the proposed algorithm has superior intrusion detection rates to existing models, where the accuracy and true positive rate improve and the false positive rate decrease. In addition, the proposed algorithm indicates better performance on KDDTEST+ than KDDTEST21 because there are few attacks from minor samples in KDDTEST+. Therefore, the results demonstrate that the novel proposed CNN with the improved GA can identify almost every intrusion.
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Sabir Abbas;Shane zahra;Muhammad Asif;khalid masood 65
The "information roadway" will give us an impact of new PC based assignments and administrations, yet the unusualness of this new condition will ask for another style of human-PC association, where the PC transforms into a sharp, dynamic and customized partner. Interface administrators are PC programs that use Artificial Intelligence frameworks to give dynamic help to a customer with PC based errands. Operators drastically change the present client encounter, through the similitude that a specialist can go about as an individual collaborator. The operator procures its capability by gaining from the client and from specialists helping different clients. A couple of model administrators have been gathered using this methodology, including authorities that give customized help with meeting planning, electronic mail taking care of, Smart Personal Assistant and choice of diversion. Operators help clients in a scope of various ways: they perform assignments for the client's sake; they can prepare or educate the client, they enable diverse clients to work together and they screen occasions and methods. -
Aryna Kharkivska;Viktoria Beskorsa;Svitlana Nikulenko;Oksana Onypchenko;Violetta Panchenko;Iryna Tolmachova 71
In order to achieve this goal, we consider the following issues: a retrospective analysis of the concepts of "giftedness", "intellectually gifted personality"; the experience of working with gifted youth abroad is analyzed; features of work with gifted student youth are revealed; educational and methodological support and practical work with gifted students are analyzed; an empirical study was conducted to determine the attitude of gifted youth to organizational forms of work with them; developed a scientific circle as a form of work with intellectually gifted student youth. A set of methods is used, in particular: theoretical methods: comparison, analysis, synthesis and generalization; empirical methods: analysis of documentation and results of pedagogical activity, observation. -
Today, crops face many characteristics/diseases. Insect damage is one of the main characteristics/diseases. Insecticides are not always effective because they can be toxic to some birds. It will also disrupt the natural food chain for animals. A common practice of plant scientists is to visually assess plant damage (leaves, stems) due to disease based on the percentage of disease. Plants suffer from various diseases at any stage of their development. For farmers and agricultural professionals, disease management is a critical issue that requires immediate attention. It requires urgent diagnosis and preventive measures to maintain quality and minimize losses. Many researchers have provided plant disease detection techniques to support rapid disease diagnosis. In this review paper, we mainly focus on artificial intelligence (AI) technology, image processing technology (IP), deep learning technology (DL), vector machine (SVM) technology, the network Convergent neuronal (CNN) content Detailed description of the identification of different types of diseases in tomato and potato plants based on image retrieval technology (CBIR). It also includes the various types of diseases that typically exist in tomato and potato. Content-based Image Retrieval (CBIR) technologies should be used as a supplementary tool to enhance search accuracy by encouraging you to access collections of extra knowledge so that it can be useful. CBIR systems mainly use colour, form, and texture as core features, such that they work on the first level of the lowest level. This is the most sophisticated methods used to diagnose diseases of tomato plants.
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This study aims to evaluate the Higher Diploma in English for the Primary Stage from the diploma students' perspectives. A questionnaire was designed consisting of 25 items distributed in two areas: cognitive/academic preparation and professional/skill preparation. The following statistical analyses were used: means, standard deviations, t-test, and one-way analysis of variance (ANOVA). The study results showed that the level of evaluation of the two domains in the program was low. The study also showed no statistically significant differences between the means of educational diploma students when evaluating the Higher Diploma in English for the Primary Stage due to their academic specialization (Arabic language, social sciences, and Islamic studies). In conclusion, the researcher suggested a developmental mechanism derived from the study results to improve the higher Diploma in English for the Primary Stage.
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Maqsood Ali Solangi;Ghulam Ali Mallah;Shagufta Naz;Jamil Ahmed Chandio;Muhammad Bux Soomro 95
Recently Machine Learning has been considered as one of the active research areas of Computer Science. The various Artificial Intelligence techniques are used to solve the classification problems of environmental sciences, biological sciences, and medical sciences etc. Due to the heterogynous and malfunctioning weather sensors a considerable amount of noisy data with missing is generated, which is alarming situation for weather prediction stockholders. Filling of these missing values with proper method is really one of the significant problems. The data must be cleaned before applying prediction model to collect more precise & accurate results. In order to solve all above stated problems, this research proposes a novel weather forecasting system which consists upon two steps. The first step will prepare data by reducing the noise; whereas a decision model is constructed at second step using regression algorithm. The Confusion Matrix will be used to evaluation the proposed classifier. -
The Internet of Things (IoT) is the most creative and focused technology to be employed today. It increases the living conditions of both individuals and society. IoT offers the ability to recognize and incorporate physical devices across the globe through a single network by connecting different devices by using various technologies. As part of IoTs, significant questions are posed about access to computer and user privacy-related personal details. This article demonstrates the three-layer architecture composed of the sensor, routing, and implementation layer, respectively, by highlighting the security risks that can occur in various layers of an IoT architecture. The article also involves countermeasures and a convenient comparative analysis by discussing major attacks spanning from detectors to application. Furthermore, it deals with the basic protocols needed for IoT to establish a reliable connection between objects and items.
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Raed Alsaqour;Maha Abdelhaq;Njoud Alghamdi;Maram Alneami;Tahani Alrsheedi;Salma Aldghbasi;Rahaf Almalki;Sarah Alqahtani 111
Mobile Ad-hoc Network (MANET) is an infrastructure-less network that can configure itself without any centralized management. The topology of MANET changes dynamically which makes it open for new nodes to join it easily. The openness area of MANET makes it very vulnerable to different types of attacks. One of the most dangerous attacks is the Resource Consumption Attack (RCA). In this type of attack, the attacker consumes the normal node energy by flooding it with bogus packets. Routing in MANET is susceptible to RCA and this is a crucial issue that deserves to be studied and solved. Therefore, the main objective of this paper is to study the impact of RCA on two routing protocols namely, Ad hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR); as a try to find the most resistant routing protocol to such attack. The contribution of this paper is a new RCA model (RCAM) which applies RCA on the two chosen routing protocols using the NS-2 simulator. -
Abdelmawgoud M. Meabed;Sherif Mahdy Abdou;Mervat Hassan Gheith 120
In this work, we have presented ATSA, a hierarchical attention deep learning model for Arabic sentiment analysis. ATSA was proposed by addressing several challenges and limitations that arise when applying the classical models to perform opinion mining in Arabic. Arabic-specific challenges including the morphological complexity and language sparsity were addressed by modeling semantic composition at the Arabic morphological analysis after performing tokenization. ATSA proposed to perform phrase-chunks sentiment embedding to provide a broader set of features that cover syntactic, semantic, and sentiment information. We used phrase structure parser to generate syntactic parse trees that are used as a reference for ATSA. This allowed modeling semantic and sentiment composition following the natural order in which words and phrase-chunks are combined in a sentence. The proposed model was evaluated on three Arabic corpora that correspond to different genres (newswire, online comments, and tweets) and different writing styles (MSA and dialectal Arabic). Experiments showed that each of the proposed contributions in ATSA was able to achieve significant improvement. The combination of all contributions, which makes up for the complete ATSA model, was able to improve the classification accuracy by 3% and 2% on Tweets and Hotel reviews datasets, respectively, compared to the existing models. -
Classification or prediction problem is how to solve it using a specific feature to obtain the predicted class. A wheat seeds specifications 4 3 classes of seeds will be used in a prediction process. A multi linear regression will be built, and a prediction error ratio will be calculated. To enhance the prediction ratio an ANN model will be built and trained. The obtained results will be examined to show how to make a prediction tool capable to compute a predicted class number very close to the target class number.
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The distributed system is playing a vital role in storing and processing big data and data generation is speedily increasing from various sources every second. Hadoop has a scalable, and efficient distributed system supporting commodity hardware by combining different networks in the topographical locality. Node support in the Hadoop cluster is rapidly increasing in different versions which are facing difficulty to manage clusters. Hadoop does not provide Node management, adding and deletion node futures. Node identification in a cluster completely depends on DHCP servers which managing IP addresses, hostname based on the physical address (MAC) address of each Node. There is a scope to the hacker to theft the data using IP or Hostname and creating a disturbance in a distributed system by adding a malicious node, assigning duplicate IP. This paper proposing novel node management for the distributed system using DNA hiding and generating a unique key using a unique physical address (MAC) of each node and hostname. The proposed mechanism is providing better node management for the Hadoop cluster providing adding and deletion node mechanism by using limited computations and providing better node security from hackers. The main target of this paper is to propose an algorithm to implement Node information hiding in DNA sequences to increase and provide security to the node from hackers.
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Repacked mobile apps constitute about 78% of all malware of Android, and it greatly affects the technical ecosystem of Android. Although many methods exist for repacked app detection, most of them suffer from performance issues. In this manuscript, a novel method using the Constant Key Point Selection and Limited Binary Pattern (CKPS: LBP) Feature extraction-based Hashing is proposed for the identification of repacked android applications through the visual similarity, which is a notable feature of repacked applications. The results from the experiment prove that the proposed method can effectively detect the apps that are similar visually even that are even under the double fold content manipulations. From the experimental analysis, it proved that the proposed CKPS: LBP method has a better efficiency of detecting 1354 similar applications from a repository of 95124 applications and also the computational time was 0.91 seconds within which a user could get the decision of whether the app repacked. The overall efficiency of the proposed algorithm is 41% greater than the average of other methods, and the time complexity is found to have been reduced by 31%. The collision probability of the Hashes was 41% better than the average value of the other state of the art methods.
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For a doctor, diagnosing a patient's heart disease is not easy. It takes the ability and experience with high flying hours to be able to accurately diagnose the type of patient's heart disease based on the existing factors in the patient. Several studies have been carried out to develop tools to identify types of heart disease in patients. However, most only focus on the results of patient answers and lab results, the rest use only echocardiography data or electrocardiogram results. This research was conducted to test how accurate the results of the classification of heart disease by using two medical data, namely echocardiography and electrocardiogram. Three treatments were applied to the two medical data and analyzed using the decision tree approach. The first treatment was to build a classification model for types of heart disease based on echocardiography and electrocardiogram data, the second treatment only used echocardiography data and the third treatment only used electrocardiogram data. The results showed that the classification of types of heart disease in the first treatment had a higher level of accuracy than the second and third treatments. The accuracy level for the first, second and third treatment were 78.95%, 73.69% and 50%, respectively. This shows that in order to diagnose the type of patient's heart disease, it is advisable to look at the records of both the patient's medical data (echocardiography and electrocardiogram) to get an accurate level of diagnosis results that can be accounted for.
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Reliability is one of the computable quality features of the software. To assess the reliability the software reliability growth models(SRGMS) are used at different test times based on statistical learning models. In all situations, Tradational time-based SRGMS may not be enough, and such models cannot recognize errors in small and medium sized applications.Numerous traditional reliability measures are used to test software errors during application development and testing. In the software testing and maintenance phase, however, new errors are taken into consideration in real time in order to decide the reliability estimate. In this article, we suggest using the Weibull model as a computational approach to eradicate the problem of software reliability modeling. In the suggested model, a new distribution model is suggested to improve the reliability estimation method. We compute the model developed and stabilize its efficiency with other popular software reliability growth models from the research publication. Our assessment results show that the proposed Model is worthier to S-shaped Yamada, Generalized Poisson, NHPP.
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Power can be generated from either renewable or non-renewable sources. Renewable sources are liked to maintain a strategic distance from contamination emanation and rely on upon fossil energizes which is decreasing day by day. The proposed sun powered vitality transformation unit comprises of a sun oriented exhibit, Bidirectional DC-DC converter, single stage inverter and AC. The inverter changes over DC control from the PV board into AC power and offered it to the heap which is associated with the lattice. The photovoltaic sun powered vitality (PV) is the most direct approach to change over sunlight based radiation into power and depends on the photovoltaic impact. The most extreme power point following of the PV yield for all daylight conditions is a key to keep the yield control per unit cost low for fruitful PV applications. Framework associated PV frameworks dependably have an association with people in general power matrix by means of an appropriate inverter in light of the fact that a PV module conveys just dc power. This project presents the new design, Development and Performance Analysis of a Grid Connected PV Inverter. Demonstrate that the proposed framework can lessen the Energy Consumption radically from the power board and give a solid support to the Grid.
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A. A. Alabi;B. S. Afolabi;B. I. Akhigbe;A. A. Ayoade 166
A scenario known as conflict in face recognition may arise as a result of some disparity-related issues (such as expression, distortion, occlusion and others) leading to a compromise of someone's identity or contradiction of the intended message. However, addressing this requires the determination and application of appropriate procedures among the various conflict theories both in terms of concepts as well as resolution strategies. Theories such as Marxist, Game theory (Prisoner's dilemma, Penny matching, Chicken problem), Lanchester theory and Information theory were analyzed in relation to facial images conflict and these were made possible by trying to provide answers to selected questions as far as resolving facial conflict is concerned. It has been observed that the scenarios presented in the Marxist theory agree with the form of resolution expected in the analysis of conflict and its related issues as they relate to face recognition. The study observed that the issue of conflict in facial images can better be analyzed using the concept introduced by the Marxist theory in relation to the Information theory. This is as a result of its resolution strategy which tends to seek a form of balance as result as opposed to the win or lose case scenarios applied in other concepts. This was also consolidated by making reference to the main mechanisms and result scenario applicable in Information theory. -
The global FinTech industry has experienced significant growth, with key projects developing the financial sector. In Saudi Arabia, startups have used technology to offer FinTech services. In this area, it is important to investigate the usability of platforms that offer FinTech services. This research aims to examine the usability of samples of Saudi FinTech websites and identify design issues impacting user experience. Usability testing was conducted on the websites of two FinTech firms identified design issues, including navigation problems on the homepage and a lack of transparency in displaying investment details, negatively impacting end users. Employing usability methods can assist in enhancing the development of FinTech platforms and addressing these issues. This study contributes to a deeper understanding of FinTech usability problems and the user experience, enabling advancements in the industry.
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Maksym Kovalchuk;Viktoriia Kharchenko;Andrii Yavorskyi;Igor Bieda;Taras Panchenko 186
Neural Networks are widely used for huge variety of tasks solution. Machine Learning methods are used also for signal and time series analysis, including electrocardiograms. Contemporary wearable devices, both medical and non-medical type like smart watch, allow to gather the data in real time uninterruptedly. This allows us to transfer these data for analysis or make an analysis on the device, and thus provide preliminary diagnosis, or at least fix some serious deviations. Different methods are being used for this kind of analysis, ranging from medical-oriented using distinctive features of the signal to machine learning and deep learning approaches. Here we will demonstrate a neural network-based approach to this task by building an ensemble of 1D CNN classifiers and a final classifier of selection using logistic regression, random forest or support vector machine, and make the conclusions of the comparison with other approaches. -
Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.