International Journal of Information Technology and Computer Science @ijitcs
Journal articles - International Journal of Information Technology and Computer Science
All articles: 1304
Tree Adapt: Web Content Adaptation for Mobile Devices
Scientific article
Mobile web browsing usually becomes time-consuming since currently it requires horizontal and vertical scrolling in addition to this, users interested in only a section of a web page are often burdened with cumbersome whole web pages that not only do not properly fit their mobile screens but also require a lot of delivery time. This problem can be addressed and resolved with the help of a mobile web content adaptation system. Existing web content adaptation systems focus on resizing contents to fit a mobile device and removing unnecessary contents from the adapted web page. This paper’s aim is to address the gap by proposing the TreeAdapt which provides a condensed view of an adapted web page. This condensed view consists of only block headers which users can then expand for complete content. In order to achieve this, the proposed algorithm will first categorize an HTML object as menu, block title or the main content. A depth-first traversal algorithm is then used to select the sequence of blocks to be displayed on a mobile device. Usability studies were performed to evaluate the usability of the adapted contents against other deployed systems. Results from the usability studies indicate that the adapted contents produced by the proposed techniques enabled users to locate targeted information within a web page in a shorter span of time.
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Trends, Issues and Challenges Concerning Spam Mails
Scientific article
Traditional correspondence system has now been replaced by internet, which has now become indispensable in everyone’s life. With the advent of the internet, majority of people correspond through emails several times in a day. However, as internet has evolved, email is being exploited by spammers so as to disturb the recipients’. The entire internet community pays the price, every time there pops a spam mail. Online privacy of the users is compromised when spam disturbs a network by crashing mail servers and filling up hard disks. Servers classified as spam sites are forfeited from sending mails to the recipients’. This paper gives the broader view of spam, issues challenges and statistical losses occurred on account of spams.
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Scientific article
In the Internet of Things (IoT) environment, a Distributed Denial-of-Service (DDoS) attack in the network causes poor performance and resource-limited issues to users. Existing systems do not provide real-time adaptability, leading to delayed mitigation. Also, centralized storage systems suffer from breaches and tampering. To tackle these issues, a secure and intelligent IoT DDoS detection and mitigation framework is presented that utilizes hybrid encryption, blockchain storage, ensemble deep learning (DL), and reinforcement learning (RL) to improve the accuracy, security, and efficiency of IoT networks against several cyber-attacks. The developed technique collects data from a dataset and pre-processes it for handling missing values and normalizes it for further analysis. Secondly, a hybrid encryption method combining Homomorphic Encryption (HE) and ChaCha20 is adopted for data encryption with optimal key selection using Dingo Optimizer (DOX). Then, the encrypted data is securely stored in blockchain through off-chain storage and on-chain hash storage to ensure data integrity and tamper-proof security. DDoS attack detection is performed using an ensemble model called TriGuard-Net that combines AlexNet, LSTM, and PSPNet, with optimizing hyperparameters using Fire Hawks Optimizer (FHO). Finally, an RL-based mitigation system using Deep Q-Network (DQN) helps in real-time attack mitigation and enhances IoT security. Experimental results reveal that the presented model offers superior performance by achieving an accuracy of 99%, a kappa score of 98%, an R2 Score of 97%, an MCC of 98%, a Jaccard Score of 98%, and a Hamming Loss of 0.006, thereby outperforming other current models.
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Trust Formulization in Dynamic Source Routing Protocol Using SVM
Scientific article
In an advanced wireless network, trust is desirable for all routing protocols to secure data transmission. An enormous volume of important information communicates over the wireless network using trusted dynamic routing protocol, which is the enhancement of the DSR (Dynamic Source Routing) protocol to improve trust. Previously fuzzy logic, genetic algorithm, neural network has been used to modify DSR and good result has been obtained in few performance indicators and parameters. In this work an SVM based trusted DSR have been developed and better results have been presented. This new novel on demand trust based routing protocol for MANET is termed as Support vector machine based Trusted Dynamic Source Routing protocol, performance of STDSR has been improved in term of the detection ratio (%) at different mobility and no. of malicious node variation.
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Trusted Mobile Client for Document Security in Mobile Office Automation
Scientific article
Mobile Office Automation is a popular application on the mobile platform. However, the mobile platform has no enough security protection in front of the open system, such as internet. The document security problem in this application has become a hot topic. This paper proposed a new solution to this problem. The solution based on the trusted computing technology, which implements the platform security by hardware. The solution also includes the transparent encryption technology that means application could run independent to the security module and keep the consistency of user experience. The trusted mobile client platform architecture and security functions process of the mobile document protection system are detailed. Related work also been introduced; the security analysis shows the proposed solution could provide well security enhancement for document protection in mobile platform.
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Tuning stacked auto-encoders for energy consumption prediction: a case study
Scientific article
Energy is a requirement for electronic devices. A processor is a substantial part of computer components in terms of energy consumption. A great concern has risen over recent years about computers with regard to the energy consumption. Taking accurate information about energy consumption of a processor allows us to predict energy flow features. However, using traditional classifiers may not enhance the accuracy of the prediction of energy consumption. Deep learning shows great promise for predicting energy consumption of a processor. Stacked auto-encoders has emerged a robust type of deep learning. This work investigates the effects of tuning stacked auto-encoder in computer processor with regard to the energy consumption. To search parameter space, a grid search based training method is adopted. To prepare data to prediction, a data preprocessing algorithm is also proposed. According to the obtained results, on average, the method provides 0.2% accuracy improvement along with a remarkable success in reducing parameter tuning error. Further, in receiver operating curve analysis, tuned stacked auto-encoder was able to increase value of are under the curve up to 0.5.
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Scientific article
Twitter is extensively used in the Arab World in the last few years. It showed positive impact on both students and teachers in academic institutions. In this paper we introduce Taibah University as a case study to examine Twitter uses and benefits in education environments. Deans, Students and Teachers accounts on Twitter will be examined to discover the general uses of Twitter and the type of information which is commuting between students and teachers. Results showed that twitter is used extensively in Taibah University for after class room discussions, and for the teachers to share class notes and lectures, in addition it showed highly agreement on the importance of twitter in Taibah University from both students and faculty members.
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Two Fold Optimization of Precopy Based Virtual Machine Live Migration
Scientific article
Virtualization is widely adopted by the data centers, in order to fulfill the high demand for resources and for their proper utilization. For system management in these virtualized data centers virtual machine live migration acts as a key method. It provides significant benefit of load-balancing without service disruption. Along with the various benefits virtual machine live migration also imposes performance overhead in terms of computation, space and bandwidth used. This paper analyzes the widely used precopy method for virtual machine live migration and proposes the two fold optimization of precopy method for virtual machine live migration. In the first phase, the proposed two fold precopy method reduces the amount of data sent in first iteration of precopy method. Second phase restricts sending of similar data iteratively in each subsequent iterations of precopy method by identifying frequently updated pages and keeps it till the last stop and copy iteration. In this way it reduces total migration time and total amount of data transferred. The proposed two fold precopy method is compared with precopy method and simulation results show the performance improvement of a virtual machine live migration in terms of total migration time and total amount of data transferred.
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Two Group Signature Schemes with Multiple Strategies Based on Bilinear Pairings
Scientific article
A group signature scheme and a threshold group signature scheme based on Bilinear Paring are proposed, there are multiple security strategies in these two schemes. These schemes have forward security which minimizes the damage caused by the exposure of any group member's signing key, and does not affect the past signatures generated by this member; meanwhile, ahead signature generated by a group member before the joining date can be prevented via this strategy. Moreover, this scheme support the group member revocable function efficiently and further has no requirement for time period limits.
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Two SAOR Iterative Formats for Solving Linear Complementarity Problems
Scientific article
In this paper, we propose two new iterative SAOR methods to solve the linear complementarity problem. Some sufficient conditions for the convergence of two new iterative methods are presented, when the system matrix M is an M-matrix. Moreover, when M is an L-matrix, we discuss the monotone convergence of the new methods. And in the numerical experiments we report some computational results with the two proposed SAOR formats.
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Typology for Linguistic Pattern in English-Hindi Journalistic Text Reuse
Scientific article
Linking and tracking news stories covering the same events written in different languages is a challenging task. In natural languages same information may be expressed in multiple ways and newspapers try to exploit this feature for making the news stories more appealing. It has been observed that the same news story is presented in same as well as in different language in different ways but normally the gist remains the same. Diversity of linguistic expressions presents a major challenge in identifying and tracking news stories covering the same events across languages, but doing so may provide rich and valuable resources as comparable and parallel corpora can be generated with this resource. In the case of Indian languages there exist limited language resources for Natural Language Processing and Information Retrieval tasks and identifying comparable and parallel documents would offer a potential source for deriving bilingual dictionaries and training statistical Machine Translation systems. Paraphrasing is the most common way of reproducing news stories and translated text is also a type of paraphrase. Prior to linking monolingual or bilingual news stories, these paraphrase types need to identified and classified to help researchers to devise techniques to solve these challenging problems. English-Hindi language pair not only differs in their scripts but also in their grammar and vocabulary. A number of paraphrase typologies have been built from the perspective of Natural Language Processing or for some or the other specific applications but as per the knowledge of the authors, no typology have been reported for English-Hindi cross language text reuse. In this paper a typology is formulated for cross lingual journalistic text reuse in English-Hindi. Typology unravels level of difficulties in English-Hindi mapping. It shall help in devising techniques for linking and tracking English-Hindi stories.
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Ultrasound Image Despeckling using Local Binary Pattern Weighted Linear Filtering
Scientific article
Speckle noise formed as a result of the coherent nature of ultrasound imaging affects the lesion detectability. We have proposed a new weighted linear filtering approach using Local Binary Patterns (LBP) for reducing the speckle noise in ultrasound images. The new filter achieves good results in reducing the noise without affecting the image content. The performance of the proposed filter has been compared with some of the commonly used denoising filters. The proposed filter outperforms the existing filters in terms of quantitative analysis and in edge preservation. The experimental analysis is done using various ultrasound images.
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Under Water Optical Wireless Communications Technology for Short and Very Short Ranges
Scientific article
This paper has presented our interest in wireless underwater optical communications. Recent interest in ocean exploration has brought about a desire for developing wireless communication techniques in this challenging environment. Due to its high attenuation in water, a radio frequency (RF) carrier is not the optimum choice. Acoustic techniques have made tremendous progress in establishing wireless underwater links, but they are ultimately limited in bandwidth. In traditional communication systems, constructing a link budget is often relatively straight forward. In the case of underwater optical systems the variations in the optical properties of sea water lead to interesting problems when considering the feasibility and reliability of underwater optical links. The main focus of this paper is to construct an underwater link budget which includes the effects of scattering and absorption of realistic sea water. As well as we have developed the underwater optical wireless communication systems to have shorter ranges, that can provide higher bandwidth (up to several hundred Mbit/sec) communications by the assistant of exciting high brightness blue LED sources, and laser diodes suggest that high speed optical links can be viable for short range application.
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Understanding Gen Z's Engagement with Conversational AI: A Modified TAM Study on WhatsApp's Meta AI
Scientific article
The rapid integration of conversational Artificial Intelligence (AI) into instant messaging platforms has transformed how younger generations interact with digital technology. This study investigates Generation Z's engagement with Meta AI on WhatsApp by employing a modified Technology Acceptance Model (TAM) that partitions End-User Computing Satisfaction (EUCS) dimensions to ensure measurement validity. Specifically, 'content' and 'accuracy' reflect Perceived Usefulness, while 'format' and 'timeliness' reflect Perceived Ease of Use. A quantitative survey involving 272 Generation Z respondents in Indonesia was analyzed using Covariance-Based Structural Equation Modeling (CB-SEM) with AMOS. The results reveal that Perceived Ease of Use significantly influences both Perceived Usefulness and Attitude Toward Using. Consequently, Perceived Usefulness acts as a partial, rather than full, mediator between ease of use and user attitude. Furthermore, Attitude Toward Using emerged as a powerful determinant of Actual System Use, with the proposed model explaining 89% of its variance (R2 = 0.89). These findings suggest a synergistic effect for "digital natives": while an intuitive format and fast response times directly foster positive attitudes, the epistemic quality and accuracy of the AI remain the dominant drivers of sustained engagement. This study contributes theoretically by validating a robust, multicollinearity-resistant modified TAM for conversational AI, providing practical insights for developers to maintain frictionless interfaces while prioritizing algorithmic accuracy to enhance user adoption.
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Underwater Image Dehazing: A Comprehensive Approach
Scientific article
Underwater imaging in recent times has advanced by trying to correct color distortion, increase contrast, and increase image clarity if the light need is less. The use of deep learning has been effective in enhancing image quality, but challenges persist in the decompression process due to data inconsistencies. In order to do this a new scheme is proposed in this study. Unlike other methods which depend only on the single images captured, here an attempt is made to use images taken in other conditions to overcome this limitation, by using the model to try and improve such underwater images in general irrespective of the water conditions. A key innovation is the disassembly and synthesis of multi-channel illuminance data. Specifically, we decompose the input image into its red, green, and blue frequencies, and then approximate the illuminance component within each channel. By independently manipulating and reconstructing these channel-specific illuminance maps, we can effectively address the non-uniform light scattering and absorption that are characteristic of underwater environments. This allows us to correct for the inherent color casts and haze that degrade image quality. To further refine the enhancement, we incorporate, advanced color correction methods such as image saliency exploration and white balance adjustment to compensate for color attenuation caused by light absorption at different depths. These techniques effectively restore lost colors and enhance contrast, thereby improving image clarity and sharpness. This is helpful in the field of engineering and also forms the foundation for further exploring methods of improving images captured underwater. Investigational outcomes exhibit that the intended method ominously augments image eminence, making it highly effective for underwater detection and exploration tasks, offering an innovative solution for hazy images in various conditions and advancing underwater monitoring and exploration technologies.
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Unifying the Access Control Mechanism for the Enterprises Using XACML Policy Levels
Scientific article
Many enterprises have intended to promote their applications with stern access control mechanism and yield the stringent authorization deployment in their individual proprietary manner. The development of this build up will result in tight coupling of authorization mechanisms within the enterprise applications. In many enterprises setup, the implicit authorization processes are embedded within the application and promote error prone accessing of requested policies. This sort of embedded authorization will let the users to carry out the specific actions without knowing the access control policy as well as its embedded setup with the help of third party involvement. But this approach has some serious effects in controlling the issues such as skipping the trust based applications, violates the policy setups and pave the way to exploit the authorized data to the end users. Many enterprises had faced serious problem in controlling its sensitive data from this implicit authorization decisions and hence decided to develop a security mechanism which can be totally controlled by centralized way of access policy. Therefore, the eXtensible Access Control Markup Language (XACML) provides a very simple and powerful remedy for authorization mechanism and for the access policy set ups.
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Unsupervised Learning based Modified C- ICA for Audio Source Separation in Blind Scenario
Scientific article
Separating audio sources from a convolutive mixture of signals from various independent sources is a very fascinating area in personal and professional context. The task of source separation becomes trickier when there is no idea about mixing environment and can be termed as blind audio source separation (BASS). Mixing scenario becomes more complicated when there is a difference between number of audio sources and number of recording microphones, under determined and over determined mixing. The main challenge in BASS is quality of separation and separation speed and the convergence speed gets compromised when separation techniques focused on quality of separation. This work proposed divergence algorithm designed for faster convergence speed along with good quality of separation. Experiments are performed for critically determined audio recording, where number of audio sources is equal to number of microphones and no noise component is taken into consideration. The result advocates that the modified convex divergence algorithm enhance the convergence speed by 20-22% and good quality of separation than conventional convex divergence ICA, Fast ICA, JADE.
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Unveiling Autism: Machine Learning-based Autism Spectrum Disorder Detection through MRI Analysis
Scientific article
The prediction of autism features in relation to age groups has not been definitively addressed, despite the fact that several studies have been conducted using various methodologies. Research in the field of neuroscience has demonstrated that intracranial brain volume and the corpus callosum provide crucial information for the identification of autism spectrum disorder (ASD). Based on these findings, we present Decision Tree-based Autism Prediction System (DT-APS) and Random Forest-based Autism Prediction System (RF-APS) for automatic ASD identification in this paper. These systems utilize characteristics extracted from the corpus callosum and intracranial brain volume, and are based on machine learning techniques. By prioritizing characteristics with the highest discriminatory power for ASD classification, our proposed approaches, DT-APS and RF-APS, have not only enhanced identification accuracy but also simplified the training of machine learning models. The initial step of this method involves dividing each MRI scan into distinct anatomical areas. These areas are adjacent slices in a single 2D image. Each 2D image is mapped to the curvelet space, and the set of GGD parameters characterizes each of the distinct curvelet sub-bands. The AQ-10 dataset was utilized to evaluate the proposed model. When tested on both types of datasets, the suggested prediction model demonstrated superior performance compared to alternative approaches in all relevant metrics, including accuracy, specificity, sensitivity, precision, and false positive rate (FPR).
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Uplink Capacity Estimation and Enhancement in WCDMA Network
Scientific article
Uplink planification in a WCDMA network consists of estimating the maximum capacity that a cell can support, by using the quality of service equation designed by (E_b)/(N_0). We are interested in this work on two different scenarios: an isolated cell and multiple cells. This capacity is adversely affected by interferences due to own mobile stations and to others belonging to neighboring cells. In order to enhance capacity and minimize the blocking probability of new requests in the cell, we have proposed a Freeing Resources algorithm which consists of releasing some mobile stations in the handover area with the overloaded cell. This algorithm is based on freeing 1, 2 and 3 mobile stations of 12.2 kbps and 1 mobile station of 64 kbps.
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Usability Evaluation Criteria for Internet of Things
Scientific article
The field of usability, user experience (UX) design and human-computer interaction (HCI) arose in the realm of desktop computers and applications. The current experience in computing has radically evolved into ubiquitous computing over the preceding years. Interactions these days take place on different devices: mobile phones, e-readers and smart TVs, amid numerous smart devices. The use of one service across multiple devices is, at present, common with different form factors. Academic researchers are still trying to figure out the best design techniques for new devices and experiences. The Internet of Things (IoT) is growing, with an ever wider range of daily objects acquiring connectivity, sensing ability and increased computing power. Designing for IoT raises a lot of challenges; the obvious difference being the much wider variety of device form factors. IoT is still a technically driven field, thus the usability of many of IoT products is, in some way, of the level anticipated of mature consumer products. This study focuses on proposing a usability evaluation criterion for the generic IoT architecture and essential technological components.
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