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افضل ذکر( کلمہ شریف)

افضل ذکر (کلمہ شریف)

اس کلمے دے راز نرالے نیں
اس ہر دے دُکھڑے ٹالے نیں
سانوں دسیا طیبہ والے نیں
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
ایہہ کلمہ نور الٰہی دا !
نالے سوہنے مدنی ماہی دا
سانوں ہر دم پڑھنا چاہی دا
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
جیہڑے کلمے دا وِرد پکاندے نیں
وچ دنیا خوش خوش راہندے نیں
نالے جنتیں ڈیرا لاندے نیں
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
کھچ کلمے دی ضرب اُلاویں توں
بن پیتیاں مست ہو جاویں توں
نالے درشن یار دا پاویں توں
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ

کر کلمے نال پیار میاں
وچ مشکلاں ایہہ غمخوار میاں
دیوے بیڑا پار اُتار میاں
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
پڑھ کلمہ سیاں بولدیاں
نالے اکھیاں تھیں اَتھرو ڈولدیاں
سب صفتاں عربی ڈھول دیاں
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
پڑھ کلمہ شکر منائی جاء
ایہہ گیت توحید دا گائی جاء
سوہنے یار نوں اینج منائی جاء
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ
پڑھ کلمہ حافظ زور دے نال
ہن چھڈ دے سارے برے خیال
تیرا ساتھی کلمہ رہے اقبال
پڑھ لَااِلٰہَ اِلَّاللہ مُحَمَّدُ الرَّسُوْلُ اللہ

Factors Affecting the Academic Achievements among Dean’s Listers of Caraga State University

The study assessed the relationship between the factors affecting the academic achievement of the dean’s listers’ of Caraga State University. It involves the total population of the dean’s listers in the said university. The independent variables are those pre-determined factors’ affecting the academic achievement of the dean’s listers’ of Caraga State University and the dependent variable is the grades of the dean’s listers’. The result shows the low relationship between the pre-determined factors and the academic achievement evidenced by the values of the p-values which are greater than. In terms of the academic achievement of the dean’s listers’ their grades signifies their excellence in their different chosen fields. With regards to the pre-determined factors, the factor that got the highest mean is the teachers’ competence with 3.7639 and the lowest one is the learning environment with 3.6690. The study habits’ got the second spot among the 4 factors followed by the learning styles. Based on Spearmen Correlation analysis in the data gathered, the results revealed that there is no significant relationship between the pre-determined factors and the academic achievement of the dean’s listers’ of Caraga State University. The p-values obtained are less than 0.05 for all the data set; that is accepting the null hypothesis. The results clearly depicts that the students’ study habit, learning style teachers’ competence and the learning environment has no influence to the achievement reached by the dean’s listers’. On the other hand, it is still very important to make and to maintain these factors visible in the academic arena for a better learning and for a better outcome. The absence of these factors might affect the performances of the students’ in Caraga State University.

Novel Particle Swarm Optimization Algorithm for Multimodal Optimization Problems by Enhancing the Robustness and Diversity

Particle Swarm Optimization (PSO) is a Swarm Intelligence (SI) based algorithm developed by Kennedy and Eberhart in 1995. PSO was initially designed for locating single peak and became popular for solving global optimization problems as well. In spite of its simplicity, PSO has several limitations, which prevent it from achieving efficient solution. However, the two main limitations are its slow convergence rate and the local trapping dilemma. In order to tackle this situation, researchers have tried to avoid the premature convergence by performing some extra computations and have improved the convergence speed by introducing new parameters in PSO. Furthermore, in many cases, instead of the single best solution, we need to know about all possible solutions as well. In this regard, different multimodal techniques have been proposed to handle multimodal optimization problem, including crowding, deterministic crowding, fitness sharing, derating, restrict tournament selection, clearing, clustering, and speciation. However, among these solutions some techniques find only all global optima, whereas in many cases all possible optima are required. But, locating all global optimum solution for the PSO and other evolutionary algorithms has its own issues. Furthermore, these issues become more challenging when we are intended to locate all possible solutions of multimodal optimization problems. In literature, various evolutionary multimodal optimization techniques have been proposed. The objectives of these algorithms are to tackle some general issues like how to locate multiple global as well as local optimal solutions?; How to retain the located optima until the end of the search?; How to locate multiple optima parallel with less number of function evaluations?; and how to avoid premature convergence by maintaining or increasing population diversity?. Among the number of existing multimodal optimization algorithms, species-based PSO (SPSO) algorithms are very common to locate multiple optima parallel. Due to its intrinsic nature of multiple species, it implicitly resolves many issues that have been occurring in single population as well as sequential evolutionary multimodal optimization algorithms. The species-based PSO is one of the SI-based multimodal optimization algorithms that can locate multiple peaks in parallel. Species-based PSO algorithms still have two main issues, which are the random initialization issue and the exploitation capability. In presence of random initialization, some promising area may remain unexplored and species are not formed around that area which ultimately misses some optima in the solution space. To the best of our knowledge, random initialization issue in species-based PSO has not been well addressed. Another issue with speciation, best of our knowledge that has not been addressed is its exploitation capability. As the species are formed in each iteration step around the seed particle and each particle learn locally. Therefore, the particles cooperate and interact with a few particles in a specific area and cannot move across the species boundaries. This dissertation is an effort to solve the above mentioned problems. In order to enhance the performance of PSO, for locating the global optima in complex multimodal problems, we have proposed an accelerated convergent PSO (ACPSO) by introducing a new velocity update equation. Further, we proposed a robust species-based PSO, called exploration strategy inspired species-based PSO VIII (ESPSO), to enhance the exploitation capability of SPSO by introducing an explorer swarm that resolve the random initialization as well as exploitation issues of SPSO. The extensive experimentation has proved the effectiveness of both solutions as compared to the existing state-of-the-art when compared with the standard benchmark test problems.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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