Predicting Depression Using Social Media Posts

The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people's mental health status. The use of the UGC could aid in the prediction of mental healthparticularly depression where it is a significant medical condition that impairs one's ability to work, learn, eat, sleep, and enjoy life. However, all of the information about a person's mood and negativism can be gather from their SNS user profile. Therefore, this study utilize SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models are evaluated to classify the UGC which are : Decision Forest, Neural Network and Support Vector Machine (SVM). The resuls shows that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model which is 78.27% and 0.042 but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best models for making predictions to determine the level of depression by using social media posts

Insulin and Insulin-like Growth Factor signalling (IGF) pathways and cancer / Wan Iryani Wan Ismail, Mohd Nazri Abu, Muhammad Ashraf Mohd Salleh, Izmil Haikal Zainol and Rosmadi Mohd Yusoff

Cell signalling is part of a strategy in drug discovery. Among the focus is by studying the insulin and insulin-like growth factor (IGF1R) signalling pathways. The molecular mechanism of insulin and IGF1R signalling pathways have been studied extensively. Both pathways are vital in many of the mechanisms in human body particularly in regulating the metabolism and cell growth. Besides, both pathways have been found to be involved in numerous diseases such as in various types of cancer. This review briefly revealed the information on the pathways, their correlations and current findings in cancer study

Insulin and Insulin-like Growth Factor signalling (IGF) pathways and cancer / Wan Iryani Wan Ismail, Mohd Nazri Abu, Muhammad Ashraf Mohd Salleh, Izmil Haikal Zainol and Rosmadi Mohd Yusoff

Cell signalling is part of a strategy in drug discovery. Among the focus is by studying the insulin and insulin-like growth factor (IGF1R) signalling pathways. The molecular mechanism of insulin and IGF1R signalling pathways have been studied extensively. Both pathways are vital in many of the mechanisms in human body particularly in regulating the metabolism and cell growth. Besides, both pathways have been found to be involved in numerous diseases such as in various types of cancer. This review briefly revealed the information on the pathways, their correlations and current findings in cancer study

Public perception on landscape design towards property values of high rise residential development in Kuala Lumpur

Due to the scarcity of land and high land value, high rise residential are becoming a popular type of property development in Kuala Lumpur. The study investigates public perception on landscape design towards property values of high rise residential in Kuala Lumpur. It is typical in current developments that landscape infrastructure is a selling point and prominent feature. However it is vague as to whether this helps to increase the property value. Potential buyers may possibly place differential preference towards properties with landscape infrastructure should it provide benefits towards increasing the property value. The research addresses whether buyers place preference towards landscape infrastructure as a factor that would offer benefit towards increasing their property value. Proof is needed that the contribution of landscape design helps in increasing property value. A further research needs to be done in order to meet the demand and supply in landscape industry

Komunikasi keluarga berdasarkan uslub Nabi SAW

Kehebatan uslub atau pendekatan baginda nabi SAW berkomunikasi dengan para sahabat amat wajar dicontohi remaja dan ibu bapa masa kini. Pelbagai elemen unggul yang baginda terapkan dalam komunikasi menjadikan setiap tutur kata baginda berkesan dan memiliki mesej-mesej tertentu yang perlu diteladani. Realiti hari ini para remaja khususnya gagal memanfaatkan contoh agung tersebut telah mengundang pelbagai masalah keruntuhan akhlak. Penggunaan kata-kata kesat, tengking-menengking melatari komunikasi remaja dan persekitarannya diparahkan lagi dengan keterlibatan mereka dalam jenayah moral merupakan bukti kukuh kegagalan ini. Statistik kegiatan jenayah yang berlaku melibatkan pelajar sekolah menengah meningkat daripada 360 orang pelajar pada tahun 2012 kepada 540 orang pada tahun berikutnya. Angka ini menunjukkan bahawa penglibatan remaja dalam gejala sosial amat membimbangkan pelbagai pihak. Justeru, kajian ini bertujuan mengkaji dan mengenal pasti nilai-nilai uslub baginda berkomunikasi dengan para sahabat dan menganalisis pendekatan hadith tersebut dapat menangani masalah komunikasi di samping membina kualiti remaja berkomunikasi. Kajian ini menggunakan pendekatan kualitatif berbentuk analisis kandungan secara tematik, deduktif dan deskriptif. Kajian menemui beberapa tema uslub komunikasi di dalam hadith tersebut berdasarkan pelbagai pendekatan baginda berkomunikasi dengan para sahabat dengan gaya bahasa yang mendidik. Hasil kajian mendapati bahawa intipati uslub baginda dapat menghidupkan suasana keharmonian komunikasi remaja, maka ibu bapa khususnya perlu memulakan peranan tersebut ketika remaja di awal usia. Implikasi kajian ini mendapati bahawa semua pihak bertanggungjawab terutama pihak keluarga malah menyedarkan masyarakat tentang Islam merupakan agama komunikasi. Oleh itu, menampilkan komunikasi remaja mencontohi uslub baginda dapat melahirkan remaja yang berakhlak mulia, berkualiti, beretika dan berintegriti dalam pertuturan sekali gus mengurangkan risiko mereka terus terjebak dengan permasalahan komunikasi

SIRAH DALAM MENEGAK PERUBAHAN SOSIAL MENGIKUT PANDANGAN SYEIKH SAID RAMADAN AL-BUTI

Sirah Rasulullah SAW merupakan salah satu karya penulisan ulama perlu diketengahkan kerana telah memainkan peranan yang besar dalam mengembangkan dakwah dan syariat Islam diseluruh dunia sejak dahulu lagi. Mempelajari sirah Nabi ini bukanlah semata-mata untuk mengetahui peristiwa menarik dan aneh yang berlaku di zaman Nabi SAW. Pengkajian sirah ini juga bukan sekadar ingin mengetahui peristiwa-peristiwa yang telah melakar sejarah sebagaimana kajian-kajian sejarah yang lain sebagai contoh sejarah hidup seorang khalifah atau sejarah tamadun yang silam. Sirah juga bukanlah sekadar satu kisah yang dibaca pada hari keputeraan baginda SAW sahaja. Apa yang lebih besar sebenarnya adalah ikatan seseorang Muslim dengan Rasulnya sehinggakan pada akhirnya Muslim itu berjaya menjadikan sirah sebagai sesuatu yang dapat menambahkan iman, memperelok akhlak, menyemarakkan perjuangan Islam serta dapat mendorong Muslim itu untuk terus berpegang dengan kebenaran dan seterusnya istiqamah kepadanya. Kajian ini akan mengetengahkan peranan sirah dalam mengukuhkan aspek sosial masyarakat dan perkara ini akan dilihat berdasarkan kepada pandangan Syeikh Ramadan al-Buti. Kaedah analisis kandungan akan digunakan dalam mengkaji kitab Fiqh as-Sirah an-Nabawiyyahbagi mendapatkan data. Data tersebut akan dianalisis secara deskriptif dan eksplanatori. Dapatan kajian menunjukkan bahawa keperluan pendekatan sirah dalam aspek sosial adalah tinggi bagi membentuk aspek sosial yang baik itu sendiri. Walau bagaimanapun, Syeikh Ramadan al-Buti telah membuka ruang sepenuhnya supaya aspek sirah diaplikasikan kedalam perubahan sosial masyarakat islam dari masa ke semasa. Implikasi kajian menunjukkan bahawa sirah Rasulullah telah berjaya membentuk satu Kerajaan Islam yang baik berbanding sejarah tamadun Barat yang musnah ekoran tiada sisa-sisa kemanusiaan yang dihidupkan

Implicit thinking knowledge injection framework for Agile requirements engineering

Agile has become commonly used as a software development methodology and its success depends on face-to-face communication of software developers and the faster software product delivery. Implicit thinking knowledge has considered as a very significant for organization self-learning. The main goal of paying attention to managing the implicit thinking knowledge is to retrieve valuable information of how the software is developed. However, requirements documentation is a challenging task for Agile software engineers. The current Agile requirements documentation does not incorporate the implicit thinking knowledge with the values it intends to achieve in the software project. This research addresses this issue and introduce a framework assists to inject the implicit thinking knowledge in Agile requirements engineering. An experiment used a survey questionnaire and case study of real project implemented for the framework evaluation. The results show that the framework enables software engineers to share and document their implicit thinking knowledge during Agile requirements documentation

SIEM Network Behaviour Monitoring Framework using Deep Learning Approach for Campus Network Infrastructure

One major problem faced by network users is an attack on the security of the network especially if the network is vulnerable due to poor security policies. Network security is largely an exercise to protect not only the network itself but most importantly, the data. This exercise involves hardware and software technology. Secure and effective access management falls under the purview of network security. It focuses on threats both internally and externally, intending to protect and stop the threats from entering or spreading into the network. A specialized collection of physical devices, such as routers, firewalls, and anti-malware tools, is required to address and ensure a secure network. Almost all agencies and businesses employ highly qualified information security analysts to execute security policies and validate the policies’ effectiveness on regular basis. This research paper presents a significant and flexible way of providing centralized log analysis between network devices. Moreover, this paper proposes a novel method for compiling and displaying all potential threats and alert information in a single dashboard using a deep learning approach for campus network infrastructure

Penentuan kesepadanan bidang pengajian-pekerjaan dalam kalangan graduan di kolej komuniti

Mismatch issues between training in skills training institutions and skills required by employers often raised. However, we found the lack of a study conducted to examine the compatibility of training between training institutions and industry. Thus, this study was undertaken to determine the level of matching between the fields of study with work and identify the underlying factors that affect compatibility between fields of study with the underlying employment among graduates. Our research involves Community College trainees in the field of electrical which graduation in 2013. This study is a quantitative study using a questionnaire as an instrument. Descriptive statistics such as frequencies and percentages and inferential statistical analysis such as Cramer -V Correlation test was used to analyze the data. The results show that almost 43.4 % of graduates work in fields that do not match the field of study at community colleges. In addition, the study also showed that there was no significant effect of educational characteristics (such as academic performance) and job characteristics (such as length of employment, type of employment sectors and employment status) on the correspondence between fields of study with jobs they undertake. Indeed, the quality of graduate students can not be measured only by the ability to place graduates in jobs in the sector , but what is more important is their ability to get a job in matching the field of study and skills acquired

An enhanced feature representation based on linear regression model for stock market prediction

Stock price prediction has been an attractive research domain for both investors and computer scientists for more than a decade. Reaction prediction to the stock market, especially based on released financial news articles and published stock prices, still poses a great challenge to researchers because the prediction accuracy is relatively low. For prediction purposes, linear regression is a popular method. Statistical metrics, such as the Document Frequency (DF), term frequency-invert document frequency (TF-IDF) and information gain (IG), are used for feature selection to extract the most expressive features to reduce the high dimensionality of the data. However, the effectivenesses of the available metrics have not been explored in identifying important financial feature representations that have dependable and strong relations with the stock price. The objective of this study are (i) to investigate the performance of five statistical metrics, namely, DF, TF-IDF, IG, Chi-square Statistics (Chi-Sqr) and occurrence in identifying important features that can represent the news and have a strong relationship with the stock price; (ii) to introduce feedback variables, namely, the prediction accuracy (PA), directional accuracy (DA) and closeness accuracy (CA), to capture the interaction between the released news and the published stock prices; and (iii) to introduce a prediction model that integrates features from financial news and a stock price value series based on a 20-minute time lag using linear regression. The experiment used the ELR-BoW method to build a number of 330 datasets with five statistical metrics to select different feature sizes of 50, 100, 150, 200, 250, 300, 400, 500, 600, 700 and 800. The performance of ELR-BoW is observed based on three parameters, namely, PA, DA and CA, and is compared against Naïve Bayes (NB) as the benchmark approach and the Support Vector Machine (SVM). The proposed ELR-BoW-SVM obtained a higher accuracy compared to ELR-BoW-NB, where the best feedback measure is PA, which has an F-measure value of 0.842. In addition, the best number of features is 300 features and using document frequency DF statistical metric. The identification of the top feature representations for financial news is highly promising for automatic news processing for stock prediction. This study demonstrates that the identification of the top feature representations for financial news is highly promising for news article processing in stock prediction